University of Groningen
Today's talented youth field hockey players, the stars of tomorrow?
Gemser, Marije
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TODAY’S TALENTED YOUTH FIELD HOCKEY PLAYERS, THE STARS OF TOMORROW?
A STUDY ON TALENT DEVELOPMENT IN FIELD HOCKEY
Groningen Studies in Sports Sciences 1
The research described in this thesis was financially supported by NOC*NSF
Paranimfen:
Jildou Gemser
Pieter-Jorn Gemser
Cover:
Corina Blom
Printed by:
Grafisch bedrijf Ponsen & Looijen bv, Wageningen
ISBN 90-6464-2923
© Copyright 2005: M.T. Elferink-Gemser, Groningen, the Netherlands.
All rights reserved. No part of this publication may be reproduced or transmitted in any form
or by any means, electronic or mechanical, including photocopy, recording or any information
storage or retrieval system, without the prior written permission of the copyright owner.
RIJKSUNIVERSITEIT GRONINGEN
Today’s talented youth field hockey players, the stars of tomorrow?
A study on talent development in field hockey
Proefschrift
ter verkrijging van het doctoraat in de
Psychologische, Pedagogische en Sociologische Wetenschappen
aan de Rijksuniversiteit Groningen
op gezag van de
Rector Magnificus, dr. F. Zwarts,
in het openbaar te verdedigen op
donderdag 14 april 2005
om 14.45 uur
door
Marije Titia Elferink-Gemser
geboren op 7 augustus 1973
te Sneek
Promotor:
Prof.dr. Th. Mulder
Copromotores:
Dr. C. Visscher
Dr. K.A.P.M. Lemmink
Beoordelingscommissie:
Prof.dr. H. Kuipers
Prof.dr. R. Bosker
Prof.dr. H. Kemper
Voor oma
Contents
Chapter 1
General Introduction
1
Chapter 2
Evaluation of the reliability of two field hockey specific sprint and
dribble tests in young field hockey players.
Lemmink, K.A.P.M., Elferink-Gemser, M.T., and Visscher, C.
(2004). British Journal of Sports Medicine, 38, 138-142.
11
Chapter 3
Relation between multidimensional performance characteristics
and level of performance in talented youth field hockey players.
Elferink-Gemser, M.T., Visscher, C., Lemmink, K.A.P.M., and
Mulder, Th. (2004). Journal of Sports Sciences, 22, 1053-1063.
27
Chapter 4
Multidimensional performance characteristics and performance
level in talented youth field hockey players: A longitudinal study.
Elferink-Gemser, M.T., Visscher, C., Lemmink, K.A.P.M., and
Mulder, Th. Journal of Sports Sciences (pending minor revisions).
49
Chapter 5
Development of the interval endurance capacity in elite and subelite youth field hockey players. Elferink-Gemser, M.T., Visscher,
C., Van Duijn, M.A.J., and Lemmink, K.A.P.M.
69
Chapter 6
Psychological characteristics of talented youth athletes in field
hockey, basketball, volleyball, speed skating, and swimming.
Elferink-Gemser, M.T., Visscher, C., and Lemmink, K.A.P.M.
The Sport Psychologist (in revision).
87
Chapter 7
Development of the Tactical Skills Inventory for Sports.
Elferink-Gemser, M.T., Visscher, C., Richart, H., and Lemmink,
K.A.P.M. (2004). Perceptual and Motor Skills, 99, 883-895.
103
Chapter 8
Discussion and Conclusions
Summary
Samenvatting
List of publications
Dankwoord
121
133
137
143
147
Chapter I
General Introduction
2
Chapter
1.1
I
II
III
IV
V
VI
VII
VIII
Expert performance in sports
The ambition of the Dutch Olympic Committee (NOC*NSF) is a permanent top-10 position
in the world of sports. To realize this, structural attention is paid to the identification of
talented athletes and their development towards expertise. The present study on talented youth
field hockey players has been conducted at the Center for Human Movement Sciences of the
University of Groningen and is one of many projects to fulfil the above mentioned objective.
Expert performance in sports can be defined as the consistent superior athletic
performance over an extended period (Starkes, 1993a). In the present study, expert
performance in field hockey is operationalised by playing in the highest league of the Dutch
field hockey competition. Dutch field hockey has been ranked among the best in the world for
decades and its competition is recognized world-wide for its high performance level.
Although reaching excellence in field hockey is not linearly related to the number of invested
hours of practice, current international-level performers have spent many hours of training for
at least ten years before reaching the top (Ericsson et al., 1993; Ericsson, 1996; Starkes et al.,
1996; Starkes, 2000; Van Rossum, 2000). All of them invested enormous amounts of time
preparing for the international sporting arena. In the Netherlands, most experts started playing
field hockey when they were seven years old. Obviously, youth players who want to make it
to the top have to start training already at an early age.
In a review on talent research, Williams and Reilly (2000a) make clear that from a
scientific perspective the pursuit of excellence can be broken down into four key stages:
‘talent detection’, ‘talent identification’, ‘talent development’, and ‘talent selection’ (Russell,
1989; Borms, 1996). Talent detection refers to the discovery of potential performers who are
currently not involved in the sport in question (Williams and Reilly, 2000a). Talent
identification refers to the process of recognizing youth players with the potential to become
elite players whereas talent development implies that these players are provided with a
suitable learning environment and resources so that they have the opportunity to realize their
potential (Régnier et al., 1993). Finally, talent selection involves the ongoing process of
identifying players at various stages who demonstrate prerequisite levels of performance for
inclusion in a selection team (Williams and Reilly, 2000a). The present thesis focuses on
talented youth field hockey players: players who perform better than their peers during
training and competition, and who have the potential to become elite performers in the future
(Howe et al., 1998; Helsen et al., 2000). This means that the current performance level of
youth players is considered important as well as their potential for the future. They are part of
a talent development program of a field hockey club of national prestige, and are playing at
the highest level for their age category.
3
1.2
Profile of field hockey
Field hockey is a field invasive sport in which players compete at the same field of action as
their opponents (Hughes and Barlett, 2002). To obtain expert status in field hockey, players
must excel in no less than four domains: physiological, technical, tactical, and psychological.
In addition, the development of their anthropometric characteristics is important. Match
analyses at the elite level make clear that field hockey is a high intensity non-continuous game
in which the physiological demands are considerable, placing it in the category of ‘heavy
exercise’ (e.g., Ghosh et al., 1991; Reilly and Borrie, 1992). Physiological components of
expertise include aerobic and anaerobic capacity (Wilmore and Costill, 1999). Specific for
field hockey is the intermittent running, e.g. the alternation of accelerating and decelerating,
and the many changes of direction while sprinting (Patel et al., 2002; Spencer et al., 2004).
The unique requirements of field hockey including dribbling the ball and moving quickly in a
semi-crouched posture superimpose the work-load demanded by the game (Reilly and Seaton,
1990). Technical expertise refers to the degree of sensorimotor coordination from which
refined, efficient, and effective movement patterns emerge (Janelle and Hillman, 2003). For a
technically sound player, dribbling is essentially an automatic process, and the better players
distinguish themselves by their running speed while dribbling the ball (Reilly and Bretherton,
1986).
Field hockey is a highly structured analytical game in which players constantly have to
deal with a complex and rapidly changing environment (Starkes, 1993b). In order to be
successful, they have to perform the right action at the right moment. Therefore, they have to
acquire great tactical skills. Tactical expertise is a requisite for expert performance in virtually
all achievement domains (Janelle and Hillman, 2003). Sport is unique, however, in that
tactical skills involve not only the knowledge to determine what strategy is most appropriate
in a given situation, but also whether the strategy can be successfully executed within the
constraints of the required movements (e.g., Starkes, 1993a; McPherson, 1994). Thus, the
execution of tactical skills in field hockey is always related to the physiological and technical
limitations of the individual player, his or her teammates and his or her opponents. To perform
at top level, players have to perform under high pressure. It is therefore not surprising that
psychological characteristics such as motivation, confidence, anxiety control, mental
preparation, team emphasis, and concentration often distinguish elite from non-elite
performers (Mahoney et al., 1987; Morris, 2000). Excellent psychological skills can not only
play a decisive role in an important match; they are also needed to develop a successful sports
career. Commitment from the performers is required since engagement in training is not
inherently motivating (Ericsson et al., 1993; Ericsson, 1996).
4
Chapter
1.3
I
II
III
IV
V
VI
VII
VIII
Research in sport expertise
For several years, researchers have tried to identify key predictors of talent in many sports. A
decade ago, Régnier and collegues (1993) published a review on talent detection and
development in sports with the purpose of providing a better understanding of the process by
which one achieves greatness in sports. Until that time, far most studies were cross-sectional
in nature measuring general characteristics. Literature on talent identification and
development has largely emerged during the 1990s. Books that contribute substantially to our
basic understanding of expertise are ‘The road to excellence: The acquisition of expert
performance in the arts, sciences, sports, and games’ by Ericsson (1996) and ‘Expert
performance in sports’ edited by Starkes and Ericsson (2003). Some years ago, the Journal of
Sports Sciences devoted a special issue to talent identification and development in soccer
(Williams and Reilly, 2000b).
Research with athletes at the highest level of performance
Sport is characterized by a hierarchical organization in which the level of performance of a
player is described by the appropriate level of competition (e.g., local, regional, national, and
international). The number of players that are allowed to compete at a given level of
competition becomes smaller as the level of performance increases. When players of different
competition levels are compared on the basis of their performance characteristics it is to be
expected that the higher level players outscore the lower level players. However, this does not
necessarily apply when players within the same competition level; i.e., within a talent-group
are compared with each other. The relation between multidimensional performance
characteristics and level of performance might be different. Therefore, to unravel the
mechanisms leading to excellence, research should be conducted within a group of talented
players, all playing at the highest performance level for their age. This is possible by
comparing elite youth players with sub-elite youth players. Both elite and sub-elite players are
part of a talent development program of a field hockey club of national prestige, and are
playing at the highest level for their age category. However, in contrast to sub-elite players,
elite players additionally play in a youth selection team of the Dutch Field Hockey
Association (KNHB).
Measuring multidimensional performance characteristics in a sports-specific way
A group of all talented players is relatively homogeneous with regard to their performance
level. As a consequence, measures of general performance characteristics are usually not
sensitive enough to detect differences between elite and sub-elite players (Bangsbo and
Lindquist, 1992; Lemmink et al., in press 2004). Tests therefore have to measure components
5
that represent the specific demands of the sport in question involving sports-specific variables
(Atkinson and Nevill, 2001). Sports scientists often acknowledge that a world-class
performance is the result of several factors, advocating a multidimensional approach in studies
on talented players (e.g., Régnier et al., 1993; Reilly et al., 2000). Burwitz et al. (1994) also
recommend interdisciplinary performance-related sports science research. Therefore, to allow
for the characteristics of field hockey, anthropometric, physiological, technical, tactical, and
psychological characteristics should be measured in a sports-specific way.
Longitudinal research design
To improve understanding of the factors that contribute to expert performance, players should
be monitored over a prolonged period of time (Williams and Reilly, 2000a). By adopting a
longitudinal design it is possible to monitor the development of the performance level of
talented youth field hockey players. Although the majority of researchers recommend
conducting research within a large group of young talented players, measuring
multidimensional performance characteristics in a sports-specific way, following the players
from childhood to elite senior standard (e.g., Hoare and Warr, 2000; Morris, 2000; Reilly et
al., 2000), thus far no study in talented field hockey players combined all these aspects.
1.4
Objective and outline
The aim of this thesis is to gain a deeper insight into the relation between (the development
of) multidimensional performance characteristics and the level of performance in talented
youth field hockey players.
In chapter 2, attention is paid to the measurement of the multidimensional performance
characteristics important for high-performance in youth field hockey players. The Shuttle
Sprint and Dribble Test and the Slalom Sprint and Dribble Test have been developed for the
purpose of this study and a paper on the development of these two field hockey specific tests
is included.
In chapter 3, a study conducted within a group of all talented youth field hockey players is
presented. To determine the relation between multidimensional performance characteristics
and performance level, elite youth players were compared with sub-elite youth players on
anthropometric, physiological, technical, tactical, and psychological characteristics.
In chapter 4, longitudinal data are presented on the talented youth field hockey players
that have been followed across time. A comparison was made between elite and sub-elite
youth players in terms of anthropometric, physiological, technical, tactical, and psychological
characteristics measured on three occasions, each separated by a time interval of one year.
6
Chapter
I
II
III
IV
V
VI
VII
VIII
In chapter 5, a model of the development of an important physiological performance
characteristic, the interval endurance capacity, is presented. Scores on the Interval Shuttle Run
Test for interval endurance capacity were modeled for female and male, elite and sub-elite
players in the age-band from 12 to 19 years.
The studies on the relation between multidimensional performance characteristics and
performance level in talented youth field hockey players, described in the former chapters,
show that psychological characteristics distinguish elite from sub-elite youth field hockey
players. To investigate whether this finding is specific for field hockey or can be generalized
to other sports, a study to reveal the relationship between psychological skills and level of
performance within talented youth athletes in field hockey, basketball, volleyball, speed
skating, and swimming is presented in chapter 6.
In chapter 7, the measurement of tactical skills is discussed. In the studies described in
chapters 3 and 4, tactical skills were measured by the opinion of the trainers. Although these
trainers are experts in the field and their opinion is highly valued, one might argue that their
judgment of a player’s tactical skills is influenced by their knowledge of that player’s
performance level. Therefore, we conducted a study with the purpose of developing a
practical, reliable, and valid self-report instrument to measure tactical skills in sports: the
Tactical Skills Inventory for Sports.
In chapter 8, the results of the different studies are combined into a general discussion and
conclusions are drawn.
7
References
Atkinson, G. and Nevill, A.M. (2001). Selected issues in the design and analysis of sport performance
research. Journal of Sports Sciences, 19, 811-827.
Bangsbo, J. and Lindquist, F. (1992). Comparison of various exercise tests with endurance
performance during soccer in professional players. International Journal of Sports Medicine, 13,
125-132.
Borms, J. (1996). Early identification of athletic talent. Keynote Adresssed to the International PreOlympic Scientific Congress, Dallas, TX, USA.
Burwitz, L., Moore, P.M., and Wilkinson, D.M. (1994). Future directions for performance-related
sports science research: An interdisciplinary approach. Journal of Sports Sciences, 12, 93-109.
Ericsson, K.A. (1996). The acquisition of expert performance: An introduction to some of the issues.
In The road to excellence: The acquisition of expert performance in the arts and sciences, sports
and games (edited by K.A. Ericsson), pp. 1-50. Mahwah, NJ: Lawrence Erlbaum Associates.
Ericsson, K.A., Krampe, R.T., and Tesch-Römer, C. (1993). The role of deliberate practice in the
acquisition of expert performance. Psychological Review, 100, 363-406.
Ghosh, A.K., Goswami, A., Mazumdar, P., and Mathur, D.N. (1991). Heart rate & blood lactate
response in field hockey players. Indian Journal of Medical Research, 94, 351-356.
Helsen, W.F., Hodges, N.J., Van Winckel, J., and Starkes, J.L. (2000). The roles of talent, physical
precocity and practice in the development of soccer expertise. Journal of Sports Sciences, 18, 727736.
Hoare, D.G. and Warr, C.R. (2000). Talent identification and women’s soccer: An Australian
experience. Journal of Sports Sciences, 18, 751-758.
Howe, M.J.A., Davidson, J.W., and Sloboda, J.A. (1998). Innate talents: Reality or myth. Behavioral
and Brain Sciences, 21, 399-442.
Hughes, M.D. and Barlett, M. (2002). The use of performance indicators in performance analysis.
Journal of Sports Sciences, 20, 739-754.
Janelle, C.M. and Hillman, C.H. (2003). Expert performance in sport. Current perspectives and critical
issues. In Expert performance in sports: Advances in research on sport expertise (edited by J.L.
Starkes and K.A. Ericsson), pp. 19-47. Champaign, IL: Human Kinetics.
Lemmink, K.A.P.M., Verheijen, R., and Visscher, C. (2004). The discriminative power of the Interval
Shuttle Run Test and the Maximal Multistage Shuttle Run Test for playing level of soccer.
Journal of Sports Medicine and Physical Fitness, 44, 233-239.
Mahoney, M.J., Gabriel, T.J., and Perkins, T.S. (1987). Psychological skills and exceptional
performance. The Sport Psychologist, 1, 181-199.
McPherson, S.L. (1994). The development of sport expertise: Mapping the tactical domain. Quest, 46,
223-240.
Morris, T. (2000). Psychological characteristics and talent identification in soccer. Journal of Sports
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Patel, D.R., Stier, B., and Luckstead, E.F. (2002). Major international sport profiles. Pediatric Clinics
of North America, 49, 769-792.
Régnier, G., Salmela, J.H., and Russell, S.J. (1993). Talent detection and development in sport. In A
Handbook of Research on Sports Psychology (edited by R. Singer, M. Murphey, and L.K.
Tennant), pp. 290-313. New York: Macmillan.
Reilly, T. and Borrie, A. (1992). Physiology applied to field hockey. Sports Medicine, 14, 10-26.
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Reilly, T. and Seaton, A. (1990). Physiological strain unique to field hockey. The Journal of Sports
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identification in soccer. Journal of Sports Sciences, 18, 695-702.
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VIII
Russell, K. (1989). Athletic talent: From detection to perfection. Science Periodical on Research and
Technology in Sport, 9, 1-6.
Spencer, M., Lawrence, S., Rechichi, C., Bishop, D., Dawson, B., and Goodman, C. (2004). Timemotion analysis of elite field hockey, with special reference to repeated-sprint activity. Journal of
Sports Sciences, 22, 843-850.
Starkes, J.L. (1993a). Motor experts: Opening thoughts. In Cognitive issues in motor expertise (edited
by J.L. Starkes and F. Allard), pp. 3-16. Amsterdam: Elsevier.
Starkes, J.L. (1993b). Skill in field hockey: The nature of the cognitive advantage. Journal of Sport
Psychology, 9, 146-160.
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Sport Psychology, 31, 431-451.
Starkes, J.L. and Ericsson, K.A. (2003). Expert performance in sports: Advances in research on sport
expertise. Champaign, IL: Human Kinetics.
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sports: What is it anyway? In The road to excellence: The acquisition of expert performance in the
arts and sciences, sports, and games (edited by K.A. Ericsson), pp. 81-106. Mahwah, NJ:
Lawrence Erlbaum.
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Wilmore, J.H. and Costill, D.L. (1999). Physiology of sport and exercise (2nd ed.). Champaign, IL:
Human Kinetics.
9
Chapter II
Evaluation of the reliability of two
field hockey specific sprint and
dribble tests in young field hockey
players
Lemmink, K.A.P.M., Elferink-Gemser, M.T., and
Visscher, C.(2004).
British Journal of Sports Medicine, 38, 138-142.
Acknowledgements:
The authors wish to express their sincere appreciation
to Mieke Richart and Inge Scheek for their assistance
in this research project
Abstract
The goal was to determine the reliability of two field hockey-specific tests: the Shuttle
Sprint and Dribble Test (ShuttleSDT) and the Slalom Sprint and Dribble Test
(SlalomSDT). The shuttle sprint and dribble performances of 22 young male and 12 young
female field hockey players were assessed on two occasions within 4 weeks. Twenty one
young female field hockey players took part in the SlalomSDT twice in a 4 week period.
The ShuttleSDT required the players to perform three 30-m shuttle sprints while carrying a
hockey stick alternated with short periods of rest and, after a 5-minute rest, three 30-m
shuttle sprints alternated with rest while dribbling a hockey ball. The SlalomSDT required
the players to run a slalom course and, after a 5-minute rest, to dribble the same slalom
with a hockey ball. There were no differences in mean time scores between the two test
sessions. The mean differences were small when compared with the means of both test
sessions. With the exception of the slalom sprint time, zero lay within the 95% confidence
interval of the mean differences indicating that no bias existed between the two
measurements. With the exception of delta shuttle time (0.79), all intraclass correlation
coefficient values for the ShuttleSDT, met the criterion for reliability of 0.80. Intraclass
correlation coefficient values for SlalomSDT were 0.91 for slalom sprint time, 0.78 for
slalom dribble time, and 0.80 for delta slalom time. This study shows that the ShuttleSDT
and the SlalomSDT are reliable measures of sprint and dribble performances of young field
hockey players.
12
Chapter
I
II
III
IV
V
VI
VII
VIII
2.1 Introduction
Recent developments in field hockey, such as the artificial playing surface, new stick material,
and the interchange rule, have increased the number of physiological and technical demands
made on field hockey players at all levels, but in particular at the elite level. Competitive field
hockey matches place heavy aerobic demands on players and require them to expend energy
at relatively high levels (Reilly and Borrie, 1992; Boyle et al., 1994). High-intensity activities
such as cruising, sprinting, and activities in which the player is directly involved with the ball
(for example, dribbling) have been shown to represent between 17.5 - 30% of the competition
time (Lothian and Farrally, 1994), and are considered critical to the outcome of the game.
Furthermore, in field hockey, high and low intensity activities alternate by a ratio ranging
from about 1:4 to 1:8 (Lothian and Farrally, 1994). Consequently, as well as maximal
performance on individual high intensity activities, the ability to produce high intensity efforts
is crucial for top level field hockey players.
Field hockey is a multiple high intensity activity sport with a multidirectional nature. The
ability to change direction rapidly while maintaining balance without loss of speed – that is,
agility - is therefore an important physical component necessary for successful performance in
field hockey. Elite field hockey players also need high level technical skills such as being able
to dribble without losing running speed. For a technically good player, dribbling is essentially
an automatic process, and the better players distinguish themselves by their running speed
while dribbling the ball (Reilly and Bretherton, 1986).
Coaches, trainers, and players are continually searching
for effective methods of
identifying and developing those characteristics in a player that may enhance performance.
There are a variety of field tests with which to measure the physiological and technical
characteristics of players in team games like soccer, rugby, and handball. However, there was
no single test to measure both physiological and technical characteristics in field hockey
players and for this reason we developed two tests specifically to measure these
characteristics. Based on tests for repeated sprint ability (Baker et al., 1993; Fitzsimons et al.,
1993; Bangsbo, 1994; Aziz et al., 2000; Lemmink et al., 2000; Wragg et al., 2000; Bishop et
al., 2001; Boddington et al., 2001) and dribbling skills of field hockey players (Reilly and
Bretherton, 1986) and soccer players (Reilly and Holmes, 1983; Van Rossum and Wijbenga,
1993) we developed the field hockey specific Shuttle Sprint and Dribble Test (ShuttleSDT) to
measure shuttle sprint and dribble performance. Based on tests for agility (Pauole et al., 2000)
and dribbling skills of field hockey players (Reilly and Bretherton, 1986) and soccer players
(Reilly and Holmes, 1983; Van Rossum and Wijbenga, 1993), the field hockey specific
Slalom Sprint and Dribble Test (SlalomSDT) was developed to measure slalom sprint and
dribble performance.
13
It is vital that the ShuttleSDT and the SlalomSDT provide reliable information. A reliable
test must perform consistently. In other words, if an individual whose ability or skill has not
changed is tested twice with a completely reliable measuring device, both scores will be
identical (Baumgartner and Jackson, 1999). The aim of this study was therefore to determine
the reliability of the ShuttleSDT and the SlalomSDT in young elite field hockey players.
2.2
Methods
Participants
A total of 34 young male (n = 22) and female (n = 12) field hockey players participated in the
reliability study of the field hockey specific Shuttle Sprint and Dribble Test (ShuttleSDT).
The mean age of the boys was 15.5 years (sd = 1.8), and of the girls 13.8 years (sd = 1.0).
Twenty one young female field hockey players whose mean age was 13.5 years (sd = 1.3)
volunteered to take part in the reliability study of the field hockey-specific Slalom Sprint and
Dribble Test (SlalomSDT). After being informed about the study procedure, the subjects gave
their verbal consent to participation. The group averaged two training sessions and one match
per week.
Procedure
To examine the reliability of the ShuttleSDT and the SlalomSDT, two trials were conducted
within a period of 2 to 4 weeks and during the subjects’ normal training hours, varying
between 16:30 and 20:00. On day one and two the average temperature during testing was 4.3
and 3.7 ˚C respectively, humidity was 86-98 % on both days and wind conditions (no high
winds) were comparable. The tests were conducted on a synthetic pitch surface laid on a
sandy field with subjects wearing their normal playing footwear. The subjects were only given
feedback on their performance after completing all the tests.
Shuttle Sprint and Dribble Test (ShuttleSDT)
The ShuttleSDT was developed to measure field hockey specific shuttle sprint and dribble
performance. This study used a modified version of the Interval Sprint Test protocol first
introduced by Lemmink et al. (2000). This was originally performed by soccer players
outdoors on a grass surface. Our modifications involved an electronic timing system and
slight modifications of the sprint distances, three instead of ten sprints, and the use of a
hockey stick and a hockey ball. The protocol consisted of three maximal sprints of 32 m while
carrying a hockey stick and three maximal sprints of 32 m while dribbling a hockey ball. Each
32-m sprint included a 6-m and a 10-m shuttle sprint. Timing procedures (timing gates),
14
Chapter
I
II
III
IV
V
VI
VII
VIII
meant that the initial and final metres of the sprint were not timed, so data are based on 30-m
distances (Figure 2.1).
Figure 2.1. Course of the Shuttle Sprint and Dribble Test (ShuttleSDT).
The subject began the test standing with both feet behind line A (marked with two cones 2 m
apart). On an auditory signal after a 5 second countdown, the subject sprinted 6 m to line B
(marked with two cones,) touched the line with one foot and returned to line A, again
touching the line with one foot. The subject then sprinted 10 m to line C (marked with two
cones), touched the line with one foot and returned to finish over line A. The subject then
tapered down from the sprint, turned and walked back slowly to line A, there waiting for the 5
second countdown and the auditory signal to start the second sprint. The second sprint started
exactly 20 seconds after the start of the first sprint. After the third sprint the subject was
allowed 5 minutes recovery time, during which he/she walked. The recovery walk was timed
so that the subject had returned to line A 10-20 seconds before the start of the dribbling
portion of the test. The protocol of the dribbling portion was identical to the sprinting portion,
except that the subject was now dribbling a hockey ball.
Timing data were measured by means of photocell gates (Eraton BV, Weert, the
Netherlands) placed at 1.05 m above ground (approximately at hip height) and at 1.0 m behind
line A. The photocells were linked to an electronic timer with an accuracy of 0.01 seconds.
15
The following variables were noted and recorded:
sprint times:
individual sprint times
dribble times:
individual dribble times
peak sprint time:
fastest sprint time
peak dribble time:
fastest dribble time
total sprint time:
total sprint time of the three sprints
total dribble time:
total dribble time of the three dribbles
delta shuttle time:
difference between the total dribble time and the total sprint time
Slalom Sprint and Dribble Test (SlalomSDT)
Based on tests for agility and dribbling skills, the field hockey specific slalom sprint and
dribble test (SlalomSDT) was developed to measure field hockey specific slalom sprint and
dribble performance. The protocol consisted of a maximal slalom sprint of 30 m while
carrying a hockey stick and a maximal slalom dribble of 30 m while dribbling a hockey ball.
Twelve cones were placed in a zigzag pattern (Figure 2.2). Start and finish lines (A and B)
were marked by two cones.
Figure 2.2. Course of the Slalom Sprint and Dribble Test (SlalomSDT).
The subject began the test with both feet behind line A; then, upon an auditory signal after a 5
second countdown, the subject ran with a hockey stick around the 12 cones finishing over line
B. After the run the subject was allowed 5 minutes for recovery, during which he/she walked
slowly. The total distance of the course was 29.07 m. The recovery walk was timed so that the
subject had returned to line A 10-20 seconds before the start of the next portion. The protocol
of the dribbling portion was identical to the sprinting portion, except that the subject was now
dribbling a hockey ball. If the subject lost control of the ball – that is, if the subject was more
than approximately 2 m away from the cones, the test was repeated. Timing data were
16
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measured using a stopwatch. Slalom sprint time, slalom dribble time, and the difference
between the slalom times of the dribble and sprint (delta slalom time) were noted and
recorded accurately to within 0.01 seconds.
Data analysis
The ShuttleSDT data are expressed as mean (standard deviation, sd). To determine the
relation between the times measured, a correlation matrix was calculated for the test scores at
t1. A three way (time x sprint/dribble x test session) analysis of variance with repeated
measures was used to determine differences in times of each sprint/dribble. A Scheffé post
hoc test was used to identify specific differences when the main effects were significant.
To determine the reliability of the ShuttleSDT, the data of both boys and girls were
analysed together. ShuttleSDT reliability analysis was carried out on the peak and total times
of the sprints and dribbles and on the delta shuttle time. The mean difference between the test
results on both days was set as a measure of absolute reliability. If zero lay within the 95%
confidence interval (CI) of the mean difference, we concluded that no bias existed between
the measurements (Bland and Altman, 1986; Altman and Gardner, 1989; Rankin and Stokes,
1998).
To determine relative reliability, we used a one way analysis of variance (ANOVA) to
calculate intraclass correlation coefficients (ICCs) of repeated interval scale measures
(Baumgartner, 1989; Rankin and Stokes, 1998; Baumgartner and Jackson, 1999). Intraclass
correlation coefficients were determined for the peak and total sprint and dribble times and for
the delta shuttle time. Ninety five per cent confidence intervals were determined for all of the
ICCs (Rankin and Stokes, 1998). As a general rule, an intraclass correlation coefficient over
0.90 is considered to be high, between 0.80 and 0.90 moderate, and below 0.80 to be
insufficient for physiological field tests (Vincent, 1995). Baumgartner and Jackson (1999)
stated that ICCs of a minimum of 0.80 are acceptable for physical measures.
The SlalomSDT data are expressed as mean (standard deviation, sd). To determine the
relation between the times measured, a correlation matrix was calculated for the test scores at
t1. A two way (sprint/dribble x test session) analysis of variance with repeated measures was
used to determine the differences in time of the slalom sprint and slalom dribble.
To determine reliability, analysis of variance was performed on the test scores of the
SlalomSDT. For absolute reliability the mean difference with a 95% CI between the testing
days was calculated (Bland and Altman, 1986; Altman and Gardner, 1989; Rankin and
Stokes, 1998). Intraclass correlation coefficients were calculated with a 95% confidence
interval for the slalom times of the sprint and dribble and the calculated delta slalom time to
determine relative reliability.
17
2.3
Results
The correlation matrix of the ShuttleSDT showed strong correlations between the time scores
of the sprinting and dribbling portions individually (Table 2.1). Weak correlations existed
between the time scores of the sprints and the dribbles. The delta shuttle time was strongly
related to the time scores of the dribbling portion of the ShuttleSDT but not to the time scores
of the sprinting portion. SlalomSDT correlations showed a weak relationship between the
slalom sprint and dribble times (r = 0.24). As in the ShuttleSDT, the delta slalom time was
strongly correlated with the slalom time of dribbling a hockey ball (r = 0.90) but not with the
slalom sprint time (r = -0.21).
Table 2.1.
S1
S2
S3
Speak
Intercorrelations (Pearson correlation coefficients) between the sprint (S) and dribble (D)
times, peak sprint and dribble times, total sprint and dribble times, and the calculated
delta shuttle time of the ShuttleSDT at t1 (n = 34). All times are expressed in seconds.
S2
S3
Speak
Stot
D1
D2
D3
Dpeak
Dtot
Delta
0.79*
0.80*
0.94*
0.93*
0.22
0.48*
0.28
0.35
0.35
-0.15
0.78*
0.87*
0.93*
0.30
0.53*
0.45*
0.43
0.47*
-0.02
0.83*
0.92*
0.35
0.60*
0.44*
0.47*
0.50*
0.03
0.95*
0.24
0.52*
0.29
0.38
0.38
-0.13
0.31
0.58*
0.43
0.45*
0.48*
-0.05
0.75*
0.76*
0.93*
0.91*
0.85*
0.76*
0.84*
0.91*
0.69*
0.85*
0.93*
0.81*
0.96*
0.82*
Stot
D1
D2
D3
Dpeak
Dtot
Delta
Note: *Significant correlation coefficient (p < 0.01).
18
0.86*
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There were significant differences between the shuttle sprint and dribble times at both test
sessions (p < 0.01) (Figure 2.3). There were no differences in time scores between the test
sessions (p = 0.98) (Figure 2.3). The mean time of each of the three sprints and dribbles on the
ShuttleSDT increased significantly at both tests sessions (p < 0.01) (Table 2.2). Post-hoc
analysis showed that each of the three sprint and dribble times was significantly different from
the others (p < 0.05). For the SlalomSDT, there were significant differences between the
sprint and dribble time at both test sessions but not between test sessions (p = 0.09).
11
10.5
Sprints t *
1
Sprints t *
2
Dribbles t *
1
Dribbles t *
2
Time (s)
10
9.5
9
8.5
8
1
2
3
Figure 2.3. Group data for the sprint and dribble times of the Shuttle Sprint and Dribble Test
(ShuttleSDT) at the first and second test sessions (t1 and t2). Means and standard
deviations of each data point are presented in Table 2.2.
* Significant difference between first, second, and third sprints and dribbles (p < 0.05)
19
Table 2.2 shows the means and standard deviations of all sprint and dribble times at the first
and second test session (t1 and t2) of the ShuttleSDT and the SlalomSDT, and the mean
difference, standard error and 95% CI of the mean difference to determine absolute reliability
(Rankin and Stokes, 1998). In general, the values of the mean differences between the first
and second test sessions were small when compared with the means of the two test sessions.
With the exception of the slalom sprint time, zero lay within the 95% CI, which indicates
reasonable agreement between the two testing days.
Table 2.2.
Results of the Bland and Altman method for absolute reliability of the ShuttleSDT
(n = 34) and the SlalomSDT (n = 21).
ShuttleSDT
t1 mean (sd)
t2 mean (sd)
Mean d (sd)
SE of d
95% CI
Sprint 1
8.35 (0.525)
8.33 (0.579)
-0.026 (0.446)
0.076
-0.182 – 0.129
Sprint 2
8.55 (0.567)
8.55 (0.632)
-0.007 (0.570)
0.098
-0.206 – 0.192
Sprint 3
8.62 (0.522)
8.61 (0.664)
-0.010 (0.507)
0.087
-0.187 – 0.167
Peak sprint time
8.28 (0.511)
8.29 (0.563)
0.010 (0.434)
0.075
-0.141 – 0.162
Total sprint time
25.52 (1.495)
25.48 (1.817)
-0.043 (1.366)
0.234
-0.520 – 0.434
Dribble 1
10.00 (0.997)
9.83 (1.045)
-0.173 (0.737)
0.126
-0.430 – 0.084
Dribble 2
10.21 (0.964)
10.29 (1.115)
0.077 (0.965)
0.165
-0.260 – 0.413
Dribble 3
10.53 (1.186)
10.45 (1.368)
-0.076 (1.098)
0.188
-0.459 – 0.307
Peak dribble time
9.79 (0.829)
9.66 (0.887)
-0.135 (0.487)
0.083
-0.305 – 0.035
Total dribble time
30.74 (2.882)
30.57 (3.093)
-0.173 (1.912)
0.328
-0.840 – 0.494
Delta shuttle time
5.22 (2.539)
5.09 (2.137)
-0.130 (1.962)
0.337
-0.814 – 0.555
Slalom sprint time
16.36 (0.751)
16.15 (0.736)
-0.207 (0.419)
0.091
-0.398 – -0.016
Slalom dribble time
20.93 (1.689)
20.56 (1.513)
-0.366 (1.366)
0.298
-0.988 – 0.256
Delta slalom time
4.57 (1.679)
4.41 (1.774)
-0.159 (1.403)
0.306
-0.797 – 0.480
SlalomSDT
Note: t1 = first test session, t2 = second test session, sd = standard deviation; d = difference, SE = standard error,
CI = confidence interval. All times are expressed in seconds.
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Intraclass correlation coefficient values to assess the relative reliability of the ShuttleSDT
ranged from 0.71 for the second sprint time to 0.91 for the peak dribble time (Table 2.3). After
excluding the separate sprint and dribble times of the ShuttleSDT, all ICC values (with the
exception of the delta shuttle time (0.79)) met the criterion of 0.80 for reliability.
Intraclass correlation coefficient values to assess the SlalomSDTs relative reliability were
0.91 for the slalom sprint time, 0.78 for the slalom dribble time, and 0.80 for the delta slalom
time (Table 2.3). With the exception of the slalom dribble time, the ICC values met the
reliability criterion of 0.80.
Table 2.3.
Intraclass correlation coefficients for relative reliability of the ShuttleSDT (n = 34) and
the SlalomSDT (n = 21).
ICC
95% CI
Sprint 1 (s)
0.81
0.61 – 0.90
Sprint 2 (s)
0.71
0.42 – 0.86
Sprint 3 (s)
0.78
0.56 – 0.89
Peak sprint time (s)
0.81
0.61 – 0.90
Total sprint time (s)
0.80
0.59 – 0.90
Dribble 1 (s)
0.85
0.70 – 0.93
Dribble (s)
0.73
0.45 – 0.86
Dribble 3 (s)
0.77
0.55 – 0.89
Peak dribble time (s)
0.91
0.82 – 0.96
Total dribble time (s)
0.89
0.77 – 0.94
Delta shuttle time (s)
0.79
0.58 – 0.89
Slalom sprint time (s)
0.91
0.78 – 0.97
Slalom dribble time (s)
0.78
0.45 – 0.91
Delta slalom time (s)
0.80
0.51 – 0.92
ShuttleSDT
SlalomSDT
Note: ICC = Intraclass Correlation Coefficient, CI = Confidence Interval.
21
2.4
Discussion
Using repeated sprint ability and agility tests and taking into account the multidirectional and
technical nature of field hockey, we developed two field tests to determine shuttle sprint and
shuttle dribble performance as well as slalom sprint and dribble performance. Both tests are
practical for use on a regular basis as they can be administered easily and are popular with the
players. Low correlations and significant differences in mean sprint and dribble times on both
tests confirmed our expectation that different physical abilities were being measured. The
course, duration, and repetitive nature of the ShuttleSDT mean that the test is probably of
more importance for defenders and midfielders. Conversely, the course of the SlalomSDT
makes the test more relevant to forwards.
Although several authors use other measures (for example, Pearson’s correlation, 95%
limits of agreement, coefficient of repeatability, and coefficient of variation) mean difference,
standard errors, 95% CI of the mean differences and ICC values have all recently been
reported as being most appropriate and clear in determining reliability (Rankin and Stokes,
1998). In our reliability data, the 95% CIs for the mean differences between the test days can
be interpreted as a minimum difference between the results of individuals that indicate a real
change in performance level. This indicates the accuracy of the test in monitoring changes
over time.
We have no reason to expect that reliability of field testing is influenced by the subjects’
youth or gender. Other characteristics such as heterogeneity, motivation to do well, and
learning capabilities are assumed to be factors that affect reliability in a positive way
(Baumgartner and Jackson, 1999). Our subjects were a homogeneous group, very motivated,
and with above average learning capabilities as they all played field hockey at the highest
regional level. Environmental conditions do influence field testing. Therefore, ambient
temperature, humidity, and wind conditions were all documented. There were only minor
differences in environmental conditions during the test sessions. Furthermore, tests were
conducted on the same artificial grass surface with players wearing their normal playing
footwear.
The measurements with the most robust relative reliability were the peak dribble time in
the ShuttleSDT and the slalom sprint time in the SlalomSDT (ICC = 0.91). As mentioned
earlier, an intraclass correlation coefficient of over 0.90 is considered to be high, between 0.80
and 0.90 moderate, and below 0.80 insufficient for physiological field tests (Vincent, 1995).
Based on these criteria, it is reasonable to suggest that the peak sprint and dribble times and
the total sprint and dribble times of the ShuttleSDT have an acceptable relative reliability. The
delta shuttle time has insufficient reliability (ICC = 0.79). The absolute reliability data of the
ShuttleSDT showed reasonable agreement between both testing sessions.
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Reliability ICC values of the SlalomSDT suggest that the slalom sprint and the delta
slalom time have acceptable relative reliability. The slalom dribble time has insufficient
reliability (ICC = 0.78) and should therefore be used with caution. The reliability of a test
depends on many factors, for instance the nature of a test (Baumgartner and Jackson, 1999).
Scores may not be stable if subjects have not had experience of or practice at the test before
being measured. In tests requiring skill, such as the dribble performance in the ShuttleSDT
and the SlalomSDT, the learning effect may have influenced the reliability of the scores. This
corresponds with the trend of faster times on the dribble performance on the ShuttleSDT and
SlalomSDT seen at t2. However, only the mean difference and 95% CI for the slalom sprint
time on the SlalomSDT between the first and second testing sessions indicated a bias and,
therefore, a lack of absolute reliability. Subjects needed less time for the slalom sprint course
at t2, indicating that there might have been a learning effect. However, only a series of trials
can lead to a more definite conclusion about this.
The reliability coefficients of this study are in line with those obtained when evaluating
other field tests in adult field hockey players and other subjects. A study of a 5-m multiple
shuttle test in 23 female field hockey players to determine players’ match related fitness
reported a range of coefficients from ICC = 0.74 to 0.98 (Boddington et al., 2001). Fitzsimons
et al. (1993) reported correlation coefficients of 0.75 to 0.94 for a running test of repeated
sprint ability in 15 male field hockey players. Pauole et al. (2000) reported an intraclass
reliability coefficient of 0.98 for a test of agility (T test) in college aged men and women.
Finally, Baker et al. (1993) reported a Pearson correlation coefficient of 0.86 for a repeated
maximal shuttle run test in 10 male subjects.
In summary, coaches and trainers can use the field tests examined in this study, as the
demands of the test are important ones for field hockey performance (alternation of high- and
low-intensity activities, agility, speed, and technical skills). It is also a practical test to use on
a regular basis because it can be administered easily. The ShuttleSDT and the SlalomSDT can
help coaches and trainers to assess young athletic talent, diagnose specific weaknesses,
provide information for the development of individualised training programmes, and assess
changes in physical characteristics as result of a training cycle. This study has shown that the
absolute and relative reliability of the ShuttleSDT and the SlalomSDT are satisfactory. The
ShuttleSDT and the SlalomSDT are sports specific field tests to measure sprint and dribble
performances of field hockey players. The tests showed reasonable reliability and can help
coaches and trainers assess young athletic talent, differentiate between players and monitor
changes over time.
23
References
Altman, D.G. and Gardner, M.J. (1989). Calculating confidence intervals for means and their
differences. In Statistics with confidence (edited by M.J. Gardner and D.G. Altman DG), pp. 2027. London.
Aziz, A.R., Chia, M., and Teh, K.C. (2000). The relationship between maximal oxygen uptake and
repeated sprint performance indices in field hockey and soccer players. The Journal of Sports
Medicine and Physical Fitness, 40, 195-200.
Baker, J., Ramsbottom, R., and Hazeldine, R. (1993). Maximal shuttle running over 40 m as a measure
of anaerobic performance. British Journal of Sports Medicine, 27, 228-232.
Bangsbo, J. (1994). The physiology of soccer - with special reference to intense intermittent exercise.
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Baumgartner, T.A. and Jackson, A.S. (1999). Measurement for evaluation in physical education and
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Bishop, D., Spencer, M., Duffield, R., and Lawrence, S. (2001). The validity of a repeated sprint
ability test. Journal of Science and Medicine in Sport, 4, 19-29.
Bland, J.M. and Altman, D.G. (1986). Statistical methods for assessing agreement between two
methods of clinical measurement. Lancet, 1, 307-310.
Boddington, M.K., Lambert, M.I., St Clair Gibson, and A., Noakes, T.D. (2001). Reliability of a 5-m
multiple shuttle test. Journal of Sports Sciences, 19, 223-228.
Boyle, P.M., Mahoney, C.A., and Wallace, W.F.M. (1994). The competitive demands of elite male
field hockey. Journal of Sports Medicine and Physical Fitness, 34, 235-241.
Fitzsimons, M., Dawson, B., Ward, D., and Wilkinson, A. (1993). Cycling and running tests of
repeated sprint ability. Australian Journal of Science and Medicine in Sport, 25, 82-87.
Lemmink, K.A.P.M., Dolleman, G., Verheijen, R., and Visscher, C. (2000). Interval Sprint Test en
Interval Shuttle Run Test – betrouwbaarheid en discriminerend vermogen van twee nieuwe
voetbaltests. [Interval Sprint Test and Interval Shuttle Run Test – reliability and discriminative
power of two new tests for soccer players]. Geneeskunde en Sport, 33, 39-48.
Lothian, F. and Farrally, M. (1994). A time-motion analysis of women’s hockey. Journal of Human
Movement Studies, 26, 255-265.
Pauole, K., Madole, K., Garhammer, J., Lacourse, M., and Rozenek, R. (2000). Reliability and validity
of the T-test as a measure of agility, leg power, and leg speed in college-aged men and women.
Journal of Strength and Conditioning Research, 14, 443-450.
Rankin, G. and Stokes, M. (1998). Reliability of assessment tools in rehabilitation: an illustration of
appropriate statistical analyses. Clinical Rehabalitation, 12, 187-199.
Reilly, T. and Holmes, M. (1983). A preliminary analysis of selected soccer skills. Physical Education
Review, 6, 64-71.
Reilly, T. and Bretherton, S. (1986). Multivariate analysis of fitness of female field hockey players. In
Perspectives in kinanthropometry (edited by J.A.P. Day), pp. 135-142. Champaign, IL: Human
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Reilly, T. and Borrie, A. (1992). Physiology applied to field hockey. Sports Medicine, 14, 10-26.
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25
Chapter III
Relation between multidimensional
performance characteristics and
level of performance in talented
youth field hockey players
Elferink-Gemser, M.T., Visscher, C., Lemmink,
K.A.P.M., and Mulder, Th.(2004).
Journal of Sports Sciences, 22, 1053-1063.
Acknowledgements:
This study has been supported by a grant of the Dutch
National Olympic Committee NOC*NSF. The authors
thank all players, trainers, and staff of the field hockey
clubs HC ’s Hertogenbosch and HC Rotterdam for their
cooperation.
Abstract
To determine the relationship between multidimensional performance characteristics and
level of performance in talented youth field hockey players, elite youth players (n = 38,
mean age 13.2 years, sd = 1.3) were compared with sub-elite youth players (n = 88, mean
age 14.2 years, sd = 1.3) on anthropometric, physiological, technical, tactical and
psychological characteristics. Multivariate analyses with performance level and gender as
factors, and age as the covariate, showed that the elite youth players scored better than the
sub-elite youth players on technical (dribble performance in a peak and repeated shuttle
run), tactical (general tactics; tactics for possession and non-possession of the ball) and
psychological variables (motivation) (p < 0.05). The most discriminating variables were
tactics for possession of the ball, motivation and performance in a slalom dribble. Age
discriminated between the two groups, indicating that the elite youth players were younger
than the sub-elite players. In the guidance of young talented players to the top as well as in
the detection of talented players, more attention has to be paid to tactical qualities,
motivation and specific technical skills.
28
Chapter
3.1
I
II
III
IV
V
VI
VII
VIII
Introduction
In the Netherlands, elite field hockey is played at a higher standard than in many other
countries. The Dutch male field hockey players won the gold medal at the Olympic Games in
Sydney 2000 while the female players won the bronze medal. In Athens 2004, both teams
won silver. To maintain this level of performance, the Dutch National Olympic Committee
has chosen ‘talent identification and development’ as one of its main research programmes. A
talented young athlete is considered to be someone who performs better than his or her peers
during training and competition, and who has the potential to reach the elite level (Howe et
al., 1998; Helsen et al., 2000). Whereas in the 1970s and 1980s scientists focused mainly on
the detection of talented athletes and developed sport talent-detection models (for a review,
see Régnier et al., 1993), recently there has been a shift in emphasis from talent detection to
talent guidance and development (Williams and Reilly, 2000). Talent development is based on
the prediction of performance and consequently on the assumption that underlying factors
determining excellence in sports really do exist (Kroll, 1970; Régnier et al., 1993). In team
games like field hockey, however, the prediction of long-term success in young players is
complex because multidimensional qualities are needed.
In the present study, we focus on youth field hockey players who were already designated
as talented. Every year, many talented Dutch field hockey players are invited to participate in
a selection team for their age category. These teams are provided extra training facilities and
highly qualified trainers. Selected players compete in the highest Dutch junior competition for
field hockey. Although all of these talented players are given the chance to develop their
potential to the full, only a few of them ultimately make it to the top. To develop a successful
sporting career, talented players have to perform at a high level at a young age, indicating
well-developed anthropometric, physiological, technical, tactical and psychological
characteristics (see Figure 3.1).
29
Figure 3.1. Multidimensional performance characteristics and level of performance in field hockey.
The multidimensional performance characteristics shown in Figure 3.1 are based on a limited
number of determining factors for elite field hockey. Unique to field hockey is the semicrouched posture, which causes extra physiological strain on players (Reilly and Seaton,
1990). Competitive match-play is a non-continuous, high-intensity, intermittent activity that
places heavy demands on the aerobic energy system. The anaerobic system is also important:
brief bursts of high-energy release are separated by periods of lower intensity (Bhanot and
Sidhu, 1983; Reilly and Borrie, 1992; Boyle et al., 1994; Lothian and Farrally, 1994).
Consequently, a successful player has to be able to perform successive short all-out sprints.
The intermittent nature of, and the many changes of direction during, match-play underscores
the importance of highly developed sprint capacity and performance in repeated sprints, as
well as of an outstanding slalom sprint performance and interval endurance capacity of elite
players (Reilly and Seaton, 1990; Lemmink et al., 2000).
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Control of the ball while sprinting, turning, passing, and scoring goals is only possible if a
player possesses excellent technical qualities. Straight dribbling is defined as running or
sprinting in a straight line, whereas slalom dribbling is defined as running or sprinting with
quick changes of direction while maintaining control of the ball (Smith and Chamberlin,
1992; Reilly et al., 2000). In Figure 3.1, these qualities make up the technical characteristics.
Due to the nature of field hockey, good sprinting ability, good endurance and the
performance of highly developed technical skills are not sufficient if the timing of actions is
not correct. This tactical knowledge is also referred to as “game intelligence”, and includes
anticipation and decision-making skills. Tests to measure these qualities in soccer players
show consistent differences between skilled and less skilled players (Williams and Davids,
1995; Williams, 2000). Tactical characteristics are part of the multidimensional performance
characteristics in Figure 3.1.
To perform at the top level, elite players must be prepared to invest many hours of
intensive training over many years (Ericsson et al., 1993). They also have to achieve under
high pressure. It is therefore not surprising that psychological characteristics often distinguish
elite from non-elite performers (Mahoney et al., 1987; Morris, 2000).
The multidimensional performance characteristics shown in Figure 3.1 are, to a certain
extent, responsive to training interventions (Hoare and Warr, 2000). In addition, the
environment of a talented player must not be underestimated in that parents and coaches play
an important role in helping talented athletes to improve themselves during their sporting
careers (Bloom, 1985; Carlson, 1988; 1993; Côté, 1999; Visscher et al., 2004).
Until now, only a few multivariate approaches focusing on identifying talent in team
sports have been completed (Deshaies et al., 1979; Pienaar et al., 1998). In these studies, elite
players have been compared to their non-elite counterparts. Reilly et al. (2000) used a
multidisciplinary approach to distinguish between elite and sub-elite soccer players on the
basis of performance on test items. They recommended a study with a pool of already selected
talented athletes who were exposed to systematic training. For this reason, the present study
focuses on youth field hockey players who were all considered to be talented.
The main aim of this study is to determine whether a relationship exists between
multidimensional performance characteristics and level of performance in talented youth field
hockey players. A comparison is made between elite youth players and youth players
immediately below this level, in terms of anthropometric, physiological, technical, tactical and
psychological characteristics.
31
3.2
Methods
Participants
A total of 126 talented field hockey players from 12 selection teams participated in this study.
There were 63 female and 63 male players. The mean age of the female players was 13.9
years (sd = 1.3, range 12-16), the mean age of the male players 13.9 years (sd = 1.4, range 1116). All players were considered to be talented, since they were already playing in a selection
team of a field hockey club of national prestige. Thus that all participants were playing at the
highest level possible for their age category. All players were tested at the end of the 20002001 Dutch competitive field hockey season. Each participant was assessed based on the
following five categories: anthropometric, physiological, technical, tactical and psychological.
Field tests were organized on modern synthetic field hockey playing surfaces (water-based
pitches).
In addition to playing in their club’s selection team, talented Dutch players who are
considered to be current elite youth players are invited to train and play in a youth selection
team of the Dutch Field Hockey Association. Talented players who are considered to be
current sub-elite youth players only play in their club’s selection team. This distinction, based
on the performance of the players in the 2000-2001 season, was also adopted in this study,
resulting in 38 elite youth players and 88 sub-elite youth players. Table 3.1 shows the general
training characteristics of the players.
Table 3.1.
Scores of general characteristics related to training of talented youth field hockey players
classified by gender and level of performance (mean; standard deviation).
Female youth players
Elite players
Sub-elite
Male youth players
Elite players
players
Sub-elite
players
n = 17
n = 46
n = 21
n = 42
Age (years)
13.18 (1.29)
14.15 (1.25)
13.24 (1.26)
14.19 (1.29)
Field hockey experience (years)
8.45 (1.47)
7.65 (1.62)
7.38 (2.01)
7.61 (2.09)
Training sessions per week
2.75 (0.72)
2.21 (0.41)
3.10 (0.62)
2.20 (0.56)
Matches per week
1.05 (0.22)
1.00 (0.00)
1.24 (0.44)
1.00 (0.32)
Procedure
All players were informed about the procedures of the study before providing their verbal
consent to participate. The governing body of the clubs and the trainers also gave their
permission for the study to proceed. The procedures were in accordance with the ethical
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standards of the Medical Faculty of the University of Groningen. The players completed all
tests at the end of the competitive field hockey season. They were told that the results would
be used anonymously and were asked to fill in the questionnaire honestly to ensure maximum
accuracy and validity of the results.
Anthropometric characteristics
Three variables were measured for each player: height, body mass and percentage body fat.
The latter was estimated by means of leg-to-leg bioelectrical impedance (BIA) analysis
(Valhalla BIA, Valhalla, Inc., San Diego, CA, USA). Both within-day and between-day
coefficients of variations of these analyses were comparable to conventional commercially
available BIA systems (Nunez et al., 1997).
Physiological characteristics
All players performed three field tests to determine four physiological characteristics. These
characteristics included peak shuttle sprint performance, repeated shuttle sprint performance,
slalom sprint performance and interval endurance capacity. Peak shuttle sprint and repeated
shuttle sprint performance were measured by means of the Shuttle Sprint and Dribble Test
(ShuttleSDT; Lemmink et al., 2004a) (see Figure 3.2). In this field hockey specific test,
participants have to sprint 30 m three times while carrying a hockey stick. The players are
allowed a short rest between successive 30-m sprints. The length of this rest period depends
on how fast the sprint is performed: the next sprint starts exactly 20 s after the start of the
previous sprint. Each 30-m sprint has three 180-degree turns, which they have to cross with
both feet: after 5 m, participants have to turn and sprint back 6 m. Here they turn for the
second time and sprint 10 m. After turning for the third time, they sprint back 9 m to the
finish. Electronic timing lights are used to measure the time (TAG Heuer, Eraton BV Digital
Timing Equipment, Weert, the Netherlands). Peak shuttle sprint performance is indicated by
the time covered in the fastest of three 30-m sprints; repeated shuttle sprint performance is the
total time covered by all three 30-m sprints.
33
Figure 3.2. Course for the Shuttle Sprint and Dribble Test (ShuttleSDT).
Relative and absolute test-retest reliability were shown for the sprinting part of the
ShuttleSDT (Lemmink et al., 2004a). If the intraclass correlation coefficient (ICC) exceeded
0.80 and if zero lay within the 95% confidence interval (CI) of the mean difference, we
concluded that no bias existed between the two measurements (peak shuttle sprint
performance: ICC = 0.81 and 95% CI for d = -0.141 to 0.162; repeated shuttle sprint
performance: ICC = 0.80 and 95% CI for d = -0.520 to 0.434).
Slalom sprint performance was measured by using the Slalom Sprint and Dribble Test
(SlalomSDT; Lemmink et al., 2004a) (see Figure 3.3). In this field hockey specific test,
players have to sprint 30 m in a zig-zag fashion with twelve 120-degree turns around cones
placed 2 m apart while carrying a hockey stick. Relative test-retest reliability was shown for
the sprinting part of the SlalomSDT (ICC = 0.91), whereas in terms of absolute reliability
there was some evidence of systematic error (95% CI for d = -0.398 to -0.016).
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Figure 3.3. Course for the Slalom Sprint and Dribble Test (SlalomSDT).
Interval endurance capacity was measured by using the Interval Shuttle Run Test (ISRT;
Lemmink et al., 2000; Lemmink and Visscher, 2003) (see Figure 3.4). The ISRT is another
sports specific field test that consists of intervals at a work to rest ratio of 2:1, turns at 20 m
and an increasing running velocity. Players are required to run back and forth on a 20-m
course with pylons positioned 3 m before lines marked out for the turns. The frequency of the
sound signals on a pre-recorded compact disc increases in such a way that running speed is
increased by 1 km·h-1 every 90 s from a starting speed of 10 km·h-1 and by 0.5 km·h-1 every
90 s from a starting speed of 13 km·h-1. Each 90-s period is divided into two 45-s periods in
which players run for 30 s and walk for 15 s. Periods of running and walking are announced
on the pre-recorded compact disc. During the periods of walking, players have simply to walk
back and forth to the 8-m line. Players are instructed to complete as many runs as possible.
The test stops when the player is unable to maintain the required pace (i.e. more than 3 m
before the 20-m lines on two consecutive audio signals) or feels unable to complete the run.
The number of fully completed 20-m runs is recorded as the test score. Players have to carry a
hockey stick during the test. In previous research, this test has been shown to be reliable and
sensitive for differences in level of performance (Lemmink et al., 2000; Lemmink et al.,
2004b).
35
Figure 3.4. Course for the Interval Shuttle Run Test (ISRT).
Technical characteristics
All players performed two field tests to determine three technical characteristics: peak shuttle
dribble performance, dribble performance in a repeated shuttle run and performance in a
slalom dribble. Peak shuttle dribble performance and dribble performance in a repeated shuttle
run were measured using the ShuttleSDT (see Figure 3.2). In performing the test, players had
to keep control of the ball while carrying out the 30-m sprint three times. While turning,
players had to cross each turning line with both feet and the ball. Performance in the peak
shuttle dribble is the time covered by the fastest of three 30-m dribbles; dribble performance
in a repeated shuttle run is the total time covered by all three 30-m dribbles.
Relative as well as absolute test-retest reliability was shown for the dribbling part of the
ShuttleSDT (peak shuttle dribble performance: ICC = 0.91 and 95% CI for d = -0.305 to
0.035; dribble performance in a repeated shuttle run: ICC = 0.89 and 95% CI for d = -0.840 to
0.494).
Slalom dribble performance was measured using the SlalomSDT (see Figure 3.3). In
performing the test, players had to maintain control of the ball while performing the 30-m
sprint with twelve 120-degree turns. Absolute test-retest reliability was shown for the
dribbling part of the SlalomSDT (95% CI for d = -0.988 to 0.256), whereas in terms of
relative reliability there was some evidence of systematic error (ICC = 0.78).
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Tactical characteristics
The trainers evaluated the tactical characteristics of the players. For this purpose, each of the
12 trainers filled out the ‘Tactics in Sports’ questionnaire to give their opinion about three
tactical characteristics of each player: general tactics, tactics for possession of the ball and
tactics for non-possession of the ball.
The ‘Tactics in Sports’ questionnaire is based on two pilot studies (Elferink-Gemser et
al., internal publication 2001). In the first study, 20 highly qualified Dutch field hockey
trainers established the most important tactical qualities for successful field hockey players to
determine the categories of tactical qualities in the questionnaire. The qualities mentioned by
the trainers can be divided into three categories. Category 1 contains general tactics, shifting
in tasks from when the team does not possess the ball to when the team does possess the ball,
and vice versa. Category 2 contains tactical qualities for when the team possesses the ball:
positioning, overview and anticipation. Category 3 contains tactical qualities for when the
team does not possess the ball: man-to-man defence, zone defence and interception.
In the second pilot study, six trainers evaluated 88 elite and sub-elite youth players using
the ‘Tactics in Sports’ questionnaire, designed as a 6-point Likert scale ranging from ‘low/
moderate’ to ‘excellent’. The trainers were instructed to compare each player with top players
in the relevant age category. Test-retest reliability for this checklist on tactical qualities was
shown (Pearson correlation coefficient: Category 1: Z = 0.84, p < 0.01; Category 2: Z = 0.85;
p < 0.01; Category 3: Z = 0.88, p < 0.01). The discriminatory power was obtained by
comparing elite with sub-elite youth players. Scores on tactical qualities differed significantly
for different performance levels (Category 1: Z = -3.954, p < 0.01; Category 2: Z = -5.084, p
< 0.01; Category 3: Z = -6.622, p < 0.01), which means that the elite youth players were
judged to be better than the sub-elite players.
Psychological characteristics
All players filled in a sports specific questionnaire, the Dutch youth version of the
Psychological Skills Inventory for Sports (PSIS) (Mahoney et al., 1987; Pennings et al., 1992;
Bakker, 1995; Companjen and Bakker, 2003). The PSIS was developed for directly assessing
an athlete’s psychological skills relevant to athletic training and exceptional performance. It
assesses motivation, confidence, anxiety control, mental preparation, team emphasis and
concentration. Internal consistency coefficients of all scales ranged from 0.68 for team
emphasis to 0.81 for confidence. Pearson correlation coefficients for test-retest reliability
ranged from 0.64 for team emphasis to 0.79 for mental preparation. The questionnaire
contains forty-four 5-point Likert-type questions. A high score on the scale corresponds to the
37
psychological skill being present to a large extent. The maximum mean score on each scale is
5, and the minimum is 1.
Data analysis
Mean scores and standard deviations were calculated for each variable for the different subgroups according to the five categories of performance characteristics (anthropometric,
physiological, technical, tactical and psychological). A multivariate analysis of covariance
(MANCOVA) was then carried out (factors of performance level and gender) to determine the
effect of performance level and gender on the dependent variables in each category of
characteristics. Since the relationship between test items may change with growth and
development, age in years was considered a covariate. In this way, each variable was adjusted
for age.
Where appropriate, analyses of covariances on each dependent variable were conducted
as follow-up tests to the MANCOVA. To correct for multiple tests and thereby keep the
overall alpha level below 0.05, the Bonferroni method was used.
Finally, all dependent variables were analysed together to determine which combination
of measures best discriminated between the elite and sub-elite youth players. A stepwise
discriminant function analysis was used in which level of performance was the dependent
variable. Besides performance characteristics, age and gender were considered independent
variables. An alpha of 0.05 was adopted for all tests of significance.
3.3
Results
Table 3.2 presents means and standard deviations of the multidimensional performance
characteristics.
38
8.95 (0.40)
27.69 (1.29)
15.09 (0.79)
51.57 (12.72)
8.82 (0.33)
27.15 (1.01)
15.05 (0.69)
55.76 (10.97)
10.13 (0.60)
32.26 (2.06)
19.18 (1.83)
4.18 (1.07)
4.45 (0.78)
4.10 (0.66)
4.66 (0.28)
3.85 (0.53)
3.85 (0.51)
2.28 (0.64)
3.53 (0.49)
3.73 (0.35)
Technical characteristics
Peak shuttle dribble performance 30m (s)
Dribble perf.in repeated shuttle run 3x30m(s)
Performance in a slalom dribble 30m (s)
Tactical characteristics
General tactics (1-6)
Tactics (possession of the ball) (1-6)
Tactics (non-possession of the ball) (1-6)
Psychological characteristics
Motivation (1-5)
Confidence (1-5)
Anxiety Control (1-5)
Mental Preparation (1-5)
Team Emphasis (1-5)
Concentration (1-5)
4.09 (0.57)
3.46 (0.55)
3.85 (0.54)
2.05 (0.83)
3.45 (0.46)
3.42 (0.56)
3.33 (0.79)
3.32 (0.54)
3.52 (0.63)
10.42 (0.58)
33.91 (3.28)
20.19 (4.98)
1.67 (0.01)
56.22 (7.35)
21.77 (5.16)
1.65 (0.01)
54.85 (8.09)
21.51 (5.57)
Female youth players
Elite players
Sub-elite players
n = 17
n = 46
4.55 (0.32)
3.97 (0.69)
4.04 (0.48)
2.22 (0.64)
3.52 (0.48)
3.46 (0.39)
4.38 (1.07)
4.38 (0.85)
4.33 (0.76)
9.72 (0.51)
30.22 (1.75)
17.43 (1.14)
8.52 (0.45)
26.31 (1.43)
14.52 (0.74)
73.00 (25.73)
1.69 (0.01)
55.09 (8.74)
9.61 (2.82)
4.29 (0.45)
3.95 (0.61)
3.94 (0.64)
2.31 (0.75)
3.54 (0.43)
3.46 (0.71)
3.46 (1.03)
3.53 (0.84)
3.75 (0.79)
9.83 (0.59)
30.83 (1.98)
18.48 (2.23)
8.45 (0.35)
26.23 (1.09)
14.61 (0.65)
69.44 (21.50)
1.73 (0.01)
59.69 (12.5)
8.79 (3.89)
Male youth players
Elite players
Sub-elite players
n = 21
n = 42
Mean scores (sd) of anthropometric, physiological, technical, tactical and psychological characteristics for talented youth field hockey
players classified by gender and level of performance.
Anthropometric characteristics
Length (m)
Body mass (kg)
% Body Fat
Physiological characteristics
Peak shuttle sprint performance 30m (s)
Repeated shuttle sprint performance 3x30m (s)
Slalom sprint performance 30m (s)
Interval endurance capacity (runs of 20m)
Table 3.2.
The results of the MANCOVA showed a significant main effect for performance level (see
Table 3.3). The univariate analyses of covariance revealed significant differences between
elite and sub-elite youth players for two physiological variables (peak shuttle sprint
performance [F
= 3.937, p < 0.05] and repeated shuttle sprint performance [F
(1,113)
=
7.498, p < 0.01]), for three technical variables (peak shuttle dribble performance [F
(1,113)
=
(1,113)
11.578, p < 0.01], dribble performance in a repeated shuttle run [F
and performance in a slalom dribble [F
(general tactics [F
(1,113)
(1,113)
(1,113)
= 11.111, p < 0.01]
= 4.822, p < 0.05]), for three tactical variables
= 20.592, p < 0.001], tactics for possession of the ball [F
(1,113)
=
48.051, p < 0.001] and tactics for non-possession of the ball [F (1,113) = 21.822, p < 0.001]) and
for one psychological variable (motivation [F (1,113) = 20.916, p < 0.001]). In all comparisons,
the elite youth players scored better than the sub-elite youth players. However, after correction
for multiple tests, differences between the two groups of players were no longer significant for
physiological variables and performance in the slalom dribble. No differences were found
between the two groups for any of the anthropometric variables.
Table 3.3.
Results of MANCOVA for performance level, gender and performance level x gender.
Wilks’
F-value
lambda
Hypothesis
Error
df
df
p-value
Performance level
0.550
4.084
19
95
< 0.001
Gender
0.192
21.011
19
95
< 0.001
Performance level x Gender
0.837
0.972
19
95
0.500
Besides a main effect on performance level, a significant main effect was found for gender.
The univariate analyses of covariance showed significant differences between female and
male players for anthropometric, physiological and technical variables but not for any of the
tactical or psychological variables, except for confidence. Overall, males scored better than
females. No interaction effects were found between performance level and gender.
Significant differences were also found for age, indicating the relevance of age as a
covariate. Scores improved with age (Wilks’ lambda = 0.363, F = 8.618, Hypothesis df = 19,
Error df = 95, p < 0.001) on the anthropometric, physiological and technical variables, but not
on any tactical or psychological variables.
The results of the stepwise discriminant analysis are presented in Table 3.4. The model
predicted that a combination of four variables would successfully discriminate between the
elite and sub-elite youth players. These measures were selected in the following order of
importance: tactics for possession of the ball (0.716), age (-0.518), motivation (0.463) and
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performance in a slalom dribble (-0.310). Tactics for possession of the ball and motivation
had a positive sign because higher scores denote better performance. With performance in a
slalom dribble, the sign was negative since here a lower score (i.e., less time needed for the
test) indicates better performance. The negative sign of age can be explained by the mean age
of the elite youth players, which was lower than that of the sub-elite youth players.
Table 3.4.
Summary of stepwise discriminant analysis: variables entered/removed.
Wilks’ Lambda
Exact F
Step
Entered
Statistic
df1
df2
df3
Statistic
df1
df2
p-value
1
Tactics (possession of ball)
0.683
1
1
116
53.760
1
116
< 0.001
2
Age
0.626
2
1
116
34.293
2
115
< 0.001
3
Motivation
0.574
3
1
116
28.205
3
114
< 0.001
4
Performance in a
0.551
4
1
116
23.056
4
113
< 0.001
slalom dribble
Note: At each step, the variable that minimizes the overall Wilks’ lambda is entered. Maximum number of
steps is 42. Minimum partial F to enter is 3.84. Maximum partial F to remove is 2.71. F level, tolerance, or
VIN insufficient for further computation.
Variables
Tolerance
F to remove
Wilks’
lambda
Step
1
Tactics (possession of ball)
1.000
53.760
2
Tactics (possession of ball)
0.999
48.624
0.891
Age
0.999
10.447
0.683
Tactics (possession of ball)
0.990
36.307
0.757
Age
0.989
11.659
0.633
Motivation
0.980
10.414
0.626
Tactics (possession of ball)
0.990
33.409
0.713
Age
0.938
14.401
0.621
Motivation
0.966
11.590
0.607
Performance in a slalom dribble
0.941
4.793
0.574
3
4
The average squared canonical correlation was 0.670. This means that, knowing the scores on
tactics for possession of the ball, age, motivation and performance in a slalom dribble,
41
estimation of the percent variance accounted for is 67%. When group membership is predicted
from these four variables, 82.8% of the original grouped players are classified correctly. The
other variables provided no additional information when discriminating further between the
two groups of players.
3.4
Discussion
In most studies of the relation between multidimensional performance characteristics and
level of performance, elite players have been compared with non-elite players. To gain more
insight into the characteristics of “tomorrow’s stars”, it seems that the critical focus should be
on talented youth players already detected. Importantly, most of today’s top performers
played in a youth selection team when they were younger.
Not all young field hockey players who are considered talented will ultimately make it to
the top, as only the very best will achieve elite status in adulthood. One cannot predict with
certainty which talented youngsters will become top players, but elite players, who play in
selection teams of the Dutch Field Hockey Association and of their club, have a better chance
of reaching the top than sub-elite players, who play only in their club’s selection team. This is
why we compared between elite youth players and sub-elite youth players.
Our results showed that the group of talented players as a whole obtained high scores on
all tests. However, the elite players scored better than the sub-elite players on variables from
three categories of characteristics (technical, tactical and psychological). This was the case for
young talented female as well as male players. No significant differences were found between
the performance groups on any of the anthropometric or physiological variables.
Researchers who have compared elite with non-elite players have reported differences in
anthropometric and physiological characteristics (Jankovic et al., 1997; Panfil et al, 1997;
Janssens et al., 1998; Malina et al., 2000), but comparisons between talented field hockey
players seem to suggest similar scores on these characteristics. Evidently, at the elite level,
differences between players are less related to physical and physiological characteristics. This
finding is in accordance with the results of a study by Franks et al. (1999) on young soccer
players, in which it was not possible to discriminate between players at the highest level on
the basis of their physical and physiological profiles.
Elite youth field hockey players scored better than the sub-elite players on the tests for
both peak shuttle dribble performance and dribble performance in a repeated shuttle run.
Sprinting repeatedly over short distances with rapid changes of direction while maintaining
control of the ball is an important attribute for these players. In their multidisciplinary
approach to talent identification in soccer, Reilly et al. (2000) found that elite soccer players
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scored better on dribbling tasks than sub-elite soccer players. It does seem that technical
qualities remain important at the elite level. Van Rossum and Gagné (1994) also confirmed
the importance of technique in field hockey. In their study, Dutch coaches considered
technique (motor skill) one of the most important factors affecting the performance of toplevel field hockey players.
For all tactical variables, again the elite youth players scored better than the sub-elite
players. This was the case for general tactics as well as tactics for possession of the ball and
non-possession of the ball. Knowing when to perform the right action, an attribute also
referred to as ‘game intelligence’, is crucial for a successful career in field hockey. In a study
of perceptual skill in soccer, Williams (2000; p.737) stated that ‘decisions in a match have to
be made under pressure with opponents trying to restrict the “time” and “space” available to
perform’. A key problem, however, is how to measure this tactical insight. Considering the
complex and rapidly changing environment in field hockey matches, it is very difficult to
grade players’ tactical insight objectively. The trainers in this study were highly qualified and
work with the club selection players throughout the season during training and match-play.
These trainers are considered to be experts in the field and their opinion was used to gauge the
tactical insight of the players. Reilly et al. (2000) indicated that an interdisciplinary scientific
approach has to be combined with the accumulated know-how of experts such as trainers,
coaches and scouts.
The only psychological variable for which the elite youth players scored better than the
sub-elite players was motivation. According to Ericsson et al. (1993) and Ericsson (1996),
expert performance is the end result of individual’s prolonged efforts to improve performance,
and since engagement in deliberate practice is not inherently motivating, commitment on the
part of the performer is required.
Reilly et al. (2000) indicated that measures of agility, speed, motivation orientation and
perceptual skill were the most important indicators of talent in soccer. These findings are in
line with Deshaies et al. (1979), who made clear that anaerobic power, speed, perceptual skill
and motivation successfully discriminated between elite and sub-elite ice-hockey players. In
our study, a stepwise discriminant function analysis was used to determine which combination
of measures distinguished most clearly between the two groups of talented field hockey
players. The analyses revealed that the groups could be discriminated on the basis of four
variables. The most discriminating measures were tactics for possession of the ball (consisting
of positioning, overview and anticipation), motivation and performance in a slalom dribble.
Age discriminated between both performance groups, indicating that the elite players were
younger than the sub-elite players. Although the elite players tended to be shorter and lighter
43
than the sub-elite players, one cannot rule out that the most mature children were performing
best at this age, since no maturity measures were taken.
The results from this study suggest that talented players cannot be distinguished from
each other on the basis of the same performance characteristics that discriminate between elite
and non-elite players. At the elite level, differences between players are less related to
physical and physiological characteristics, and more to tactics, motivation and specific
technical skills in field hockey. It is also interesting that the elite players were younger than
the sub-elite players. One explanation is that the elite players started playing earlier and so
had more experience than the sub-elite players (see Table 3.1). It is also possible that the time
needed to master the important characteristics differs between elite and sub-elite players. Elite
players may need less time to develop better performance characteristics. Not only in the
guidance of young talented players to the top, but also in the detection of talented players,
more attention has to be paid to tactical qualities, motivation and specific technical skills
along with the time needed to master these skills.
44
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II
III
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V
VI
VII
VIII
References
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46
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47
Chapter IV
Multidimensional performance
characteristics and performance
level in talented youth field hockey
players: A longitudinal study
Elferink-Gemser, M.T., Visscher, C., Lemmink,
K.A.P.M., and Mulder, Th.
Journal of Sports Sciences (pending minor revisions)
Acknowledgements:
This study has been supported by a grant of the Dutch
National Olympic Committee NOC*NSF. The authors
thank all players, trainers, and staff of the field hockey
clubs HC ’s Hertogenbosch and HC Rotterdam for their
cooperation.
Abstract
To reveal performance characteristics, which may have power for predicting future elite
field hockey players, we made a comparison between 30 elite and 35 sub-elite youth
players in terms of anthropometric, physiological, technical, tactical and psychological
characteristics measured on three occasions, each separated by a time interval of one year.
Mean age of the players on the first measurement was 14.2 years (sd = 1.1). Repeated
measures analyses of covariance with factors of performance level and measurement, and
with age as a covariate, showed that the elite players scored better than the sub-elite players
on technical and tactical variables. Female elite youth players also scored better on interval
endurance capacity, motivation and confidence. Future elite players seem to excel in
tactical skills by the age of 14 already. They also stand out in specific technical skills and
develop these together with the interval endurance capacity better than sub-elite youth
players in the two subsequent years. It will be interesting to follow these players until they
reach elite status in adulthood to verify these conclusions.
50
Chapter
4.1
I
II
III
IV
V
VI
VII
VIII
Introduction
Field hockey is an important sport in the Netherlands. The high level of Dutch field hockey
players is recognised world-wide. To reach elite level, players have to start their intensive and
time-consuming training at an early age (Alabin et al., 1980; Hahn, 1990). According to
Ericsson et al. (1993) and Ericsson (1996), expert performance is the end result of an
individual’s prolonged efforts to improve performance, and since engagement in deliberate
practice is not inherently motivating, commitment from the performers is required.
Notwithstanding the efforts of many players, only a few will become successful in the end.
What is it that characterises the ones who succeed? This question forms the background for
the present paper in which it is attempted to deliver some preliminary answers to this
intriguing question.
A number of studies have focused on performance-related characteristics of elite field
hockey players. Unique requirements of the game include dribbling the ball and moving
quickly in a semi-crouched posture (Reilly and Seaton, 1990). Analysis of the physiological
cost and energy expenditure of playing hockey has placed it in the category of heavy exercise
(Ghosh et al., 1991; Reilly and Borrie, 1992; Boyle et al., 1994; Lothian and Farrally, 1994;
Aziz et al., 2000). Two decades ago, Hargraeves (1984) already showed high VO2max values
for British Olympians, and Withers et al. (1977) did the same for Australian nationals. The
intermittent running, accelerating and decelerating increases the overall effort needed in field
hockey (Patel et al., 2002).
Researchers who focus on talent development in sports often acknowledge that a worldclass performance is the result of several factors (Deshaies et al., 1979; Régnier, 1993;
Pienaar et al., 1998; Reilly et al., 2000). According to Williams and Reilly (2000), research
should adopt a multidisciplinary approach to identify talent. Burwitz et al. (1994) also
recommend interdisciplinary performance-related sports science research. Thereby, Atkinson
and Nevill (2001) have argued that more research should involve sports-specific dependent
variables. We applied a multidisciplinary design in a recent study on talented Dutch field
hockey players. It was shown that a combination of technical, tactical and psychological
characteristics distinguished best between elite and sub-elite youth players (Elferink-Gemser
et al., 2004).
In most previous research, a cross-sectional rather than longitudinal approach has been
applied. However, to improve understanding of the factors that contribute to expert
performance, players should be monitored over a prolonged period of time (Williams and
Reilly, 2000). The goal of the present study is to reveal performance characteristics, which
may have power for predicting future elite field hockey players. Within the pool of talented
players, a comparison has been made between elite and sub-elite youth players in terms of
51
anthropometric, physiological, technical, tactical and psychological characteristics measured
on three occasions, each separated by a time interval of one year. Questions to be answered
are: On which performance characteristics do elite youth players score better than their subelite counterparts? How do elite and sub-elite youth players develop their performance
characteristics over a period of two years, and is there a difference between elite and sub-elite
youth players concerning this development?
4.2
Methods
Participants
One hundred and twenty-six talented field hockey players in the 12-16 age-bracket (mean age
= 13.9, sd = 1.3) participated in a cross-sectional study on the relation between
multidimensional performance characteristics and performance level (Elferink-Gemser et al.,
2004). All participants were part of a talent development program of a field hockey club of
national prestige, and were playing at the highest level for their age category. Within this
group, a distinction was made between 38 elite and 88 sub-elite youth players. In contrast to
sub-elite players, elite players train and play in a youth selection team of the Dutch Field
Hockey Association (KNHB).
From the initial 126 field hockey players, 85 were tested again one year later (t2), and
after two years (t3) 65 players were tested for the third time. Table 4.1 presents the number of
participants at t1, t2 and t3 divided by performance level and gender. Thirty players left the
study because they were no longer part of the talent development program. They continued
playing field hockey but fell back to club performance level. Reasons for players who were
still playing on a national level for leaving the study were not being able to attend the
measurements because of illness or injuries or because of lacking time or transportation.
Table 4.1.
Number of participants at t1, t2 and t3 classified by performance level, gender and number
of players that left the study.
Female players
Male players
Elite
Sub-elite
Elite
Sub-elite
Players that left the study
Elite
Sub-elite
Club level
Measurement
t1
n = 126
17
46
21
42
t2
n = 85
17
25
20
23
1
18
22
t3
n = 65
15
18
15
17
7
5
8
52
Chapter
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II
III
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VI
VII
VIII
The proportion of female and male participants on the three measurements was about the
same. Two female and three male players of the total group (n = 65) were elite players at t1
but sub-elite at t3. One female player was sub-elite at t1 but elite at t3. The other players
remained either elite or sub-elite from t1 through t3. Table 4.2 displays the general
characteristics of the participants concerning age, field hockey experience, training hours and
match play frequency.
Table 4.2.
Mean (sd) scores of general characteristics at t3 concerning age, field hockey experience,
training hours and matches per week for talented youth field hockey players classified by
gender and performance level.
Female youth players
Age
Male youth players
Elite
players
n = 15
Sub-elite
players
n = 18
Elite
players
n = 15
Sub-elite
players
n = 17
15.71 (1.01)
16.40 (1.28)
16.01 (1.00)
16.48 (1.08)
8.43 (2.19)
9.06 (1.69)
8.87 (1.51)
8.80 (2.31)
5.13 (1.65)
4.28 (1.70)
5.18 (0.57)
4.70 (0.31)
8.37 (3.91)
5.64 (2.31)
8.15 (3.64)
7.94 (3.85)
1.07 (0.26)
1.00 (0.00)
1.17 (0.36)
1.00 (0.00)
(years)
Field hockey experience
(years)
Field hockey training
(hours per week)
Total training
(hours per week)
Matches per week
53
Procedure
All players were informed about the procedure of the study before giving their informed
consent to participate. The clubs and trainers gave their permission for this study. The
procedures were in accordance with the standards of the local medical ethics committee of the
University of Groningen. The players completed the tests at the end of the competitive 20002001 field hockey season (t1), at the end of the 2001-2002 season (t2) and at the end of the
2002-2003 season (t3). Ambient temperature, humidity and wind conditions were documented.
Measurements for each player took place according to five categories of performance
characteristics: anthropometric, physiological, technical, tactical and psychological. Field tests
were executed on synthetic field hockey playing surfaces (water-based pitches). The
employed test procedures are described in detail elsewhere (Elferink-Gemser et al., 2004).
Anthropometric characteristics
Anthropometric measurements were length (m), body mass (kg) and percentage of body fat.
The latter was estimated by means of leg-to-leg bioelectrical impedance (BIA) analysis
(Valhalla BIA, Valhalla, Inc., San Diego, CA). This method proved to be reliable for
measuring body fat percentage, and results correlated highly with body fat percentage as
measured with underwater weighing and dual energy X-ray absorptiometry (Nunez et al.,
1997).
Physiological characteristics
All players performed three field tests to determine four physiological characteristics. These
characteristics included peak shuttle sprint performance, repeated shuttle sprint performance,
slalom sprint performance and interval endurance capacity. In all tests, players had to carry
their hockey stick. Peak shuttle sprint and repeated shuttle sprint performance were measured
by means of the Shuttle Sprint and Dribble Test (ShuttleSDT), in which players had to run
three 30-m sprints with 180-degree turning points. Each 30-meter sprint consists of 5 m toand-fro, directly followed by 10 m to-and-fro. Peak shuttle sprint performance is indicated by
the time covered in the fastest of three 30-m sprints, whereas repeated shuttle sprint
performance is the total time covered by all three 30-m sprints. Reliability proved to be
satisfactory in young field hockey players (Lemmink et al., 2004a).
Slalom sprint performance was measured using the Slalom Sprint and Dribble Test
(SlalomSDT), in which players have to sprint 30 m in a zigzag fashion with twelve 120degree turns around cones placed 2 m apart from each other. Reliability for this test was
supported (Lemmink et al., 2004a). Interval endurance capacity was measured with the
Interval Shuttle Run Test (ISRT) (Lemmink et al., 2000; Lemmink and Visscher, 2003). The
54
Chapter
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II
III
IV
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VI
VII
VIII
ISRT is a field test that contains intervals at a work-rest ratio of 2:1, turning points at 20 m
and an increasing running velocity. The number of fully completed 20-m runs is recorded as
the test score. In previous research, this test has proven to be reliable and sensitive for
differences in performance level (Lemmink, et al., 2000; Lemmink et al., 2004b; Lemmink et
al., 2004c).
Technical characteristics
All players performed two field tests to determine three technical characteristics. These
characteristics included peak shuttle dribble performance, dribble performance in a repeated
shuttle run and performance in a slalom dribble. Peak shuttle dribble performance as well as
dribble performance in a repeated shuttle run were measured using the ShuttleSDT;
performance in a slalom dribble was measured using the SlalomSDT. Players now had to keep
control over the ball while performing the tests. Reliability of the dribbling part of both the
ShuttleSDT and the SlalomSDT has been supported in young field hockey players (Lemmink
et al., 2004a).
Tactical characteristics
The trainers evaluated the tactical characteristics of their players. For this purpose, each of the
12 trainers filled out the ‘Tactics in Sports’ questionnaire in order to give their opinion about
three tactical characteristics of each player: general tactics, tactics for possession of the ball
and tactics for non-possession of the ball. The trainers were instructed to compare each player
with the top players in the same age category. In a previous study this questionnaire has
proven to be reliable and sensitive for differences in performance level (Elferink-Gemser et
al., 2001; 2004).
Psychological characteristics
All players filled in a sports-specific questionnaire: the Dutch Youth Version of the
Psychological Skills Inventory for Sports (PSIS) (Mahoney et al., 1987; Elferink-Gemser et
al., 2004). The PSIS was developed for directly assessing an athlete’s psychological skills
relevant to athletic training and exceptional performance. It assesses the level of motivation,
confidence, anxiety control, mental preparation, team emphasis and concentration. This
questionnaire has proven to be reliable in previous research (Bakker, 1995; Companjen and
Bakker, 2003).
55
Data analysis
All data were analysed for male and female players separately using SPSS 10. According to
the five categories of performance characteristics (anthropometric, physiological, technical,
tactical and psychological), mean scores and standard deviations were calculated on
measurements 1, 2 and 3 for the four different subgroups based on performance level (elite
and sub-elite youth players) and gender.
Repeated measures analyses of covariance were used to examine group differences based
on performance level together with differences in performance characteristics over time. Age
was considered as covariate. The statistical techniques provide comparisons of the subgroups
over time, taking into account the possible influence of age.
In the between-subjects analysis, a performance-level effect shows differences in average
scores on measurements 1, 2 and 3 between elite and sub-elite players. In the within-subjects
analysis, a measurement effect shows differences between scores on the three measurements.
An interaction effect between performance level and measurement reveals differences
between elite and sub-elite players that change as a function of time. An alpha of 0.05 was
adopted for all tests of significance.
4.3
Results
Table 4.3 presents mean scores and standard deviations of the multidimensional performance
characteristics for talented youth female field hockey players on the three measurements
classified by performance level. Table 4.4 provides the same information for talented youth
male field hockey players.
56
1.61 (0.08)
49.96 (7.75)
18.77 (6.11)
9.00 (0.30)
27.56 (0.93)
15.21 (0.85)
55.27 (12.08)
10.36 (0.58)
32.86 (1.75)
19.69 (1.78)
4.17 (1.10)
4.19 (0.80)
3.87 (0.65)
4.65 (0.31)
3.68 (0.63)
3.89 (0.48)
2.18 (0.53)
3.54 (0.41)
3.59 (0.41)
Physiological characteristics
Peak shuttle sprint performance 30m (s)
Repeated shuttle sprint perf. 3x30m (s)
Slalom sprint performance 30m (s)
Interval endurance capacity (runs of 20m)
Technical characteristics
Peak shuttle dribble performance 30m (s)
Dribble perf. in rep. shuttle run 3x30m (s)
Performance in a slalom dribble 30m (s)
Tactical characteristics
General tactics (1-6)
Tactics (possession of the ball) (1-6)
Tactics (non-possession of the ball) (1-6)
Psychological characteristics
Motivation (1-5)
Confidence (1-5)
Anxiety Control (1-5)
Mental Preparation (1-5)
Team Emphasis (1-5)
Concentration (1-5)
t1
4.57 (0.37)
3.27 (0.24)
3.94 (0.48)
2.24 (0.74)
3.52 (0.44)
3.65 (0.53)
3.97 (0.93)
3.97 (0.72)
3.59 (0.52)
10.00 (0.46)
31.47 (1.58)
19.02 (2.00)
8.64 (0.33)
26.70 (0.88)
14.93 (0.56)
61.20 (14.98)
1.64 (0.07)
53.73 (7.18)
19.60 (5.66)
Female elite players
n = 15
t2
4.41 (0.48)
3.68 (0.48)
3.81 (0.67)
2.34 (0.68)
3.53 (0.55)
3.52 (0.53)
4.07 (0.59)
4.18 (0.60)
3.91 (0.37)
9.72 (0.46)
30.88 (2.26)
17.61 (1.18)
8.63 (0.26)
26.64 (0.81)
14.75 (0.71)
75.33 (17.16)
1.66 (0.05)
57.14 (6.42)
20.93 (5.94)
t3
4.08 (0.55)
3.37 (0.55)
3.92 (0.51)
1.91 (0.58)
3.54 (0.65)
3.64 (0.59)
3.14 (0.90)
3.63 (0.66)
3.75 (0.71)
10.56 (0.66)
33.90 (2.64)
20.08 (2.72)
9.08 (0.45)
28.08 (1.51)
15.36 (1.20)
49.33 (17.32)
1.67 (0.05)
56.33 (7.09)
22.89 (7.11)
t1
4.32 (0.60)
3.17 (0.41)
3.89 (0.39)
2.19 (0.64)
3.52 (0.38)
3.64 (0.60)
3.27 (1.03)
3.44 (0.86)
3.55 (0.66)
10.23 (0.81)
32.56 (3.47)
18.84 (1.55)
8.78 (0.40)
27.16 (1.36)
15.00 (0.97)
48.67 (12.51)
1.68 (0.05)
58.76 (7.33)
24.16 (5.85)
Female sub-elite players
n = 18
t2
4.10 (0.56)
3.21 (0.56)
3.90 (0.38)
2.00 (0.66)
3.44 (0.35)
3.52 (0.47)
3.67 (0.91)
3.69 (0.75)
3.73 (0.64)
9.99 (0.87)
31.66 (2.87)
18.72 (2.07)
8.72 (0.44)
26.87 (1.35)
15.03 (0.96)
53.89 (16.46)
1.69 (0.04)
60.91 (6.65)
21.84 (6.15)
t3
Mean scores (sd) of anthropometric, physiological, technical, tactical and psychological characteristics for talented youth female field
hockey players on measurements 1, 2 and 3 classified by performance level.
Anthropometric characteristics
Length (m)
Body mass (kg)
% Body Fat
Table 4.3.
1.66 (0.06)
52.57 (8.15)
9.20 (2.23)
8.63 (0.40)
26.61 (1.20)
14.56 (0.70)
68.80 (27.76)
10.02 (0.68)
30.97 (1.89)
17.82 (1.28)
4.33 (0.90)
4.64 (0.83)
4.44 (0.72)
4.52 (0.24)
3.94 (0.70)
4.08 (0.44)
2.34 (0.68)
3.46 (0.43)
3.41 (0.64)
Physiological characteristics
Peak shuttle sprint performance 30m (s)
Repeated shuttle sprint perf. 3x30m (s)
Slalom sprint performance 30m (s)
Interval endurance capacity (runs of 20m)
Technical characteristics
Peak shuttle dribble performance 30m (s)
Dribble perf. in rep. shuttle run 3x30m (s)
Performance in a slalom dribble 30m (s)
Tactical characteristics
General tactics (1-6)
Tactics (possession of the ball) (1-6)
Tactics (non-possession of the ball) (1-6)
Psychological characteristics
Motivation (1-5)
Confidence (1-5)
Anxiety Control (1-5)
Mental Preparation (1-5)
Team Emphasis (1-5)
Concentration (1-5)
t1
4.28 (0.50)
3.51 (0.43)
3.88 (0.91)
2.56 (1.24)
3.48 (0.56)
3.42 (0.85)
4.27 (0.70)
4.13 (0.62)
4.09 (0.68)
9.38 (0.41)
29.40 (1.55)
17.82 (1.13)
8.61 (0.41)
26.21 (1.12)
14.81 (0.91)
79.07 (18.94)
1.72 (0.06)
58.11 (7.71)
7.55 (2.09)
Male elite players
n = 15
t2
4.20 (0.47)
3.70 (0.79)
3.34 (1.07)
3.07 (1.28)
3.42 (0.38)
3.15 (1.01)
4.17 (0.79)
4.09 (0.65)
4.08 (0.50)
9.06 (0.40)
28.45 (1.18)
17.26 (0.94)
8.18 (0.29)
25.13 (0.90)
14.14 (0.54)
101.07 (19.14)
1.76 (0.08)
64.42 (8.03)
7.83 (1.56)
t3
4.30 (0.46)
3.93 (0.61)
4.01 (0.51)
2.13 (0.60)
3.55 (0.43)
3.88 (0.70)
3.65 (0.79)
3.71 (0.61)
3.90 (0.48)
9.91 (0.66)
30.99 (2.41)
18.95 (2.33)
8.58 (0.35)
26.68 (1.44)
14.90 (0.71)
70.82 (22.19)
1.69 (0.08)
54.39 (10.80)
9.51 (4.64)
t1
4.20 (0.61)
3.35 (0.35)
3.88 (0.57)
2.46 (0.58)
3.52 (0.50)
3.65 (0.56)
2.94 (0.83)
3.16 (0.75)
3.26 (0.60)
9.49 (0.77)
29.96 (2.55)
18.55 (1.83)
8.61 (0.41)
26.71 (1.55)
14.76 (0.60)
82.29 (28.85)
1.74 (0.08)
59.28 (9.95)
8.78 (5.30)
Male sub-elite players
n = 17
t2
3.88 (0.64)
3.71 (0.72)
3.95 (0.55)
2.45 (0.42)
3.54 (0.46)
3.73 (0.48)
3.21 (0.59)
3.22 (0.82)
3.31 (0.70)
9.36 (0.64)
29.12 (2.51)
18.36 (1.70)
8.18 (0.36)
25.12 (1.22)
14.39 (1.00)
82.94 (26.07)
1.77 (0.07)
63.93 (9.28)
8.65 (4.50)
t3
Mean scores (sd) of anthropometric, physiological, technical, tactical and psychological characteristics for talented youth male field
hockey players on measurements 1, 2 and 3 classified by performance level.
Anthropometric characteristics
Length (m)
Body mass (kg)
% Body Fat
Table 4.4.
Chapter
I
II
III
IV
V
VI
VII
VIII
Talented youth female field hockey players
We found a significant main effect on performance level. Elite players performed better than
sub-elite players on physiological, technical, tactical and psychological characteristics. Elite
players performed more runs on the interval shuttle run test [F (1,30) = 12.538, p < 0.01]. They
were also faster in the peak shuttle dribble [F (1,30) = 3.146, p < 0.05], repeated shuttle dribble
[F (1,30) = 4.536, p < 0.05] and slalom dribble [F (1,30) = 4.064, p < 0.05], with higher scores on
general tactics [F (1,30) = 8.133, p < 0.01] and tactics for possession of the ball [F (1,30) = 4.719,
p < 0.05]. Finally, elite players were more motivated [F (1,30) = 6.840, p < 0.01] and had more
confidence [F (1,30) = 4.509, p < 0.05] than sub-elite players.
Concerning the development of the performance characteristics in two years, a significant
main effect on measurement was found on anthropometric, physiological, technical and
tactical characteristics. From measurements 1 through 3, players were taller [F (1,29) = 13.481,
p < 0.01) and heavier [F
performance [F
(1,29)
(1,29)
= 7.864, p < 0.01]. They improved on repeated shuttle sprint
= 4.248, p < 0.05] and interval endurance capacity [F
0.01], becoming faster in the peak shuttle dribble [F
(1,29)
(1,29)
= 6.546, p <
= 5.626, p < 0.01] and attaining
higher scores on general tactics [F (1,29) = 7.941, p < 0.01].
We found an interaction effect between performance level and measurement on interval
endurance capacity [F
(1,29)
= 2.600, p < 0.05]. In contrast to sub-elite players, elite players
showed an increase in the number of runs on the interval shuttle run test, especially from the
second to the third measurement (Figure 4.1A). We also found an interaction effect on slalom
dribble performance [F
(1,29)
= 3.178, p < 0.05]. Elite players improved more than sub-elite
players (Figure 4.1D). Finally, we found an interaction effect for confidence [F (1,29) = 3.065, p
< 0.05]. Scores for confidence on the second measurement were lower than on the first
measurement for both elite and sub-elite players. In contrast to the sub-elite players, whose
scores remained relatively stable from t2 to t3, the confidence scores of the elite players on t3
were back to the level of measurement 1. No other interaction effects were found, indicating
that the development from t1 through t3 in test scores is similar for elite and sub-elite players.
59
Interval endurance capacity
Female elite
Female sub-elite
Male elite
Male sub-elite
Number of runs
100
90
11
A
Peak shuttle dribble performance
B
10.5
Time (s)
110
80
70
60
10
9.5
50
40
t1
t2
9
t3
Dribble performance in a repeated shuttle run
t2
t3
Performance in a slalom dribble
C
34
t1
20
D
Time (s)
Time (s)
33
32
31
30
19
18
29
28
t1
t2
Measurement
t3
17
t1
t2
Measurement
t3
Figure 4.1. Performances of the talented youth field hockey players on the three measurements.
Talented youth male field hockey players
As with female players, we found a significant main effect on performance level in male
players. Elite players were faster than sub-elite players in the peak shuttle dribble [F
(1,29)
=
2.914, p < 0.05], repeated shuttle dribble [F (1,29) = 2.988, p < 0.05] and slalom dribble [F (1,29)
= 8.306, p < 0.01] (Figures 4.1B, 4.1C, 4.1D). Compared to sub-elite players, elite players
scored better on general tactics [F (1,29) = 38.883, p < 0.01], tactics for possession of the ball [F
(1,29)
= 23.640, p < 0.01] and tactics for non-possession of the ball [F (1,29) = 25.888, p < 0.01].
Sub-elite players scored better than elite players on concentration [F (1,28) = 6.264, p < 0.01].
A significant main effect on measurement has been found for anthropometric,
physiological, technical and psychological characteristics. From measurements 1 through 3,
players were taller [F (1,28) = 5.802, p < 0.01], heavier [F (1,28) = 3.752, p < 0.05] and had less
body fat [F (1,28) = 3.400, p < 0.05]. They improved on peak shuttle sprint performance [F (1,28)
= 3.623, p < 0.05], repeated shuttle sprint performance [F
sprint [F
(1,28)
= 2.875, p < 0.05], interval endurance capacity [F
slalom dribble [F
(1,28)
= 9.693, p < 0.01], slalom
(1,28)
= 5.915, p < 0.01] and
= 2.635, p < 0.05]. Scores on anxiety control decreased from
measurements 1 through 3 [F (1,27) = 3.678, p < 0.05].
60
(1,28)
Chapter
I
II
III
IV
V
VI
VII
VIII
We found an interaction effect between performance level and measurement for interval
endurance capacity [F (1,28) = 3.699, p < 0.05]. Both elite and sub-elite players improved with
about 12 runs from t1 to t2. From t2 to t3, sub-elite players did not improve in contrast to elite
players, who ran on average over 20 more runs (Figure 4.1A). We also found an interaction
effect for anxiety control [F
(1,27)
= 6.647, p < 0.01]. From t1 through t3, elite players scored
lower in contrast to sub-elite players, whose scores remained relatively stable.
4.4
Discussion
This study deals with talented youth field hockey players in the Netherlands. At the end of the
2000-2001 competitive season we measured 126 players in the 12-16 age-bracket, all part of a
talent development program of a field hockey club of national prestige. Most of today’s top
performers played in a youth selection team when they were younger. We divided the players
into elite and sub-elite youth categories on the basis of membership in an extra selection team
of the Dutch Field Hockey Association (KNHB) in the 2000-2001, 2001-2002 and/or 20022003 seasons. During this study, there was only one player that shifted from sub-elite to elite.
Therefore, it appears that to develop a successful field hockey career, a youth player has to be
part of an extra selection team of the KNHB around the age of 14 already. This, however, is
no guarantee for success, since there are more shifts from elite to sub-elite (n = 5) and from
sub-elite to club level (n = 30), making it clear that in a period of two years more than 25% of
the players could not meet the expectations. It is obviously very hard to predict who will
ultimately reach elite status in adulthood, especially in a team sport. Unlike individual sports,
in which there is a unidimensional performance criterion like time, distance or height, a
performance in team sports depends on the combination of numerous mini-performances of
the player and his team-mates (Régnier et al., 1993). Due to a lack of objective performance
measurements, players therefore have to convince the scout, trainer or coach of their talent.
Over the years, many researchers have attempted to get a grip on the rather vague concept
of talent in studies that concentrated on music (Sloboda et al., 1994a,b; Krampe and Ericsson,
1996; Howe et al., 1998) and sports (Starkes and Deakin, 1984; Starkes, 1987; Helsen and
Pauwels, 1993; Starkes et al., 1994; Helsen and Starkes, 1999). However, the suggestion that
talent provides a basis for predicting excellence is not supported by the current evidence
(Helsen et al., 2000). To justify early identification and selection of talented young athletes, it
seems crucial to gain more insight into the characteristics of ‘tomorrow’s stars’. One way to
do so is by adopting a multidisciplinary, longitudinal approach in which talented youth
players are followed over time until some of them reach elite status in adulthood.
61
In our study, 65 players who have been considered as talented for at least three
consecutive years were followed and measured on three occasions. Both female and male elite
youth players scored better than the sub-elite players on technical and tactical variables. In the
female group, elite players also scored better on interval endurance capacity, motivation and
confidence than sub-elite players, whereas the sub-elite male players had higher scores on
concentration than the elite male players. Hence, the results clearly show that relevant
variables to distinguish between elite and sub-elite players do not stem from a single domain
of performance characteristics. This is in line with a study of Nieuwenhuis et al. (2002) in
which successful and less successful female field hockey players were compared. They
concluded that the successful 14-15 year-old player passes the ball more accurately over a
distance, is faster in covering a short distance, has a broader humerus and femur, and
experiences the competitive situation more positively.
Our results show that maintaining speed while dribbling a hockey ball is important at the
elite level. These findings are consistent with other studies. Reilly and Bretherton (1986)
reported better dribbling control in elite versus county field hockey players, and Keogh et al.
(2003) mentioned better scores for regional representative players in contrast to club-level
players while dribbling a hockey ball through an agility course. Top-level coaches also
confirmed the importance of technique in field hockey (Van Rossum and Gagné, 1994).
It follows that tactical skills – performing the right action at the right moment – seems
crucial for a successful career in field hockey. Our results are in line with other studies
showing that skilled players outscore less skilled ones on tactical elements (Williams et al.,
1993; Williams and Davids, 1995; Enns and Richards, 1997). However, we are aware that the
tactical skill variables in our study do not specify exactly the underlying processes that enable
players to perform the right action at the right moment. In the absence of a practical, reliable
and valid instrument to measure tactical skills, we used the opinion of the trainer to gain
insight into each player’s general tactics, tactics for possession of the ball and tactics for nonpossession of the ball. Because we were unable to measure tactical skills directly with the
player, results may have been confounded. Trainers work with the players throughout the
season during training and match play, and know which players belong to an extra selection
team of the KNHB. They might therefore be inclined to grade those players higher than the
sub-elite players. However, the trainers in this study were highly qualified and considered as
experts in the field, and their opinion was considered as valuable.
Except for the interval endurance capacity in female players, we found no differences in
anthropometric or physiological characteristics between both performance groups. In contrast,
according to Keogh et al. (2003), measures of body fat percentage and short-duration
sprinting speed are useful for distinguishing between field hockey players of different ability.
62
Chapter
I
II
III
IV
V
VI
VII
VIII
However, in their study they compared regional representative players with local club-level
players, and not players who were playing at the national level as was the case in our study.
Evidently, differences between players at the elite level are less related to physical and
physiological characteristics (Elferink-Gemser et al., 2004). Also Franks et al. (1999) could
not discriminate either between young soccer players at the highest level on the basis of their
physical and physiological profiles.
In the female group, elite youth players scored higher on motivation and confidence than
the sub-elite players; we did not find such results in the male group, where on average all
players had high scores on motivation and confidence. It seems that in talent identification and
development, psychological characteristics are more important for female than for male youth
players. When comparing the scores on confidence of the male and female youth players, it
appears that the elite female players had scores similar to those of the male players, whereas
the sub-elite female players had lower scores. Other studies show that male players have on
average higher confidence scores than females (Cox and Liu, 1993; Sewell and Edmondson,
1996), but it seems that this gender difference on confidence cannot be applied at the elite
level.
We found interaction effects showing a different development over time for both
performance groups. Compared to sub-elite youth players, elite youth players improved more
on interval endurance capacity and slalom dribble performance. The improvement of the
interval endurance capacity is consistent with the TOYA study of Baxter-Jones et al. (1993;
1995). They studied the development of aerobic power in young soccer, swimming,
gymnastics and tennis athletes in the 8-16 age-bracket. Results showed that VO2max
increased significantly with pubertal status in males. In our study, male youth players
increased their number of runs on the Interval Shuttle Run Test from the first through the third
measurement. Although we did not take any maturity measures, we do not expect significant
differences between elite and sub-elite players concerning maturation based on a similar
development of their length, body mass and body fat percentage. Nevertheless, one cannot
rule out that the most mature children were performing best at this age. Baxter-Jones et al. did
not show a significant increase in VO2max in the latter stages of puberty in females. In our
study, female sub-elite players increased their score on the ISRT (4 runs) only slightly,
whereas the female elite players were able to increase their ISRT score with 20 runs on
average from the first through the third measurement.
To sum up, elite youth players seem to either score better than sub-elite youth players on
performance characteristics by the age of 14 and subsequently remain better in the following
two years, or they have similar scores to the sub-elite youth players on the first measurement
but develop these characteristics better in the next two years (ages 14-16). Since at the first
63
measurement both elite and sub-elite youth players were active in field hockey for an average
of over 6 years, these findings are not likely to be caused by a difference in active field
hockey experience. However, elite players did seem to train more frequently than sub-elite
players at the age of 14, even at 16. This concerns both specific field hockey training and
general training. These findings are in line with the study of Ericsson et al. (1993), who
proposed a model of expertise based on deliberate practice. They argued that practice is the
only determinant of expertise. However, another explanation could be that the elite youth
players have inherited a more favourable genetic profile for success in field hockey.
According to Howe (1998), a talent originates in genetically-transmitted structures and hence
is at least partly innate. Probably both nature and nurture are essential. In a study of Starkes et
al. (1996), coaches of elite figure skaters acknowledged the role played by talent, but stated
that even the most talented athletes must practice hard to succeed. Until the middle of the 20th
century, it was possible to become an international athlete without belonging to a nation’s
group of most-talented individuals (Bouchard et al., 1997). Today, the level of competition
has increased to the point that only those athletes who combine their talent with intensive
training are potentially able to reach elite status.
In conclusion, in field hockey future elite players seem to excel in tactical skills by the
age of 14 already. They also stand out in specific technical skills and develop these together
with their interval endurance capacity favourably in the subsequent two years, and show high
levels of motivation and confidence. To verify these conclusions, it will be interesting to
follow these players until they reach elite status in adulthood.
64
Chapter
I
II
III
IV
V
VI
VII
VIII
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67
Chapter V
Development of the interval
endurance capacity in elite and subelite youth field hockey players
Elferink-Gemser, M.T., Visscher, C., Van Duijn,
M.A.J.1, and Lemmink, K.A.P.M.
1
Interuniversity Center for Social Science Theory and
Methodology, University of Groningen, the
Netherlands
Abstract
To gain more insight into the mechanisms that underlie the development of the interval
endurance capacity in talented youth field hockey players in the 12-19 age band, 377
complete measurements were taken in a period of three years. A longitudinal model for
interval endurance capacity was developed using the multilevel modelling program
MlwiN. With the model, scores on the Interval Shuttle Run Test can be predicted for elite
and sub-elite boys and girls in field hockey in the age-band of 12-19 years. During
adolescence both male and female elite youth players have a more promising development
pattern of their interval endurance capacity than sub-elite youth players. Besides age,
gender, and performance level, the effect of percentage body fat, additional training, and
motivation was investigated.
70
Chapter
I
II
III
IV
V
VI
VII
VIII
5.1 Introduction
Match analyses make clear that field hockey is a high intensity non-continuous game in which
the physiological demands are considerable, placing it in the category of ‘heavy exercise’
(Ghosh et al., 1991; Reilly and Borrie, 1992; Aziz et al., 2000). In terms of energy
requirements, the aerobic capacity is most important during matches at the elite level.
Although great anaerobic capacity is needed during the many brief bursts of high-energy
release, it is the aerobic capacity that is needed for efficient recovery during the short rest
periods (Bhanot and Sidhu, 1983; Boyle et al., 1994; Lothian and Farrally, 1994). Field
hockey players need a well developed interval endurance capacity to carry out all sorts of
explosive actions such as intermittent running, accelerating, decelerating, cruising, and
dribbling. While performing these actions they repeatedly change their direction to, for
example, overtake an opponent, thereby increasing the overall effort needed in field hockey
(Patel et al., 2002). The interval endurance capacity is the ability to perform high-intensity
activities like running and sprinting, as well as the ability to recover well during low-intensity
activities such as walking and jogging (Lemmink and Visscher, 2003).
In our longitudinal study on performance characteristics of talented youth field hockey
players in the age-band of 12-18 years, a strong improvement in interval endurance capacity is
apparent. As well in boys as in girls, elite youth players improved themselves more than subelite youth players on their interval endurance capacity across a period of two years (ElferinkGemser et al., in revision 2005). When the youth players were on average fourteen years old,
however, differences in interval endurance capacity scores were not significant yet between
elite and sub-elite players (Elferink-Gemser et al., 2004). One year later, there was a trend that
elite players outscored the sub-elite players on their interval endurance capacity, but again
differences were not significant. Two years later, at an average age of sixteen years,
differences were significant, favoring the elite players. Therefore, to unravel the relationship
between the interval endurance capacity and the level of performance in talented field hockey
players, it is essential to gain a deeper insight into the development of this performance
characteristic.
In ‘normal’ children, aerobic capacity, i.e., VO2max, increases proportionally to body size
and mass in both sexes. Most studies show that, when ‘normalizing’ for body size and mass,
VO2max remains stable in males throughout childhood and adolescence while it decreases in
females (Krahenbuhl et al., 1985). The Amsterdam Growth Study, a 23-year follow-up from
teenager to adult about lifestyle and health (Kemper, 2004), shows that in the adolescent
period VO2max increases in males whereas it decreases gradually in females (Kemper and
Koppes, 2004). Generally, anaerobic performance also increases with age. Girls improve from
late childhood to 14-15 years whereas in boys the increase sustains to 19 years. In late
71
childhood and early adolescence gender differences are evident and they are magnified later in
adolescence (Martin and Malina, 1998).
The adolescent period is characterized by an acceleration of somatic growth and rapid
changes in body composition and hormonal status including growth spurt and increase in fatfree mass (Bitar et al., 2000). Anthropometrics such as body height, lean body mass and
percentage body fat influence the physiological aspects of a sports performance, i.e., the
interval endurance capacity. Increase in body height is related to an increase in lung volume
and therefore with an increase in metabolism and endurance. Gain in lean body mass is related
to muscle mass increase and therefore positively influences endurance in contrast to gain in
body fat, which negatively influences endurance (e.g., Astrand et al., 2003).
Most world-class field hockey players are in their twenties and as a consequence athletes
who want to make it to the top have to start training already at a relatively early age, thereby
developing their interval endurance capacity. It is generally known that with training players
can improve their performance by increasing the aerobic and anaerobic energy output during a
particular movement. This is also the case in youth players (e.g. Powers and Howley, 2001).
However, it is not self-evident that all players make use of their full interval endurance
capacity during training or competition. A player has to be motivated to do so since intense
activity can cause uncomfortable side effects such as fatigue and muscle soreness. Motivation
affects the intensity and persistence of a player’s behavior, which in sports can obviously have
a strong impact on his or her performance (e.g., Silva and Weinberg, 1984).
Is it possible to adequately model the development of the interval endurance capacity of
12-19 year-old talented youth field hockey players? Can the development of the interval
endurance capacity of talented field hockey players be explained by age, performance level,
gender, anthropometrics, training, and motivation? These questions led to the goal of this
present study which aim it is to gain more insight into the mechanisms that underlie the
development of a performance characteristic that is important for a successful career in field
hockey: the interval endurance capacity.
5.2
Methods
Participants
In the period 2000-2003, 217 talented field hockey players in the 12-19 years age-bracket
participated in a semi-longitudinal study on the relation between multidimensional
performance characteristics and performance level. This group consisted of 110 male and 107
female players. All participants were part of a talent development program of a field hockey
club of national prestige, and were playing at the highest level for their age category. For three
72
Chapter
I
II
III
IV
V
VI
VII
VIII
consecutive years, measurements were taken at the end of the competitive field hockey season
of 2000-2001 (t1), 2001-2002 (t2), and 2002-2003 (t3). In total 404 measurements were taken
since 77 players were tested on all three occasions (231 measurements), 33 players were
tested on two occasions (66 measurements) and 107 players were tested on one occasion only
(107 measurements). Of these measurements, 392 contained scores of the interval endurance
capacity and 377 measurements were complete in that there were scores on all variables.
Next to being part of a talent development program, talented Dutch field hockey players
who are considered to be current elite youth players are invited to train and play in a youth
selection team of the Dutch Field Hockey Association (KNHB). Talented players who are
considered to be current sub-elite youth players are part of the talent development program of
their field hockey club only. This distinction, based on the performance level of the players
was also followed in this study.
Procedure
All players were informed about the procedure of the study before giving their verbal consent
to participate. The field hockey clubs and trainers gave permission for this study and
procedures were in accordance with the standards of the local medical ethics committee of the
University of Groningen. The players completed the Interval Shuttle Run Test for interval
endurance capacity on a synthetic field hockey playing surface (water-based pitch). Ambient
temperature, humidity and wind conditions were documented. In addition, anthropometric
measurements were taken and the players filled in questionnaires for training habits and
motivation.
Anthropometric characteristics
Anthropometric measurements were height (m), lean body mass (kg) and percentage of body
fat. The latter was estimated by means of leg-to-leg bioelectrical impedance (BIA) analysis
(Valhalla BIA, Valhalla, Inc., San Diego, CA). This method proved to be reliable for
measuring body fat percentage, and results correlated highly with body fat percentage as
measured with underwater weighing and dual energy X-ray absorptiometry (Nunez et al.,
1997).
Interval Shuttle Run Test
Interval endurance capacity was measured with the Interval Shuttle Run Test (ISRT)
(Lemmink et al., 2000; Lemmink and Visscher, 2003). The ISRT is a field test that contains
intervals at a work-rest ratio of 2:1 and turning points at 20 m. Players alternately run for 30
seconds and walk for 15 seconds. The running speed increases from 10 km/hour every 90
73
seconds until exhaustion. The number of fully completed 20-m runs is recorded as the test
score. During the ISRT players were carrying their hockey stick. Although, as a result of the
interval character of the test, anaerobic energy production is important, aerobic energy
production as indicated by VO2max contributes mainly to the total energy requirement during
the test (Lemmink and Visscher, 2003). In previous research, the reliability of the ISRT as a
maximal field test for intermittent sport players has been supported (Lemmink et al., 2000;
2004a). The ISRT also shows discriminative power for players at different levels of
competition supporting the validity of the ISRT for measuring endurance in a more specific
way (Lemmink et al., 2004b).
Training
In the questionnaire players were asked how many field hockey training sessions they attend
per week and what the duration of these training sessions is. Time spend in matches is not
included in the field hockey training since all players spend equal time in field hockey
matches, on average 1 hour per week. Players also filled in how many times per week they
train in other sports or by themselves and what the duration of these training sessions is. Time
spend on physical education at school, which is on average 2.5 hours per week, is excluded.
Outcome variables are field hockey training (hours/week) and additional training
(hours/week).
Motivation
Motivation was measured using the Dutch youth version of the Psychological Skills Inventory
for Sports. The Psychological Skills Inventory for Sports (PSIS-R-5) consists of 5-point
Likert type items that are distributed over 6 scales (Mahoney et al., 1987). The PSIS-R-5 has
been translated into Dutch and subjected to psychometric testing (Bakker, 1995; Companjen
and Bakker, 2003). The Dutch Youth Version of the Psychological Skills Inventory for Sports
(PSIS–Youth) is based upon the Dutch version of the PSIS-R-5, but the formulation of
questions is simpler. The Motivation scale contains eight 5-point Likert type items. The
answer almost never equates to 1, and almost always to 5. Items worded negatively
(indicating a problem or concern) are transformed by reversing the aforementioned 1-5
format. In this way, a high score on each scale corresponds to the psychological skill being
present to a large extent. The mean scale score was calculated which ranged from (1) low to
(5) high. An example of an item is: ‘In my sport, I want to bring out the best in myself’.
74
Chapter
I
II
III
IV
V
VI
VII
VIII
Study design
Measurements were taken annually for three consecutive years from 2001 to 2003. As there
were overlaps in ages between the clusters it was possible to estimate a consecutive 7-year
development pattern. Although subjects returned each year, they were not measured exactly
the same time each year. However, intervals between measurements were never less than 12
or longer than 13 months. The age of the subjects was recorded in months. To create
standardized age groups, the players were classified into age groups at the time of
measurement. A 14-year old player was defined as a player tested within the age range 13.5014.49 years.
Data analysis
Longitudinal changes in interval endurance capacity were investigated using the multilevel
modelling program MlwiN (Goldstein et al., 1998). Multilevel modelling is an extension of
multiple regression, which is appropriate for analysing hierarchically structured data. In the
present longitudinal data set, a simple two-level hierarchy was defined with the repeated
measurements (defined as level 1 units) grouped within the individual players who form the
level 2 units. An advantage of using a multilevel regression modelling approach is that both
the number of measurements and the temporal spacing of the measurements may vary
between players (Maas and Snijders, 2003). A multilevel model describes not only underlying
population trends in a response (the fixed part of the model), but also models the variation
around this mean response due to the time of measurement and due to individual differences
(the random part) (Snijders and Bosker, 2000).
Following Snijders and Bosker (2000, chapter 12) the first step in the multilevel
modelling of the interval endurance capacity data was to establish a satisfactory variance
structure for these longitudinal data, using age (measured as months/12 - 15 years). Then, the
difference between elite and sub-elite-groups was modelled, taking into account interactions
with age and gender. In a next step, the effects of the anthropometric variables, height (m),
lean body mass (kg), and percentage body fat, were investigated. Subsequently, the effect of
the total number of training hours per week, as well as the effect of different types of training
(distinguishing field hockey training and additional training) were investigated. Finally, the
effect of motivation was tested.
5.3
Results
In Table 5.1, the players’ anthropometrics, training, motivation, and interval endurance
capacity scores are presented by gender, performance level, and age.
75
n
39.8 (3.5)
42.3 (4.2)
41.8 (3.2)
44.5 (2.8)
45.7 (3.8)
51.06 (3.85) a
1.61 (0.05)
1.64 (0.07)
1.66 (0.05)
1.69 (0.04)
1.69 (0.04)
1.71 (0.05)a
17.94 (7.32)
18.20 (5.30)
21.18 (5.46)
24.02 (4.67)
25.23 (4.38)
21.95 (3.56) a
15.64 (5.17)
19.05 (6.58)
21.57 (4.76)
22.65 (5.03)
18.88 (5.95)
9.96 (4.72)
10.54 (5.90)
8.47 (4.49)
8.89 (3.93)
9.16 (4.45)
7.31 (4.33)
10.75 (2.82)
9.23 (2.48)
7.94 (2.48)
7.97 (2.70)
7.46 (3.03)
8.90 (1.12)
% Body fat
5.3 (1.9) b
3.3 (2.5)
2.3 (2.4) a
1.5 (1.9)
2.5 (4.5)
2.1 (1.8) b
2.7 (2.2)
1.5 (2.5)
1.4 (1.5)
2.2 (2.5)
1.8 (2.2) a
3.1 (0.4) b
3.1 (0.4)
3.3 (0.9)
3.4 (0.8)
4.5 (1.5)
4.7 (1.0) a
2.8 (2.7)
3.6 (4.4)
4.7 (4.0) a
3.7 (3.5) b
3.4 (3.0) a
1.8 (2.5)
6.2 (2.7)
5.0 (4.5)
3.0 (2.4)
2.4 (2.5) a
2.0 (2.0)
3.0 (2.8)
Additional
training
3.5 (1.1) b
3.7 (1.3)
4.4 (1.1) a
5.5 (1.6)
6.0 (2.2)
3.4 (0.7)
3.5 (0.6)
3.5 (0.9) a
3.5 (0.7) b
4.1 (1.4) a
4.3 (0.9)
3.4 (0.7)
3.9 (1.2)
4.8 (2.2)
5.0 (1.7) a
5.5 (1.4)
5.3 (1.1)
Field hockey
training
3.91 (0.47) b
4.19 (0.58)
4.06 (0.66)
3.93 (0.57)
4.23 (0.56)
4.31 (0.38) a
4.54 (0.29) b
4.55 (0.34) a
4.57 (0.47) b
4.36 (0.52)
4.68 (0.20)
4.22 (0.44)
4.27 (0.44)
4.35 (0.38) a
4.17 (0.50) b
4.17 (0.63) a
4.06 (0.64)
4.51 (0.35)
4.56 (0.30)
4.24 (0.50)
4.27 (0.51) a
4.18 (0.58)
3.56 (0.80)
Motivation
(1-5)
47.79 (16.70)
57.08 (16.29)
51.44 (10.00)
48.88 (15.92)
51.29 (13.14)
46.35 (15.03)
48.50 (12.56)
58.35 (12.92)
65.67 (19.42)
59.80 (18.16)
61.00 (24.12)
54.67 (23.77)
56.58 (18.64)
81.19 (26.16)
84.59 (20.48)
74.16 (27.16)
74.90 (21.49)
58.82 (18.64)
77.10 (23.03)
88.76 (22.53)
91.46 (21.55)
88.86 (29.13)
100.50 (0.71)
ISRT
(runs of 20m)
Note: Field hockey training (hours per week) is exclusive of field hockey matches. Additional training (hours per week) is exclusive of physical education at school.
a
One missing value. b Two missing values.
38.3 (5.1)
41.9 (4.6)
43.9 (3.5)
44.5 (3.3)
48.5 (5.9)
44.4 (7.3)
47.4 (6.8)
48.0 (8.8)
57.0 (6.5)
60.6 (6.3)
67.7 (4.4)
43.3 (5.1)
49.6 (7.0)
52.8 (6.7)
59.7 (4.3)
64.1 (2.9)
70.0 (0.3)
Lean body
mass (kg)
1.56 (0.08)
1.62 (0.08)
1.66 (0.06)
1.67 (0.06)
1.69 (0.07)
1.63 (0.09)
1.67 (0.08)
1.68 (0.10)
1.77 (0.06)
1.79 (0.06)
1.81 (0.05)
Male sub-elite youth players
12-13 years
9
13.04 (0.40)
14 years
26
13.96 (0.32)
15 years
16
14.83 (0.32)
16 years
29
15.97 (0.31)
17 years
25
16.83 (0.34)
18-19 years
21
17.91 (0.45)
Female elite youth players
12-13 years
12
12.95 (0.32)
14 years
17
14.01 (0.32)
15 years
18
14.98 (0.29)
16 years
15
16.07 (0.30)
17 years
9
17.00 (0.31)
18-19 years
0
Female sub-elite youth players
12-13 years
14
12.89 (0.45)
14 years
25
13.88 (0.33)
15 years
18
14.92 (0.38)
16 years
27
15.88 (0.27)
17 years
21
16.79 (0.25)
18-19 years
16
17.84 (0.38)
1.62 (0.06)
1.67 (0.07)
1.73 (0.07)
1.78 (0.04)
1.79 (0.06)
1.81 (0.05)
Height (m)
12.88 (0.55)
13.95 (0.29)
15.04 (0.30)
16.06 (0.27)
16.98 (0.31)
17.94 (0.24)
Age (year)
Mean scores (sd) of talented youth field hockey players presented by gender, performance level, and age.
Male elite youth players
12-13 years
11
14 years
20
15 years
21
16 years
13
17 years
7
18-19 years
2
Cohort
Table 5.1.
Chapter
I
II
III
IV
V
VI
VII
VIII
As expected, in both male and female players height and lean body mass increase with age
whereas percentage body fat tends to decrease in male and increase in female players. With
age, players seem to increase their field hockey training and decrease their additional training.
Motivation scores seem to remain relatively stable with age.
In Figure 5.1, predicted mean scores of the ISRT derived from the multilevel model are
plotted against age for elite and sub-elite boys and elite and sub-elite girls. The general trend
is that the interval endurance capacity increases with age in male youth players. However,
elite youth players improve themselves more across time than sub-elite youth players. In
females, the interval endurance capacity seems to increase with age in elite youth players
only. Sub-elite youth players improve themselves until the age of about fifteen years and
decrease their interval endurance capacity afterwards.
Predicted mean scores
Female elite
Female sub-elite
Male elite
Male sub-elite
110
100
90
ISRT
80
70
60
50
40
30
20
12
14
Age (yrs)
16
18
Figure 5.1. Predicted development of the interval endurance capacity of talented youth field hockey
players in the age-band of 12-18 years.
It was found that a polynomial model of order 2 adequately represents the variance structure
of the data (deviance 3394.0, difference with a fully saturated model of 43.9 on 36 degrees of
77
freedom, p = 0.17). The fixed part of the model contains a different intercept and linear age
term for boys and girls, and a common quadratic term; the random part of the model as a
common level 2 (between-individual) variance and gender-specific level 1 (measurement)
variances. The model was significantly improved by including differential effects of
performance level for age and gender (deviance 3367.8, difference with previous model 26.2
on 3 degrees of freedom, p < 0.01). No effect was found for height and lean body mass, but a
significant negative effect was found for percentage body fat (t = 4.423, p < 0.01). A positive
significant effect was found for additional training (t = 3.374, p < 0.01), whereas no effect was
found for field hockey training as such. Finally, a positive significant effect of motivation was
found (t = 2.726, p = 0.003). The model parameters are given in Table 5.2. The coefficients of
the variables percentage body fat, additional training hours, and motivation are
unstandardized. Their effects, however, can be interpreted such that an additional training
hour could compensate for 1.23 % body fat (1.093/0.889), or likewise, is equivalent to 0.225
points on the motivation scale (1.093/4.86).
Table 5.2.
Final multilevel model for interval endurance capacity data (377 measurements).
Fixed effects
Coefficient
S.E.
p
Constant
52.6
9.10
< 0.001
Age (months/12 – 15 years)
6.21
1.20
< 0.001
Age
-1.83
0.363
< 0.001
Boy
16.5
4.30
< 0.001
Sub-elite
0.786
2.90
0.393
Age x boy
5.27
1.33
< 0.001
Age x sub-elite
-5.11
1.39
< 0.001
Boy x sub-elite
-13.0
4.55
0.002
Percentage body fat
-0.889
0.201
< 0.001
Additional training
1.092
0.324
< 0.001
Motivation
4.86
1.87
0.003
Variance
S.E.
Between-individuals
136.0
25.43
Within-boy
292.8
39.20
Within-girl
105.9
16.31
Deviance
3205.6
2
Random effects
78
Chapter
I
II
III
IV
V
VI
VII
VIII
With the multilevel model for interval endurance capacity, knowing the age of a player, his or
her percentage body fat, additional training hours and motivation, scores on the Interval
Shuttle Run Test for elite and sub-elite boys and girls can be predicted. Derived from the
model in Table 5.2, the equations for the four subgroups are:
Elite boys:
ISRT =
(52.6) + (16.5) + (6.21 + 5.27) X age – (1.83 X age2) –
(0.889 X percentage body fat) + (1.092 X additional training hours) + (4.86
X motivation)
Sub-elite boys:
ISRT =
(52.6) + (16.5) + (0.786) – (13.0) + (6.21 + 5.27 – 5.11) X age –
(1.83 X age2) – (0.889 X percentage body fat) +
(1.092 X additional training hours) + (4.86 X motivation)
Elite girls:
ISRT =
(52.6) + (6.21 X age) – (1.83 X age2) – (0.889 X percentage body fat) +
(1.092 X additional training hours) + (4.86 X motivation)
Sub-elite girls:
ISRT =
(52.6) + (0.786) + (6.21- 5.11) X age – (1.83 X age2) –
(0.889 X percentage body fat) + (1.092 X additional training hours) +
(4.86 X motivation)
Thus, the development of the interval endurance capacity in the age-band from 12-19 years
can be predicted with the multilevel model. For instance, it is predicted that an elite male
player of fifteen years old will increase his performance on the Interval Shuttle Run Test in
one year with (6.21 – 1.83 + 5.27) = 9.65 runs. In contrast, in the period from fifteen to
sixteen years old, a sub-elite male player will increase ‘only’ with (6.21 – 1.83 + 5.27 – 5.11)
= 4.54 runs. An elite girl is predicted to achieve an extra (6.21 – 1.83) = 4.38 runs whereas a
sub-elite girl will run (6.21 – 1.83 – 5.11) = 0.73 runs less according to the model.
In Figure 5.2 the data are represented for the four different gender and performance
groups. In the figure, the lines connect two or three individual yearly observations; the points
are single individual observations. The bold solid lines depict the estimated mean ISRT score
79
for “average” representatives of each group, i.e., with mean scores on percentage body fat,
additional training hours and motivation (8.65, 3.82, and 4.35 for elite boys; 9.15, 3.36, and
4.2 for sub-elite boys; 20.0, 2.84, and 4.53 for elite girls, and 21.6, 1.94, and 4.11 for sub-elite
girls, respectively).
The dotted lines around the bold line indicate the 95% confidence region taking into
account between-individual (level 2) variation. This variation is estimated by the level 2
variance of 136 (see Table 5.2), which is equivalent to a standard deviation of approximately
12 runs, indicating rather large differences between individuals as is also apparent from
Figure 5.2.
The curvature of the lines represents the fitted second-order polynomial (quadratic)
model. It can be observed that the linear effect of the model is most strong for the elite boys
and least strong for sub-elite girls, and approximately equal for sub-elite boys and elite girls
(due to the interaction effects with age and sub-elite). Also visible from the figure is the rather
large within-individual (level 1) variance, which is much larger for boys than for girls,
estimated as 292.8 (equivalent to a standard deviation of about 17 runs) and 105.9 (standard
deviation about 10 runs), respectively.
Female sub-elite
Female elite
80
80
ISRT
120
ISRT
120
40
40
0
0
12
14
16
age (yrs)
18
12
Male elite
16
age (yrs)
18
Male sub-elite
120
80
80
ISRT
120
ISRT
14
40
40
0
0
12
14
16
age (yrs)
18
12
14
16
age (yrs)
18
Figure 5.2. Predicted curves of the interval endurance capacity for elite boys, sub-elite boys, elite
girls, and sub-elite girls.
80
Chapter
5.4
I
II
III
IV
V
VI
VII
VIII
Discussion
Talented field hockey players of twelve years old score on average 35 runs on the Interval
Shuttle Run Test, regardless whether they are a boy or a girl, an elite or a sub-elite player.
Only elite girls score on average 10 runs less, which may indicate that at the age of twelve
years it is still possible for talented girls in field hockey to compensate a relatively low
interval endurance capacity with other performance characteristics, such as great technique
and tactics.
During adolescence, differences between boys and girls become apparent. Boys have a
much faster development of their interval endurance capacity than girls but also within the
male and female group differences are remarkable. At the age of fifteen, elite boys score on
average 15 runs more than sub-elite boys (85 versus 70 runs). At the age of eighteen this
difference has grown to 30 runs (100 versus 70 runs) because elite boys still improve in
contrast to sub-elite boys who seem to remain relatively stable. Although elite girls start of
with a lower score on the ISRT when they are twelve years old, they catch up with sub-elite
girls at the age of about fourteen. At fifteen, they are already better and they keep improving
themselves. After about seventeen years of age they seem to remain relatively stable. The
curve of the elite girls resembles that of the sub-elite boys to a high degree in contrast to the
sub-elite girls who increase their number of runs until the age of fifteen and decrease
afterwards until they fall back to the levels of twelve-year-olds again.
During the Interval Shuttle Run Test for interval endurance capacity both the aerobic and
anaerobic energy production contribute to the total energy requirement (Lemmink and
Visscher, 2003). In a ‘normal’ population of adolescents, boys increase their aerobic and
anaerobic performance with age whereas girls improve to 14-15 years with a gradually
decrease afterwards (e.g., Martin and Malina, 1998; Kemper and Koppes, 2004). The
development of the interval endurance capacity of talented youth field hockey players,
however, is not quite the same as that of ‘normal’ adolescents. Instead of decreasing their
performance after the age of fifteen, elite girls are able to sustain the improvement of their
interval endurance capacity. Although boys and sub-elite girls seem to follow the ‘normal’
pattern, elite boys improve themselves more than sub-elite boys on the interval endurance
capacity.
Although small (one hour extra training represents only one extra run on the Interval
Shuttle Run Test), additional training was found to have a significant effect in improving the
model. The explanation that we did not find such effect for field hockey training may be that
during field hockey specific training more attention is paid to improving other aspects of a
field hockey performance, such as technique and tactics than to endurance capacity. We found
a large variation in additional training between players. In some cases the standard deviation
81
was greater than the amount of additional training itself. Apparently there are major
differences concerning the amount of additional training between talented players, as well
within the elite group as within the sub-elite group.
Motivation was found to have a significant effect in improving the model. The Interval
Shuttle Run Test to measure the interval endurance capacity is a maximal test. The intense
activity needed in this test causes uncomfortable side effects such as fatigue and muscle
soreness, and a player has to be very motivated to continue running until exhaustion.
Motivation can be defined as the direction and intensity of one’s effort. The direction of
behavior indicates whether an individual approaches or avoids a particular situation and the
intensity of behavior relates to the degree of effort put forth to accomplish the behavior (Silva
and Weinberg, 1984). Possibly, motivation in sports is one of the greatest differences between
‘normal’ adolescents and talented field hockey players. The latter are more motivated to get
the best out of themselves and elite youth players want this even more than sub-elite youth
players. Since the road to the top is long, motivation is not only essential for current
performance in a match or test, but also in talent development. Talented players have to
devote long hours of training for many years in a row in order to improve their performance
level (Ericsson et al., 1993; Ericsson, 1996).
Height and lean body mass gave no significant improvement of our model for the
development of the interval endurance capacity. Therefore, any difference between a ‘normal’
population of adolescents and this population cannot be explained from these anthropometric
variables. However, we did find a significant negative effect for percentage body fat. This is
in line with a study on young male gymnasts, swimmers, soccer, and tennis players (BaxterJones et al., 1995). Training did not appear to have affected these young athletes’ growth and
development. However, training can have an effect on percentage body fat (e.g., Astrand et
al., 2003).
There is a rather large variation in interval endurance capacity within and between
players. The within-persons variation, i.e. the variance between measurements, is noticeable
especially in boys and elite girls. This variation was based upon those players that have been
tested repeatedly, which is less than half of the population. Consequently, some bias in this
random effect may have occurred, possibly overestimating it. Since previous research
underscored the reliability of the ISRT, we do not doubt the reliability of the test (Lemmink et
al., 2004a). However, we do not have a clear alternative explanation for this phenomenon. It
might have to do with the moment of testing. At the end of the season, the most important
matches are played and when players are tested a couple of days before an important match,
they might be inclined to take it easy at the test. Other explanations might be differences in
weather conditions or previous training sessions for which it was impossible to control for.
82
Chapter
I
II
III
IV
V
VI
VII
VIII
The between-persons variation is based on the total population and is distinct in the total
age-band of 12-19 years. Evidently, a field hockey performance can be broken down into
many multidimensional performance characteristics, from which the interval endurance
capacity is only one (Nieuwenhuis et al., 2002; Elferink-Gemser et al., 2004). The
combination of anthropometric, physiological, technical, tactical, and psychological
characteristics results in a player’s performance level (Elferink-Gemser et al., 2004). In their
young adolescent years, players still can compensate for less developed performance
characteristics such as their interval endurance capacity. However, towards expertise
performance demands increase and all players need to meet high values for all performance
characteristics, including the interval endurance capacity. Therefore, it is possible for sub-elite
players to possess a great interval endurance capacity, for example because they spend a lot of
time to additional training, but when lacking a high level of other performance characteristics
they will not be able compete at the highest performance level after all.
In sum, the development of the interval endurance capacity of 12-19 year-old talented
field hockey players can be modeled with a polynomial model of order 2 with gender- and
performance level specific intercepts and linear age terms as well as different level 1
variances for boys and girls. Differential effects of performance level for age and gender
significantly improved the model. Results show that during adolescence both male and female
elite youth players have, on average, a more promising development pattern of their interval
endurance capacity than sub-elite youth players. After taking into account the effect of
percentage body fat, additional training hours, and motivation, the remaining differences
between individual players are considerable.
83
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85
Chapter VI
Psychological characteristics of
talented youth athletes in field
hockey, basketball, volleyball, speed
skating, and swimming
Elferink-Gemser, M.T., Visscher, C., and Lemmink,
K.A.P.M.
The Sports Psychologist (in revision)
Acknowledgements:
This study has been supported by a grant of the Dutch
National Olympic Committee NOC*NSF. The authors
wish to express their sincere appreciation to Joke
Tissingh, Karen Oldenziel, Yvonne van Heijzen, and
Alien van der Sluis for their assistance in this research
project.
Abstract
To reveal the relationship between psychological skills and performance level within a
talent group, 458 talented youth athletes (age 14.8 years, sd = 1.5) filled in the Dutch
Youth Version of the Psychological Skills Inventory for Sports with scales for motivation,
confidence, anxiety control, mental preparation, team emphasis, and concentration. A
performance level (elite versus sub-elite) by type of sport (team sports versus individual
sports) by gender multivariate analysis of covariance (2 X 2 X 2) with age as a covariate
resulted in significant effects. Psychological skills distinguished between more and less
successful talented athletes, especially in females. In general, psychological profiles
differed between males and females and between team sport athletes and individual sport
athletes. However, for discrimination of elite and sub-elite youth athletes, motivation and
mental preparation were useful indicators that are independent of gender and type of sport.
88
Chapter
6.1
I
II
III
IV
V
VI
VII
VIII
Introduction
Elite athletes repeatedly have to perform under high pressure, and it is therefore not surprising
that psychological characteristics often distinguish those successful at the highest standard
from their less successful counterparts (Morris, 2000). Early research evidence already
supported an association between psychological characteristics and sports performance
(Morgan and Pollock, 1977; Morgan, 1979; May et al., 1985). Further research evolved with
an emphasis in identifying psychological skills relevant to sport (Meyers et al., 1996).
Mahoney et al. (1987) identified potential constructs assessing motivation, confidence,
anxiety control, mental preparation, team emphasis and concentration. They developed an
instrument that assesses a broad range of psychological skills possessed by athletes and
moreover is sport-specific: the Psychological Skills Inventory for Sport (PSIS-R-5).
Compared to non-elite athletes, elite athletes reported that they were more motivated to do
well in their sport, were more self-confident, experienced fewer problems with anxiety, relied
more on internally referenced and kinesthetic mental preparations, were more focused on their
own performance than that of their team, and were more successful at deploying their
concentration (Mahoney et al., 1987; Mahoney, 1989). So far, many other researchers have
also distinguished successfully elite from non-elite athletes on the basis of their psychological
skills. For example, Grossarth-Maticek et al. (1990) described psychological factors as
determinants of success in football and boxing. Meyers and colleagues (1996) reported better
scores for elite rodeo athletes than non-elite ones on motivation, confidence, anxiety control
and concentration, whereas in a study on Chinese track and field athletes, Cox et al. (1996)
found elite athletes outscoring collegiate level athletes on confidence and anxiety control.
It is not self-evident that the relation between psychological skills and performance level
is similar for different types of sports or for males and females. Various studies have
indicated, for example, that differences exist in psychological skills between individual and
team sports (Feltz and Ewing, 1987; Mahoney et al., 1987; Cox and Liu, 1993) and between
the genders (White, 1993; Chantal et al., 1996; Sewell and Edmondson, 1996; MacIntyre et
al., 1998).
In addition, a relation between psychological skills and performance level has been found
within the highest performance level, i.e. when elite and sub-elite athletes are compared to
each other. Orlick and Partington (1988) reported that among physical, technical and mental
characteristics, mental readiness provided the only statistically significant link with final
Olympic ranking of Canadian Olympians. However, it seems that differences are smaller
when elite athletes are compared to sub-elite athletes rather than to non-elite ones. In a study
on equestrian athletes, elite athletes scored higher than sub-elite athletes on only two of six
psychological skills from the PSIS-R-5 (Meyers et al., 1999) whereas Meyers and colleagues
89
(1994) found no differences in psychological skills between top-ranked (1 to 65), middleranked (75-180), and bottom-ranked (200+) female world-ranked tennis players.
So far, it is not yet clear whether the same psychological variables that distinguish elite
from non-elite or elite from sub-elite athletes in adulthood are important for outstanding
performance throughout the process of talent development (Morris, 2000). To assist young
athletes in reaching elite level, it is important to gain insight into factors that influence the
development of a successful sports career, such as their psychological skills. However, as far
as the authors know, no studies have focused primarily on the relation between psychological
skills and performance level within a talent group. Therefore, this study concentrates on
athletes that have been identified as talent but who have not yet reached the top in adult elite
sports. Two different performance-level groups within a group of all-talented athletes were
compared on psychological skills. The goal of this study was to reveal the relationship
between psychological skills and performance level with possible effects of type of sport and
gender in talented youth athletes.
6.2
Methods
Participants
A total of 458 talented youth athletes (age 14.8 years, sd = 1.5), all of whom participate in
high-level competitive sports in the Netherlands, filled in the Dutch Youth Version of the
Psychological Skills Inventory for Sports (Appendix 6.1). Among them were 124 field hockey
players (62 male and 62 female), 54 basketball players (30 male and 24 female), 121
volleyball players (59 male and 62 female), 72 speed skaters (41 male and 31 female) and 87
swimmers (52 male and 35 female). The participants were divided into 148 elite athletes and
310 sub-elite athletes based on their performance level. Feltz and Ewing (1987) suggest that
an elite-level young athlete can be defined as one who has competed in national-level
competitions and has participated in his/her sport for at least 2 years. In our study, all talented
youth athletes met these conditions. In team sports, elite youth athletes distinguished
themselves from sub-elite athletes by being part of an extra selection team (field hockey:
national or district youth selection team; basketball: national youth selection team; volleyball:
national youth selection team). In speed skating and swimming, elite youth athletes
distinguished themselves from sub-elite athletes by being among the 12 best of their age
category in the Netherlands.
90
Chapter
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II
III
IV
V
VI
VII
VIII
Procedure
All athletes gave their informed consent prior to participation and completed the inventory
individually in a group setting. Instructions were standardized, since obtained scores may be
influenced by changing test instructions (Nideffer, 1987; Greenspan et al., 1988). To allow
mutual comparisons between athletes of different ages, athletes were asked to compare
themselves with top athletes in their age category. To avoid socially desirable answers,
athletes were told that the results were being used solely for research purposes.
Instrument
The Psychological Skills Inventory for Sports (PSIS-R-5) consists of 5-point Likert type items
that are distributed over 6 scales (Mahoney et al., 1987). The PSIS-R-5 has been translated
into Dutch and subjected to psychometric testing (Bakker, 1995; Companjen and Bakker,
2003). The Dutch Youth Version of the Psychological Skills Inventory for Sports (PSIS–
Youth) is based upon the Dutch version of the PSIS-R-5, but the formulation of questions is
simpler. It contains 44 5-point Likert type items, distributed over the same 6 scales as the
PSIS-R-5: Motivation (8 items), Confidence (8 items), Anxiety Control (8 items), Mental
Preparation (6 items), Team Emphasis (7 items) and Concentration (7 items) (see Appendix
6.1). The answer almost never equates to 1, and almost always to 5. Items worded negatively
(indicating a problem or concern) are transformed by reversing the aforementioned 1-5
format. In this way, a high score on each scale corresponds to the psychological skill being
present to a large extent.
Psychometric characteristics
In a study at our Center on the psychometric characteristics of the PSIS-Youth, 381 youth
field hockey and soccer players (age 14.7 years, sd = 1.7; 32% female, 68% male) filled in the
questionnaire (Elferink-Gemser et al., internal publication 2002). Correlations between scales
did not exceed 0.42, supporting the PSIS-Youth as a measure of six relatively independent
constructs. Internal consistency estimates for each scale were acceptably high, ranging from
0.68 on the Team Emphasis scale to 0.81 on the Confidence scale. Apart from the Team
Emphasis scale, Cronbach’s alpha was above 0.70, which is the minimum level recommended
for research purposes (Nunnally, 1978). These internal consistency estimates are in line with
other studies using the PSIS-R-5. White and Croce (1992) found Cronbach alpha reliability
scores ranging from 0.69 to 0.77. White (1993) likewise showed good internal consistency
with alpha coefficients ranging from 0.67 to 0.84, while Meyers et al. (1994), using
discriminant analysis, successfully classified 84% of selected athletes into rank order using
the results of the questionnaire. By contrast, Chartrand et al. (1992) did note internal
consistency problems, with the exception of the confidence factor. This however was an
91
isolated result, which appears to stand opposed to most of the evidence (MacIntyre et al.,
1998).
Data analysis
According to the six categories of psychological skills (motivation, confidence, anxiety
control, mental preparation, team emphasis and concentration), mean scores and standard
deviations were calculated for the eight different subgroups based on performance level (elite
youth athletes and sub-elite youth athletes), type of sport (team sports and individual sports)
and gender. To make mutual comparisons between scales possible, scores on each of the six
scales are also presented as means on the 5-point Likert scale (minimum score = 1; maximum
score = 5) ± standard deviation. Because of the nature of competition of speed skating and
swimming in the Netherlands, in which an emphasis is placed on individual performance, the
questions in the Team Emphasis scale are not valid for the individual sport athletes in this
study (e.g., “I get very frustrated when a teammate is performing poorly”). Consequently,
only athletes from team sports answered questions in this scale.
Data were analyzed using multivariate analysis of covariance (MANCOVA) general
linear models (GLM) procedure. As part of the GLM procedure, least-squares means are
calculated. For the MANCOVA, performance level, type of sport and gender served as the
independent variables, while the categories of psychological skills served as the multivariate
dependent variable. Age was considered as a covariate since the relationship between
psychological skills and performance level may change with age. In this way, each variable
was adjusted for age.
Univariate analyses of covariance (ANCOVA) with factors of performance level, type of
sport and gender and with age as a covariate were carried out separately for each
psychological variable, with follow-up analyses to clarify the source and nature of significant
relationships. The ANCOVA for the Team Emphasis scale was conducted with scores of the
team sport athletes only. An alpha of 0.05 was adopted for all tests of significance.
6.3
Results
A performance level by type of sport by gender multivariate analysis of covariance (2 X 2 X
2) resulted in significant main effects for performance level [F (5,445) = 5.18, p < 0.01]; type of
sport [F (5,445) = 23.90, p < 0.01] and gender [F (5,445) = 9.70, p < 0.01]. Table 6.1 displays the
means of the psychological skills for categories of performance level, type of sport and
gender.
92
Team Emphasis (TM)
Scale score
5-point Likert Scale score
Concentration (CC)
Scale score
5-point Likert Scale score
Anxiety Control (AX)
Scale score
5-point Likert Scale score
Mental Preparation (MP)
Scale score
5-point Likert Scale score
25.11 (3.17)
3.59 (0.45)
24.57 (3.99)
3.51 (0.57)
24.68 (3.47)
3.53 (0.50)
14.40 (4.64)
2.40 (0.77)
14.70 (4.15)
2.44 (0.69)
24.45 (3.02)
3.50 (0.43)
32.10 (4.53)
4.01 (0.57)
31.77 (4.80)
3.97 (0.60)
31.99 (4.80)
4.00 (0.60)
32.38 (4.42)
4.06 (0.54)
34.95 (3.56)
4.37 (0.45)
36.74 (2.68)
4.59 (0.34)
25.26 (3.95)
3.61 (0.56)
19.55 (4.67)
3.26 (0.78)
32.32 (5.22)
4.04 (0.65)
31.84 (4.43)
3.98 (0.55)
35.84 (2.96)
4.48 (0.37)
26.60 (3.54)
3.80 (0.50)
17.66 (4.81)
2.94 (0.80)
30.74 (4.59)
3.84 (0.57)
32.57 (4.76)
4.07 (0.60)
34.74 (3.50)
4.34 (0.44)
Male athletes
Team sports
Individual sports
Elite
Sub-elite
Elite
Sub-elite
n = 60
n = 91
n = 31
n = 62
25.57 (2.96)
3.65 (0.42)
25.37 (3.09)
3.62 (0.44)
14.56 (4.17)
2.43 (0.70)
31.27 (4.21)
3.91 (0.53)
30.00 (4.70)
3.75 (0.59)
37.08 (2.12)
4.64 (0.27)
24.00 (3.40)
3.43 (0.49)
23.85 (2.94)
3.41 (0.42)
12.75 (4.97)
2.12 (0.83)
31.13 (4.47)
3.89 (0.56)
27.67 (4.66)
3.46 (0.58)
34.31 (4.12)
4.29 (0.52)
27.00 (3.77)
3.86 (0.54)
17.11 (4.63)
2.85 (0.77)
29.63 (4.94)
3.70 (0.62)
30.00 (4.93)
3.75 (0.62)
35.16 (2.32)
4.40 (0.29)
26.05 (3.60)
3.72 (0.51)
16.97 (3.84)
2.83 (0.64)
29.31 (5.46)
3.66 (0.68)
31.04 (5.20)
3.88 (0.65)
33.67 (3.21)
4.21 (0.40)
Female athletes
Team sports
Individual sports
Elite
Sub-elite
Elite
Sub-elite
n = 38
n = 110
n = 19
n = 47
Mean scores (sd) of the psychological skills as a function of performance level, type of sport, and gender (N = 458).
Motivation (MV)
Scale score
5-point Likert Scale score
Confidence (CF)
Scale score
5-point Likert Scale score
Table 6.1.
Performance level
In the relation of psychological skills and performance level we found significant main effects
for motivation and mental preparation (Table 6.2). We also found significant interaction
effects for confidence (performance level by type of sport), team emphasis (performance level
by gender) and concentration (performance level by gender).
Table 6.2.
Summary of univariate F-Ratios calculated using Type III sums of squares with
Hypothesis df = 1 and Error df = 449 for MV, CF, AX, MP, CC and Error df = 294 for
TM (General Linear Model).
Psychological Skills
MV
CF
AX
MP
TM
CC
Performance level (P)
23.06**
0.62
1.55
3.56*
1.24
0.95
Type of sport (T)
6.36**
4.15*
6.22**
56.95**
Gender (G)
1.73
26.28**
10.04**
4.98*
PXT
1.87
3.92*
0.47
0.01
PXG
0.89
0.73
0.57
0.01
TXG
0.93
1.37
0.94
0.37
0.26
PXTXG
0.15
1.83
0.26
2.53
0.23
15.65**
0.17
0.63
1.66
7.33**
5.81**
Note: * p < 0.05. ** p < 0.01.
Elite athletes scored higher than sub-elite athletes on motivation and mental preparation,
irrespective of gender or type of sport. Regardless of gender, elite athletes also scored higher
than sub-elite athletes on confidence, but this can only be applied to team sports (p < 0.01).
No significant differences between elite and sub-elite athletes were found in individual sports
(p > 0.05). On team emphasis, female elite athletes had better scores than female sub-elite
athletes (p < 0.01), but male elite and sub-elite athletes had similar scores (p > 0.05). On
concentration, again female elite athletes scored better than female sub-elite athletes (p <
0.01), whereas male scores of elite and sub-elite athletes did not differ significantly (p > 0.05).
These results can be applied to both team and individual sport athletes.
Type of sport
In the relation of psychological skills and type of sport, we found significant main effects for
motivation, confidence, anxiety control, mental preparation and concentration (Table 6.2).
Irrespective of performance level or gender, team sport athletes had higher scores than
individual sport athletes on motivation and anxiety control, whereas on confidence, mental
preparation and concentration individual athletes outscored team sport athletes. We also found
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a significant interaction effect for confidence (type of sport by performance level). Among
elite athletes, team sport athletes had scores similar to individual sport athletes (p > 0.05),
whereas among sub-elite athletes individual sport athletes outscored team sport athletes (p <
0.01). This can be applied to males as well as females.
Gender
In the relation of psychological skills and gender, we found significant main effects for
confidence, anxiety control and mental preparation (Table 6.2). Regardless of performance
level or type of sport, male athletes outscored female athletes in all these psychological skills.
We also found significant interaction effects for team emphasis and concentration (both
gender by performance level). Among elite athletes, females and males had similar scores on
team emphasis (p > 0.05), but in the sub-elite group male athletes scored better than female
athletes (p < 0.01). Elite female athletes outscored their male counterparts on concentration (p
< 0.05), while no significant differences based on gender were found among sub-elite athletes
(p > 0.05). These results can be applied to both team and individual sport athletes.
6.4
Discussion
The goal of this study was to reveal the relationship between psychological skills and
performance level with possible effects of type of sport and gender in talented youth athletes.
To accomplish this purpose, two different performance level groups within a group of alltalented athletes were compared on psychological skills.
The relation between motivation and performance level, i.e. that elite youth athletes
outscore sub-elite ones is in general congruence with studies examining differences between
elite and sub-elite players in adulthood (e.g., Smith and Christensen, 1995; Chantal et al.,
1996). It is however unclear whether athletes perform better because of their high motivation
or whether they are more motivated because of their high performance level. The relation
between mental preparation and performance level favoring elite athletes in contrast to subelite ones was also found in a study on golf players in which skilled golfers reported greater
mental preparation than less skilled ones (Thomas and Over, 1993).
The relation between psychological skills and performance level is different in male than
in female athletes. Male elite athletes outscore sub-elite athletes on motivation and mental
preparation only, female elite youth athletes distinguish themselves from their sub-elite
counterparts by their higher scores on four of six psychological skills as measured with the
PSIS-Youth (motivation, mental preparation, team emphasis and concentration). In team
sports, confidence can be added to this list.
95
The relation between psychological skills and performance level is also different in team
sport and individual sport athletes. In team sports, elite youth athletes outscored the sub-elite
athletes on confidence, whereas all individual sport athletes scored relatively high on this
scale, taking the highest possible scores into account. Weinberg and Gould (1999) stated that
less confident athletes doubt whether they are good enough or whether they have what it takes
to be successful. Positive feedback about their performance is thought to build confidence.
Duda and Nicholls (1992) also stated that confidence plays an important role in beliefs
regarding success in sports. In individual sports, the athlete gets feedback individually most of
the time as opposed to team sports, in which feedback is mostly presented to the team as a
whole. Only the best performers get positive feedback individually, which may be an
explanation for the difference in confidence scores between elite and sub-elite team sport
athletes found in this study. In addition, team sports are characterized by a lack of objective
performance measurements, making it hard to give feedback. Unlike individual sports, in
which there is a unidimensional performance criterion like time or distance, a performance in
team sports depends on the combination of numerous mini-performances of the player and his
teammates (Régnier et al., 1993).
Comparably to our study, Cox and Liu (1993) found that those athletes exhibiting the
highest levels of mental preparation were the individual sport athletes. They concluded that
this might be due to the fact that individual sport athletes do not have the luxury of being able
to rely on their teammates. Another explanation could relate to the character of the individual
sports in this study. Speed skating and swimming are cyclic sports in which the same
movement pattern is repeated frequently. During mental preparation, this movement pattern
can be practiced in one’s head. In team sports like field hockey, basketball and volleyball,
environmental characteristics change constantly, which makes mental preparation more
difficult.
Noise and sounds during training and competition are distracters that can complicate an
athlete’s concentration. These distracters are part of most team sports, whereas more quiet
environments are expected for most individual sports (Weinberg and Gould, 1999). Individual
sport athletes may therefore have better environmental circumstances to concentrate. This is in
congruence with the results obtained in our study, in which team sport athletes tended to
report significantly lower concentration than individual sport athletes. Thus it seems logical
that concentration and mental preparation are related to each other (Weinberg and Gould,
1999), supporting our findings that individual sport athletes outscore team sport athletes in
both concentration and mental preparation.
Average motivation scores of all athletes were very high in that they surpassed 4.0 on a 5point Likert scale. Hence all talented athletes have a relatively high motivation related to
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sports. Before reaching the top, an athlete has to invest many years in training. According to
Ericsson et al. (1993), only those who are committed to their sport can persist in deliberate
practice. Therefore athletes have to be highly motivated if they are to have a chance of
becoming elite athletes. This supports what has been reported in other research with talented
children in disciplines other than sports (e.g., Bloom, 1985).
In contrast to their high motivation scores, all athletes had relatively low mental
preparation scores in comparison to the other scales. This confirms a statement of Reilly
(1996), who notes that “an increasing minority of soccer players are now paying attention to
psychological preparation”. In their study on professional sport psychology in Ireland,
MacIntyre et al. (1998) also reported low levels of mental preparation in top athletes. Since
mental preparation distinguishes elite from sub-elite athletes, it seems valuable to give more
attention during training to developing this psychological skill in talented youth athletes.
To provide them with an external reference point, all participants were told to compare
themselves with top athletes in their age category. Although this may be difficult, we think it
is easier than comparing themselves with adult elite athletes. Because of this age-bound
external reference point, we did not draw conclusions for different age groups. It is also
interesting to gain insight into the development of psychological skills though. This is
possible only when all athletes are provided with the same external reference point, e.g. the
absolute top in adult sports.
Researchers who focus on talent development in sports often acknowledge that a worldclass performance is the result of several factors (e.g., Deshaies et al., 1979; Régnier et al.,
1993; Reilly et al., 2000; Elferink-Gemser et al., 2004). Accordingly, it is recommended to
relate the described relationships between psychological skills and performance level to other
performance characteristics, such as an athlete’s physiological, technical and tactical
characteristics. Only by adopting a multidisciplinary design can the relative contribution of
psychological skills to performance level be made clear. Nonetheless, from this study it
becomes clear that psychological skills can distinguish between more and less successful
talented athletes, especially among females. Psychological profiles differ between males and
females, and between team sport athletes and individual sport athletes. However, for
differentiation purposes between elite and sub-elite athletes within a talent group, motivation
and mental preparation are useful indicators that are independent of type of sport and gender.
97
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Appendix 6.1.
Motivation
Items distributed over the six scales of the PSIS-Youth.
I am very motivated to do well in my sport.
I sometimes lack the motivation to train.
Winning is very important to me.
Right now, the most important thing in my life is to do well in my sport.
My sport is my whole life.
I want to train hard to belong to the top in my sport.
In my sport, I want to bring out the best in myself.
I want to succeed in my sport
Confidence
In most competitions, I go in confident that I will do well.
It doesn’t take much to shake my self-confidence.
A minor injury or a bad practice can really shake my self-confidence.
I have frequent doubts about my athletic ability.
When I begin to perform poorly, my confidence drops very quickly.
I can usually remain confident even through one of my poorer performances.
My self-confidence jumps all over the place.
I have faith in myself.
Anxiety
I am more tense before I perform than I am during the performance.
Control
I am often panic-struck during those last few moments before I begin my
performance.
I spend a lot of energy trying to stay calm before a meet.
I get nervous, because I want to start performing.
I am anxious to perform in strange places.
Before a meet, I worry if I will do well.
Before important meets, I feel intense anxiety.
The period right before a performance feels unpleasant.
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Mental
I often dream about competition.
Preparation
I often “rehearse” my performance in my head before I perform.
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When I mentally practice my performance, I “see” myself performing- just like
I was watching a videotape.
When I am preparing to perform, I try to imagine what it will feel like in my
muscles.
When I close my eyes, I can imagine what my muscles feel like.
I prepare for a meet by making mental representations of my performance.
Team
I get very frustrated when a teammate is performing poorly.
Emphasis
I concentrate more on my own performance than on the performance of the team.
I think team spirit is very important.
When my team loses, I feel badly – no matter how well I did as an individual.
I think the performance of the team is more important than my individual
performance.
If my teammates don’t exert themselves to the utmost, I get angry.
If I decline the performance level of the team, I have to be replaced.
Concentration I often have trouble concentrating during my performance.
I experience frequent “hot streaks” in which my performance is unusually good.
When I am performing poorly, I tend to lose my concentration.
During my performance, I am incommoded by comments of people surrounding 66
me.
At the beginning of my performance, I have trouble forgetting things I was
doing before.
During my performance, others distract me.
I can concentrate better on a difficult meet than on an easy one.
Note: Items were rated on a 5-point scale, using anchors of 1 = almost never and 5 = almost always, while
comparing oneself with top players in the same age category.
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Chapter VII
Development of the Tactical Skills
Inventory for Sports
Elferink-Gemser, M.T., Visscher, C., Richart, H.,and
Lemmink, K.A.P.M.
Perceptual and Motor Skills, 99, 883-895
Acknowledgements:
This study has been supported by a grant of the Dutch
National Olympic Committee, NOC*NSF. The authors wish
to thank Nynke Schippers for her assistance in collecting the
data.
Abstract
Purpose of this study, in which 19 trainers and 415 competitive youth field hockey and
soccer players (age = 15.9 years, sd = 1.6; 283 boys and 132 girls) selected by their age,
sex, and performance status participated, was to develop a practical, reliable, and valid
measure of tactical skills in sports. With trainers, 34 questions were formulated involving
tactical skills. Factor analysis yielded the Tactical Skills Inventory for Sports. Scales were
labeled Positioning and Deciding, Knowing about Ball Actions, Knowing about Others,
and Acting in Changing Situations, covering all aspects of tactical skills regarding
declarative versus procedural knowledge, and attack and defense. Internal consistency and
test-retest measures for reliability (except Knowing about Ball Actions) were within
acceptable limits. Elite players scored better than non-elite players, supporting construct
validity. The inventory is suitable for measuring tactical skills in youth field hockey and
soccer players in sports practice.
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Introduction
Elite athletes not only need well-developed physiological and technical characteristics, but
certain cognitive characteristics too (French and Thomas, 1987; Starkes, 1987; Williams et
al., 1993; Helsen and Starkes, 1999; Nougier and Rossi, 1999). This certainly applies to
players of invasive games, in which players compete at the same field of action as their
opponents. Invasive games are time dependent and can be subcategorized into goal-throwing
(e.g., basketball), try scoring (e.g., rugby), and goal striking games (e.g., soccer). A
characteristic of invasive game players is that they constantly need to adapt to opposition by
punctual adaptation to new play configurations and to the circulation of the ball (Gréhaigne
and Godbout, 1995). In this type of games, players have to deal with a complex and rapidly
changing environment while invading the opposing team’s area of the field to score (Almond,
1986; Williams, 2000; Hughes and Bartlett, 2002).
A common way to categorize the cognitive skills needed in sports is the distinction in
declarative and procedural knowledge (Anderson, 1982; Thomas and Thomas, 1994; Turner
and Martinek, 1999). Both motor skills and tactical skills have elements of declarative
knowledge and procedural knowledge (McPherson and Kernodle, 2003). Declarative
knowledge includes knowledge of the rules and goals of the game (French and Thomas, 1987;
Williams and Davids, 1995), whereas procedural knowledge involves the selection of an
appropriate action within the context of the game. In other words, ‘knowing what to do’ refers
to declarative knowledge and ‘doing it’ refers to procedural knowledge (McPherson, 1994).
Bjurwill (1993) stated that, only if a player has a proper understanding of the game, that is,
only when he is very good at ‘reading the game’, can the player be a top player.
So far, many different terms have been used to describe the concept of performing the
right action at the right moment. The action and the moment are right when the performance
or outcome is successful. For example, Bjurwill (1993) used the terms ‘game intelligence’ and
‘reading the game’. Many other descriptors have been applied, including ‘implicit
knowledge’, ‘practical intelligence’, ‘tricks of the trade’, ‘tactical knowledge’, and ‘tactics’
(Davids and Myers, 1990; McPherson, 1994; Gréhaigne and Godbout, 1995; Gréhaigne et al.,
1999). At present the term ‘tactical skills’ is utilized (McPherson and Kernodle, 2003).
Tactical Skills refer to the quality of an individual player to perform the right action at the
right moment; it should therefore be distinguished from strategy, which refers to choices
discussed in advance with the trainer in order for the team to organize itself (Gréhaigne and
Godbout, 1995).
Most studies of tactical skills applied experimental test situations in which, for example,
subjects viewed action sequences on a video projection screen (e.g., Starkes and Deakin,
1984; Williams et al., 1993; Bard et al., 1994; McMorris and Graydon, 1997; Helsen and
105
Starkes, 1999). Others, especially cognitive psychologists, have used propositional-type
analyses of subjects’ think-aloud protocols to examine the representation of conceptual
knowledge, e.g., declarative, procedural, and to examine how this knowledge guides the
solution process during problem-solving or task performance (McPherson, 1994).
Although these settings are useful for fundamental research, they are less suitable for
applied purposes. In the field, there is a clear need for information about the tactical skills of
individual players, for example, to help trainers guide players toward a higher performance.
Information on tactical skills could also prove to be very valuable in leading talented players
to the top or in evaluating training effects. Therefore, the goal of this study is to construct an
inventory that can be used in sports practice; that is a practical, reliable, and valid measure of
tactical skills in sports.
7.2
Methods
To construct the self-reporting inventory, the theoretical elements on tactical skills according
to the framework created by McPherson (1994) with one continuum that moves from response
selection to response execution and the other continuum that moves from knowledge
(knowing what to do) to action (doing it), were discussed with 19 highly qualified trainers of
youth national and district selection sports teams in the Netherlands. They were asked to put
forward those elements they considered most important for high performance. Elements
frequently named as important were overview, anticipation, fast switching from ball
possession to no ball possession and vice versa, positioning, man-to-man defense, zone
defense, and interception (Elferink-Gemser et al., 2004). These elements are specific to match
play in invasive games and concern mostly the combination of picking up relevant
information from the environment and reacting to that. Questions were formulated and
reformulated until consensus was reached on the content of the inventory within the team of
experts. Thirty-four items were put in questionnaire form; these were answered on a 6-point
scale regarding sports performance with anchors of 1 = very poor and 6 = excellent or of 1 =
almost never and 6 = always, while comparing oneself with top players in the same age
category (Table 7.1). Factor analysis was applied in Study 1 to examine the structure of
relations among the items in the original sample with the purpose of bringing them together
into a smaller set of variables or constructs (Nunnally and Bernstein, 1994). After that, the
internal consistency of the inventory was examined in Study 2A and test-retest reliability in
Study 2B. Starkes (1987) pointed out the importance of cognitive abilities in the development
of skill in field hockey, whereas Williams et al. (1993) concluded that experienced soccer
players’ cognitive knowledge permitted more meaningful associations between players’
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positions resulting in more efficient retrieval. Based on these studies showing that elite
players in field hockey and soccer have better cognitive features than lower-performance
players, construct validity was examined by comparing scores of players at different playing
levels.
7.3
Study 1: Factor Analysis
Method
Participants
A total of 209 youth players (age = 15.8 years, sd = 1.6 years, range = 12.6 - 18.9 years), all
participating in competitive field hockey (n = 123) or soccer (n = 86), gave their informed
consent prior to participation. This population consisted of 148 boys and 61 girls. All players
were given the same instructions and were taught in the same way. They filled out the original
sample of 34 questions individually.
Data analysis
Principal component analysis of the 34 item sample, with four factors fixed, followed by
varimax rotation, yielded a structure which accounted for 50% of the response variance. The
number of four fixed factors was based on the transition point in the scree plot where
successive eigenvalues are plotted against component number (Nunnally and Bernstein,
1994). Items that met the criterion of loading at greater than or equal to 0.55 with a factor
were selected to make interpretation of the inventory possible (Kline, 1994; Smith et al.,
1995).
Results
Twenty-three items met the criterion and are indicated in Table 7.1. Factor 1 consists of Items
1, 2, 4, 5, 6, 7, 8, 9 and 10, and, based on their content, is labeled Positioning and Deciding.
Factor 2 consists of Items 16, 17, 18, 19 and 20, and is labeled Knowing about Ball Actions.
Factor 3 consists of Items 11, 15, 21, 22 and 23, and is labeled Knowing about Others. Factor
4 has Items 3, 12, 13 and 14, and is labeled Acting in Changing Situations. These four factors
make up the four scales in the 23 item Tactical Skills Inventory for Sports.
107
My interception of the opponent’s ball is*
My positioning during a match is generally*
My overview (in ball possession or in team’s ball possession) is*
My anticipation (thinking about proceeding actions) is*
3
4
5
6
In the opinion of my trainer, my understanding of the game is*
My getting open and choosing position is*
In the opinion of my trainer, my positioning is*
My judgment of the opponent’s play is*
My interception of the ball is*
8
9
10
11
12
I know quickly how the opponent is playing*
I know exactly when to pass the ball to a teammate or when not to*
16
0.27
0.06
0.33
During matches, I look not only at the ball but also look over the fieldx
15
0.29
I quickly react to changes, as from not possessing the ball to ball possession*
14
0.04
If our team loses the ball during a match, I quickly switch to my task as defender*
0.36
0.29
0.21
0.32
0.67
0.64
0.73
0.65
0.22
0.71
0.66
0.76
0.26
0.69
0.68
0.50
1
13
During matches I quickly make decisionsx
I apply rules of the game smartly to matches
x
I am good at making the right decisions at the right moments*
7
I know my strong and weak points exactly
I know how to get open during a match*
2
x
Decisions I make during matches about proceeding actions are generally*
I know which position I should take during matchesx
Items
Original 34 items and their factor loadings (n = 209).
1
#
Table 7.1.
0.60
0.18
0.51
0.46
0.20
0.49
0.45
0.05
0.10
0.20
0.29
0.08
0.21
0.33
0.14
0.34
0.10
-0.04
0.23
0.09
0.10
2
3
0.09
0.57
0.07
-0.12
-0.04
0.14
0.14
0.41
0.62
0.13
0.07
0.16
0.25
0.19
0.24
0.24
0.10
0.25
-0.04
0.14
0.23
Factor
0.01
0.16
0.13
0.63
0.80
0.22
0.20
0.68
0.15
0.04
0.15
0.09
0.12
0.05
0.10
-0.08
0.12
0.72
0.09
0.05
0.26
4
If an opponent receives the ball, I know exactly what he is going to do*
23
0.05
0.28
0.29
0.07
0.25
0.17
0.12
0.28
0.47
0.35
0.23
0.56
0.63
0.50
0.60
0.43
0.45
0.60
0.46
0.63
0.60
0.48
0.37
0.66
0.43
0.43
0.47
0.12
0.12
0.43
0.11
0.16
0.32
-0.10
0.06
0.50
0.12
-0.02
0.07
0.03
0.15
0.44
0.03
0.13
0.18
Items not meeting the criteria of a > 0.55 factor loading.
†Item omitted after reliability studies.
*Items meeting the criteria of a > 0.55 factor loading.
x
items 11, 15, 21, 22, 23: Knowing about Others; Factor 4 = items 3, 12, 13, 14: Acting in Changing Situations.
included. Factor 1 = items 1, 2, 4, 5, 6, 7, 8, 9, 10: Positioning and Deciding; Factor 2 = items 16, 17#, 18, 19, 20: Knowing about Ball Actions; Factor 3 =
with top players in the same age category. The numbers indicate the item number in the Tactical Skills Inventory for Sports; unnumbered items were not
Note: Items were rated on a 6-point scale, using anchors of 1 = very poor and 6 = excellent or of 1 = almost never and 6 = always, while comparing oneself
Without seeing my teammates, I know where they are going*
22
If I receive the ball from a teammate, I know in advance where to pass the ball
If our team loses ball possession, I know exactly what to do
x
Although I do not see my opponents, I know where they are going*
21
x
If I possess the ball, I know exactly whom I have to pass to*
20
0.21
0.33
While receiving the ball, I do not have to look where my teammates are; I already knowx
While executing an action in a match, I know exactly what to do subsequently*
0.21
If we receive the ball (getting ball possession), I know exactly what to do*
I quickly react to correct mistakes of my teammates
0.03
0.06
I see the weak points of the opponent quicklyx
x
0.01
0.06
I quickly adapt my play to circumstances, such as rainy or windy weather*†
19
18
17
I know quickly what to do to win a matchx
7.4
Study 2: Reliability A – Internal Consistency
Method
Participants
A different sample of 206 competitive youth field hockey players (n = 139) and soccer players
(n = 67) filled out the Tactical Skills Inventory for Sports (age = 15.9 years, sd = 1.7 years,
range = 12.2 - 19.3 years; 135 boys and 71 girls). Again, all players gave their informed
consent prior to participation, and procedures were equivalent to those in Study 1.
Data analysis
Raw data were screened for missing values. In case of 20% or more missing values within a
scale, a participant was excluded from the analysis. Otherwise, a missing value was replaced
by the participant’s mean score on the scale involved. Item-total correlations, interitem
correlations, Cronbach coefficients alpha for internal consistency, and interscale correlations
were used to assess reliability. Concerning item-total correlations, items should correlate more
with the scale to which they are assigned than with a different scale. With regard to the
interitem correlations, items should correlate positively within their assigned scale. Scales
should have a Cronbach coefficient alpha of at least 0.70 (Nunnally, 1978), and interscale
correlations should not exceed 0.80 (Carron et al., 1985).
Results
None of the participants had 20% missing values or more. Means, standard deviations, and
Cronbach coefficients alpha for the inventory are presented in Table 7.2.
Table 7.2.
Descriptive statistics and internal consistencies (α) of the four subscales of the Tactical
Skills Inventory for Sports (n = 206).
Scale
Mean
sd
α
1
Positioning and Deciding
3.79
0.61
0.89
2
Knowing about Ball Actions
4.11
0.62
0.75
3
Knowing about Others
3.74
0.67
0.74
4
Acting in Changing Situations
4.15
0.69
0.72
Sum of scales
3.95
0.51
0.91
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Internal consistency estimates for the scales ranged from 0.72 to 0.89. Item-total correlations
showed that items had higher correlations with their assigned scale than with any other scale,
with the exception of Item 11 (which correlated 0.50 with the assigned Scale 3 and 0.51 with
Scale 1), Item 12 (which correlated 0.50 with the assigned Scale 4 and 0.54 with Scale 3) and
Item 17 (which correlated 0.31 with the assigned Scale 2 and 0.33 with Scale 3). Interitem
correlations within each scale were all positive, ranging from 0.17 to 0.75. The interscale
correlations varied from 0.37 between Scales 1 and 4 and 0.59 between Scales 1 and 3 (Table
7.3).
Table 7.3.
Tactical Skills Inventory for Sports interscale correlations (n = 206).
Scale 1
Scale 2
Scale 3
Scale 4
0.52
0.59
0.37
0.56
0.48
Scale 2
Scale 3
0.54
Scale 4
Note: Scale 1 = Positioning and Deciding; Scale 2 = Knowing about Ball Actions
Scale 3 = Knowing about Others; Scale 4 = Acting in Changing Situations
7.5
Study 2: Reliability B - Test-retest
Method
Participants
From the participants of Study 2A, a sample of 47 competitive youth field hockey players
filled out the inventory twice (age = 15.6 years, sd = 1.58 years, range = 12.3 - 18.7 years; 18
boys and 29 girls). The second session took place two to four weeks after the first
questionnaire completing session, to minimize test-retest effects.
Data analysis
Mean scores and standard deviations for the four scales and the sum of scale scores for the
first measurement (t1) and second measurement (t2) were calculated. Baumgarter (1989)
identified two types of reliability, relative and absolute. Relative reliability is the extent to
which individuals maintain their position in a sample with repeated measurements. Absolute
reliability is how much repeated measurements vary for individuals. It provides an indication
111
of the variability in repeated tests for specific individuals, irrespective of the individual’s rank
in a particular sample (Atkinson and Nevill, 1998; 2001).
The mean difference between the test scores on both days was set as a measure of
absolute reliability. If zero lay within the 95% confidence interval of the mean difference, it
was concluded that no bias existed between the two measurements. To estimate relative
reliability, a one-way analysis of variance was conducted to calculate Intraclass Correlation
Coefficients (ICCs) of repeated measures. Ninety-five percent confidence intervals were
calculated for all ICC’s (Rankin and Stokes, 1998). An ICC above 0.75 was considered to
indicate good stability (Lee et al., 1989; Streiner and Norman, 1995).
Results
Zero lay within the 95% confidence interval of the mean difference for Scales 1, 3, and 4 and
the sum of scales. Scales 1, 3 and 4 and the sum of scales had an ICC varying between 0.76
and 0.89. Only Scale 2 did not meet the criterion, with an ICC of 0.53 (Table 7.4).
Table 7.4.
Measures for absolute and relative reliability of the Tactical Skills Inventory for Sports
(n = 47).
t1
t2
t1 – t
SE of
95% CI
ICC
95% CI
(sd)
(sd)
(sd)
t1 – t2
for t1 – t2
Scale 1
3.3 (0.6)
3.4 (0.5)
-0.06 (0.35)
0.05
-0.17 – 0.04
0.88
0.78 – 0.93
Scale 2
3.7 (0.6)
3.4 (0.4)
0.30 (0.60)
0.09
0.13 – 0.48
0.53
0.16 – 0.74
Scale 3
3.3 (0.7)
3.3 (0.6)
0.00 (0.59)
0.09
-0.17 – 0.17
0.76
0.57 – 0.87
Scale 4
3.8 (0.7)
3.7 (0.7)
0.09 (0.54)
0.08
-0.07 – 0.25
0.82
0.67 – 0.90
Sum of scales
3.5 (0.5)
3.5 (0.4)
0.08 (0.31)
0.05
-0.00 – 0.17
0.89
0.80 – 0.94
for ICC
Note: t1 – t2 = mean difference between scores from testing times 1 and 2; SE of t1 – t2 = Standard Error of the
mean difference; 95% CI for t1 – t2 = 95% Confidence Interval for the mean difference; ICC = Intraclass
Correlation Coefficient; 95% CI for ICC = 95% Confidence Interval for each Intraclass Correlation
Coefficient.
7.6
Study 3: Construct validity
Elite and non-elite youth players were compared on the basis of their scores on the Tactical
Skills Inventory for Sports. It was hypothesized that the elite youth group would have higher
mean tactical skills scores than the non-elite youth group. Youth players participating in the
highest national leagues for their age were considered elite youth players, whereas youth
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players at a moderate performance status, i.e., played in a regional competition, were
considered non-elite youth players.
Method
Participants
A total of 148 youth field hockey players filled out the inventory. Among them were 76 elite
youth field hockey players (age = 15.7 years, sd = 1.7 years, range = 12.8 - 18.4 years; 34
boys and 42 girls) from Study 1 and 72 non-elite youth field hockey players (age = 15.3 years,
sd = 1.7 years, range = 12.3 – 18.7 years; 28 boys and 44 girls) from Study 2. Again, all
players gave their informed consent prior to participation, and procedures were equivalent to
those in Study 1 and Study 2.
Data analysis
Mean scores and standard deviations were calculated for each scale and the sum of scales. The
scores of the elite players were then compared with those of the non-elite players using an
analysis of variance.
Results
The lowest mean scores were obtained for Scale 3, Knowing about Others; the highest mean
scores for Scale 4, Acting in Changing Situations. The mean Scale 3 score of the elite youth
field hockey players was 3.8, and their mean scale score was 4.3 for Scale 4, whereas nonelite youth players showed means of 3.4 for Scale 3 and 3.8 for Scale 4 (Table 7.5). On all
scales, elite youth players scored higher than non-elite youth players (p < 0.01).
Table 7.5.
Scale score statistics for groups playing at different skill levels (n = 148).
Scale
Elite players
Non-elite players
(n = 76)
(n = 72)
1
Positioning and Deciding
3.97 (0.56)
3.43 (0.61)
2
Knowing about Ball Actions
4.22 (0.57)
3.77 (0.68)
3
Knowing about Others
3.77 (0.60)
3.41 (0.72)
4
Acting in Changing Situations
4.25 (0.65)
3.82 (0.69)
Sum of scales
4.05 (0.44)
3.61 (0.55)
Note: Elite and non-elite player groups’ mean scores differed on all scales and the sum of scales (p < 0.01).
113
7.7
Discussion
The goal of this study was to construct a practical, reliable, and valid measure of tactical skills
in invasive game players. The content of the inventory was selected with the help of a team of
expert trainers. Factor analysis yielded four scales which were labeled Positioning and
Deciding, Knowing about Ball Actions, Knowing about Others, and Acting in Changing
Situations.
Two factors (2 and 3) contain questions more related to declarative knowledge. In these
factors, Knowing about Ball Actions and Knowing about Others, knowledge of the game is
the central element. The other two factors (1 and 4) contain questions more related to
procedural knowledge. In these factors, Positioning and Deciding, and Acting in Changing
Situations, selection of the appropriate action is the central element. A way to categorize
elements of tactical skills related to the nature of match play in invasive games is by making a
distinction between on-the-ball and off-the-ball situations (Oslin et al., 1998). Tactics related
to scoring or attack can be distinguished from tactics related to preventing scoring or defense
(Bjurwill, 1993). According to Mitchell (1996), tactical skills such as maintaining possession
of the ball, attacking the goal, and creating space in the attack are similar across invasive
games, as are defending space or defending against an attack. Among the four factors, Factors
1 and 2 are more related to the attack, whereas the other two factors (3 and 4) are more related
to defense. Questions for Positioning and Deciding and for Knowing about Ball Actions
mostly concern situations in which the team possesses the ball. Questions in Knowing about
Others and Acting in Changing Situations, on the other hand, mostly concern situations in
which the opposing team possesses the ball. By combining both ways of categorizing
elements of tactical skills, i.e., declarative versus procedural knowledge and attack versus
defense, the four factors in the inventory cover all four of these aspects of tactical skills.
Cronbach coefficients alpha of all four scales were above the criterion value of 0.70,
indicating good internal consistency (Nunnally, 1978). In addition, item-total correlations
supported the categorization, although three items correlated better with a scale different than
their assigned one. However, the small difference between the correlations and the other
satisfying psychometric results were the basis for not altering the inventory derived from
Study 1. Interscale correlations were moderate, varying from 0.37 to 0.59. This is in line with
the assumption that the scales are all part of the same construct. The correlations did not have
such high values (< 0.80) that one scale should replace two of them (Carron et al., 1985).
Except for Scale 2, Knowing about Ball Actions, values of test-retest reliability led to the
conclusion that the scales, as well as the sum of scales, met the criteria for absolute and
relative reliability. It was remarkable that the average scores on Scale 2 were lower on t2 than
on t1, whereas no such decrease was found on the other three scales. When examining the
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items of Scale 2, we detected that Item 17 (‘I quickly adapt my play to circumstances, such as
rainy or windy weather’) had a very low ICC compared to the other items (ICC = 0.03). An
explanation could be that, between measurements, some players actually had to play a match
in rainy or windy weather and found that they were better or worse in adapting to those
circumstances than they formerly thought. Reliability coefficients of Scale 2 increase when
Item 17 is omitted (ICC = 0.60 instead of 0.53). Besides, the content of this item does not fit
well in the scale. Based on these findings, in combination with the results from Study 2A that
this item correlated higher with Scale 3 than with its assigned Scale 2, Item 17 should be
omitted from the Tactical Skills Inventory for Sports.
Study 3 showed that elite field hockey players scored significantly better on all scales and
on the sum of scales than non-elite field hockey players. The above-mentioned findings
support the construct validity of the questionnaire. The results are in line with those of other
studies showing that skilled players outscore less skilled ones on tactical skills elements
(Williams et al., 1993; Williams and Davids, 1995; Enns and Richards, 1997).
Whether the inventory is measuring the whole concept of tactical skills cannot completely
be ascertained without an accepted reference criterion (inventory). However, this inventory
was constructed with help of expert trainers and embedded in theory. This method of
gathering items can be considered logical validity, also referred to as face validity, and
supports the notion that the inventory is really measuring tactical skills (Thomas and Nelson,
1996). Nevertheless, the results may be influenced by the limitations of the inventory,
requiring self-report. Self-reported measures are susceptible to the individual’s selfconfidence, and, since confidence is associated with elite performance in various sports, this
might have affected the results (Mahoney et al., 1987; Woodman and Hardy, 2003).
Therefore, one could argue that the results of Study 3 for construct validity may have been
influenced by enhanced self-confidence of elite players. However, an alternative hypothesis
might also be true. The elite players have on average over eight years of active field hockey
experience, and they are all part of a talent development program of a field hockey club of
national prestige. This means that they have been confronted frequently with all aspects of
their performance on the field. Trainers, coaches, peers, and parents give feedback on how
fast they are, how well they dribble the ball, and also whether they perform the right action at
the right moment. When players are confronted by (significant) others with their tactical skills
for many years in a row, they ultimately know how good (or bad) they really are. In other
words, regardless of their enhanced confidence, elite players are thought to have a realistic
perspective on their tactical skills. It will be interesting to test this hypothesis.
Caution should be taken in generalizing the results to other populations. This sample
consisted of competitive youth field hockey and soccer players from the Netherlands.
115
Therefore Dutch is the original language in which the Tactical Skills Inventory for Sports was
constructed. So far, the English version of the inventory has not yet been applied and it can
not be assumed straightforwardly that the same results will be obtained. Based on
performance indicators, formal games can be classified in three categories: net and wall
games, invasive games, and striking and fielding games (Read and Edwards, 1992). Field
hockey and soccer are invasive games which fall into the subcategory goal striking games
(Hughes and Barlett, 2002). Research could be directed to populations of competitive sports
athletes in other categories of formal games and in other countries. Moreover it would be
valuable to study the tactical skills from the inventory with other scales than the self-reported
inventory.
In conclusion, the internal consistency, test-retest reliability, and construct validity of the
Tactical Skills Inventory for Sports were acceptable. With the Tactical Skills Inventory for
Sports, which can be used in sports practice, information can be gathered on ‘positioning and
deciding’, ‘knowing about ball actions’, ‘knowing about others’, and ‘acting in changing
situations’.
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Chapter VIII
Discussion and Conclusions
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Development of a field hockey specific test battery
In chapters 2 and 3, attention is paid to how to measure the multidimensional performance
characteristics important for high-performance in youth field hockey players. With a field
hockey test battery the multidimensional performance characteristics can be measured
practically, reliably, and with validity in talented youth field hockey players in a sportsspecific way. This test battery consists of measurement of height, body mass, and percentage
body fat, the Shuttle Sprint and Dribble Test (ShuttleSDT), the Slalom Sprint and Dribble
Test (SlalomSDT), the Interval Shuttle Run Test (ISRT), the ‘Tactics in Sports’ questionnaire,
and the Dutch Youth Version of the ‘Psychological Skills Inventory for Sports’ (PSIS-Youth).
To avoid depending exclusively on the opinion of the trainer to measure tactical skills, it is
recommended to apply the ‘Tactical Skills Inventory for Sports’ in stead of the ‘Tactics in
Sports’ questionnaire in the future (see chapter 7).
8.2
Studies on the relation between multidimensional performance characteristics
and performance level
In chapter 3, a study conducted within a group of all talented youth field hockey players is
presented. The research question to be addressed was: which of the multidimensional
performance characteristics; anthropometric, physiological, technical, tactical and / or
psychological, makes it possible to discriminate between elite and sub-elite youth field hockey
players? Results show that at the age of about fourteen years, an elite player as well as a subelite player has a high level of physiological characteristics, i.e. sprints fast over short
distances, can perform these sprints repeatedly, is agile while sprinting and has a great interval
endurance capacity. An elite player, however, distinguishes him/herself from a sub-elite
player not on these physiological characteristics or on anthropometric characteristics but on
excellent technical, tactical and psychological skills. Tactical skill, i.e., performing the right
action at the right moment seems the most discriminating variable, followed by motivation.
Although sub-elite players score high on motivation, elite players score even higher. This
motivation seems essential for both their current and future performance.
In chapters 4 and 5, the talented players from the study presented in chapter 3, were
followed over time by applying a longitudinal study design. The research question was: how
do elite and sub-elite youth field hockey players develop their multidimensional performance
characteristics across time? Results show that during the phase of talent development, players
improve on all anthropometric, physiological, and technical performance characteristics.
Except for the anthropometric characteristics where the development of elite and sub-elite
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players is similar, elite players improve more rapidly than sub-elite players. With a
longitudinal model for interval endurance capacity, scores on the Interval Shuttle Run Test
can be predicted for elite and sub-elite boys and girls in field hockey in the age-band of 12-19
years. During adolescence both male and female elite youth players have a more promising
development pattern of their interval endurance capacity than sub-elite youth players. A lower
percentage body fat, more hours of additional training, and a greater motivation account for a
more desirable development of the interval endurance capacity. Questionnaires were used as
the measuring instrument for tactical and psychological skills in absence of other valid,
reliable, and practible instruments to measure these skills. To provide an external reference
point, each player was to be compared to top players in their age-category. As a result of the
nature of measuring instrument, however, it was difficult to draw conclusions about the
development of those skills.
The studies on the relation between multidimensional performance characteristics and
level of performance yield a hierarchy in the multidimensional performance characteristics
important for success in field hockey. Tactics, motivation, and slalom dribble performance are
the most important performance characteristics in distinguishing between elite and sub-elite
youth players at the age of about 14 years. This hierarchy is in agreement with the results of a
recent study on talent identification and development of talented water-polo players (Falk et
al., 2004). Elite water-polo players at the age of 14-15 years were superior on most of the
swim tasks, as well as on dribbling and game intelligence. This superiority was maintained
throughout 2 years. The longitudinal data in chapter 4 show that at the age of 15 years and,
again, at the age of 16 years, the performance characteristics from the hierarchy are still
important. However, not only is the gap in test scores between elite and sub-elite players
greater by that time, other performance characteristics as the interval endurance capacity
additionally play a more prominent role in distinguishing between the two performance
groups. Towards excellence, players have to meet increasingly high standards of achievement.
In field hockey, as in most sports, there is a clear necessity for youth players to improve their
performance level across a limited period of time. Different from actualizing for example
musical or intellectual talents into excellence, players only have a relatively short period of
time to perform at the highest level before the aging process causes a decline in their
performance (Rowley, 1995).
The hierarchy in the multidimensional performance characteristics important for success
in field hockey might change with time according to the evolution of the sport. Over historical
time the absolute performance level of sports substantially increases. In some sports, the
world records have improved by around 50% in the last century (Schulz and Curnow, 1988).
The public, media, business and industry attach great importance to world-class performances
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and this leads to growing sophistication of training, equipment, and facilities. In the last
decade in field hockey for example, new stick material, artificial playing surface (water-based
pitches) and the interchange rule made the game faster, increasing and changing the demands
of the players. This development of the sport may also change the relationship between
multidimensional performance characteristics and performance level.
8.3
Additional studies on talented athletes
The studies on the relation between multidimensional performance characteristics and
performance level in talented youth field hockey players show that psychological
characteristics distinguish elite from sub-elite talented youth field hockey players. One of the
most discriminating variables between both performance groups is motivation. Motivation is
not only essential for an optimal performance at a certain moment, i.e. in a match, training, or
test, but throughout the long process of developing a successful sports career. To investigate
whether this finding is specific for field hockey or can be generalized to other sports, a study
to reveal the relationship between psychological skills and performance level within talented
youth athletes in field hockey, basketball, volleyball, speed skating, and swimming was
presented in chapter 6. Results show that, in general, psychological profiles differed between
team sport athletes and individual sport athletes. Psychological characteristics seem more
related to performance level in female than in male athletes. However, for discrimination of
elite and sub-elite youth athletes, motivation and mental preparation were useful indicators
that are independent of gender and type of sport.
In chapter 7, attention was paid to measuring tactical skills. The studies described in
chapters 3 and 4 show that the most discriminating variable between elite youth field hockey
players and sub-elite youth field hockey players is tactical skill. Future elite players seem to
excel in tactical skills by the age of 14 already. However, in these studies tactical skills were
measured by the opinion of the trainers. Although these trainers are experts in the field and
their opinion is highly valued, one might argue that their judgment of a player’s tactical skills
is influenced by their knowledge of that player’s performance level. Therefore, we conducted
a study with the purpose of developing a practical, reliable, and valid self-report instrument to
measure tactical skills in sports. Results show that the Tactical Skills Inventory for Sports is
suitable for measuring tactical skills in youth field hockey players in sports practice.
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8.4
Theoretical considerations
The present thesis contributes to a clearer understanding of the relation between (the
development of) multidimensional performance characteristics and the performance level in
talented youth field hockey players, and is a relevant step in unraveling the mechanisms of
how one achieves greatness in sports. However, the definition of talent used in the present
thesis is still vague: what exactly does it mean when a player is ‘better than peers during
training and competition’ and how can we measure ‘the potential to become an elite performer
in the future’? We used the accumulated know-how of field hockey experts to select the
participants for this study. Players were considered talented in cases where they were part of a
talent development program of a field hockey club of national prestige, and were playing at
the highest level for their age category. The distinction between elite and sub-elite youth
players was made on the basis of players additionally being part of a youth selection team of
the Dutch Field Hockey Association (KNHB) or not. Consequently, the present thesis only
gives insight into the process of talent development of already identified talented players; but
what if not the most talented youth players were detected and identified? Field hockey, as all
team sports, is characterized by a lack of objective performance measurements, making it hard
to decide which player is the superior one. Although several trainers and coaches claim to be
able to ‘recognize a talented player when they see one’ it would be valuable to measure more
objectively what exactly it is that they ‘see’.
In the present thesis elite youth players were compared to sub-elite youth players
revealing information on the performance characteristics that are important for current and
future success in field hockey. Despite that, this thesis does not specify exactly the underlying
processes that enable players to, for example, perform the right action at the right moment and
future research on this topic is highly recommended. In addition, with the exception of the
interval endurance capacity, no mechanisms underlying the development of multidimensional
performance characteristics have been studied yet.
8.5
Nature-nurture controversy
Although not included in this present thesis, the environment of talented players must not be
underestimated. When a talented youth player attempts to develop his or her talent to reach
elite status, this has major consequences for lifestyle. The process is long, averaging at least
10 to 12 years, and during this interval, significant others, particularly parents and coaches,
play an important role (Côté, 1999; Visscher et al., 2004). Bloom (1985) also stressed the role
of the environment by indicating that the development of exceptional talents requires family
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support, excellent teaching, and appropriate motivational reinforcement at any stage of their
development. Because the present thesis focuses on individual performance characteristics
and not on the environment, it is hard to draw conclusions on the real determinants of expert
performance. Nevertheless, the results show that for the attainment of expert performance the
undertaking of extensive amounts of practice is essential, which is in line with other research
(Janelle and Hillman, 2003). Experts simply do not become experts without an enormous
investment in training. The stars of tomorrow are the talented players of today and they have a
long way to go to the top.
Howe et al. (1998) suggest that differences in early experiences, preferences,
opportunities, habits, training, and practice are the real determinants of excellence. The
deliberate practice theory of expert performance also takes the perspective that it is practice
and experience rather than innate talent that is the real determinant of expert performance
(Ericsson, 1998; 2003a; 2003b). This perspective is one that gains support from those
advocating environmental determinants of exceptional performance (e.g., Sloboda et al.,
1994a, 1994b; Howe, 2001) but it is at odds with the perspectives advanced by behavioral
geneticists (e.g., Rowe, 1998).
Abernethy and colleagues (2003) critique the deliberate practice framework. They argue
that genetic factors play a critical role in determining the limits to the impact of training and
therefore the ultimate levels to performance in many sports (Singer and Janelle, 1999;
Skinner, 2001). In support of this hypothesis, a longitudinal study on growth and development
of young gymnasts, swimmers, soccer and tennis players, showed that continued success in
sport of young athletes appeared to be related to inherited traits (Baxter-Jones et al., 1995).
Further research with the field hockey players in the present thesis might give advancing
insight in the determinants of excellence.
8.6
Recommendations for future research
Although this present thesis revealed performance characteristics that can distinguish elite
youth field hockey players from sub-elite youth field hockey players, it is still unclear to what
extent and how these performance characteristics can be trained. If more insight can be given
into this question, sports in general and field hockey in particular can benefit enormously.
Without detracting from the current results, this study will greatly increase in value when the
talented field hockey players are followed until adulthood and some of them actually reach
expert status. This study is a first step in bridging the gap between science and sports practice.
The next important step to take is to specify the underlying processes of the multidimensional
performance characteristics, to measure these in training and competition, and to evaluate
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current training programs. Furthermore, when necessary, to develop, implement, and evaluate
new training programs with the goal of improving the multidimensional performance
characteristics of talented field hockey players. Examples are field hockey-specific programs
for tactical and perceptual training.
8.7
Conclusions and implications for field hockey
The aim of the thesis was addressed by conducting research within a group of all talented field
hockey players, measuring multidimensional performance characteristics in a sports-specific
way, and following talented players across time by adopting a longitudinal study design. With
caution because the talented players from this study have not yet reached expert performance
in adulthood, and with acknowledging the limitations of this study, it is concluded that a
talented field hockey player with the greatest chance of succeeding is a player with a
relatively high level of performance in field hockey specific physiological characteristics,
excellent technical skills, excellent tactical skills, and a very high motivation at the age of
fourteen already. This, however, is not enough. A player also has to have potential to reach
elite status in the future. Elite players need less time to develop better performance
characteristics, meaning that a talented player has to increase his or her performance
characteristics at a relatively fast pace for many years in a row. To sustain the long road to the
top, investing enormous amounts of time preparing for the international sporting arena, again
motivation is essential. From this thesis, relevant information for trainers, coaches, scouts,
players, parents and other field hockey enthusiastics can be given:
-
Acknowledge the multidimensional nature of a field hockey performance: a talented
player is more than a technically gifted player.
-
Motivation plays an essential role in the development of a successful career in field
hockey.
-
Technical and especially tactical skills have to be excellent if a player is to succeed.
-
In addition, a talented player needs a relatively high level of field hockey specific
physiological characteristics: sprinting fast over short distances, perform these sprints
repeatedly, is agile while sprinting, and has a great interval endurance capacity.
For each talented player, it is recommended to construct a performance profile repeatedly, i.e.
every year, during the entire process of talent development. In this way, the player’s level of
performance characteristics can be compared to other talented peers. Even more, his or her
development of the performance characteristics across time can be recorded. This
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performance profile can be constructed by measuring the multidimensional performance
characteristics with the field hockey test battery from this study. In Vakblad Hockey, an
electronic journal of the Dutch Field Hockey Association, reference data of the
multidimensional performance characteristics of talented boys and girls under 14 years, under
16 years, and under 18 years are published (Elferink-Gemser et al., 2004a; 2004b). By
comparing the test results of each player with these reference data, it is possible to
acknowledge strong and weaker performance characteristics, and, consequently, use this
information in training.
129
References
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general theory of expert perceptual-motor performance. A critique of the deliberate practice
framework. In Expert performance in sports: Advances in research on sport expertise (edited by
J. L. Starkes and K. A. Ericsson), pp. 349-369. Champaign, IL: Human Kinetics.
Baxter-Jones, A.D.G., Helms, P., Muffulli, N., Baines-Preece, J., and Preece, M. (1995). Growth and
development of male gymnasts, swimmers, soccer and tennis players: a longitudinal study. Annals
of Human Biology, 22, 381-395.
Bloom, B.S. (1985). Developing talent in young people. New York: Ballantine.
Côté, J. (1999). The influence of the family in the development of talent in sport. The Sport
Psychologist, 13, 395-417.
Elferink-Gemser, M.T., Visscher, C., and Lemmink, K.A.P.M. (2004a). Prestatieprofielen van jeugdig
getalenteerde hockeyers: deel 1. [Performance profiles of talented youth field hockey players: part
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Elferink-Gemser, M.T., Visscher, C., and Lemmink, K.A.P.M. (2004b). Prestatieprofielen van jeugdig
getalenteerde hockeyers: deel 2. [Performance profiles of talented youth field hockey players: part
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optimal learning and creativity. High Ability Studies, 9, 75-100.
Ericsson, K.A. (2003a). Development of elite performance and deliberate practice. An update from the
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Ericsson, K.A. (2003b). How the expert performance approach differs from traditional approaches to
expertise in sport. In Expert performance in sports: Advances in research on sport expertise
(edited by J.L. Starkes and K.A. Ericsson), pp. 370-402. Champaign, IL: Human Kinetics.
Falk, B., Lidor, R., Lander, Y., and Lang, B. (2004). Talent identification and early development of
elite water-polo players: a 2-year follow-up study. Journal of Sports Sciences, 22, 347-355.
Howe, M.J.A. (2001). Genius explained. Cambridge, UK: Cambridge University Press.
Howe, M.J.A., Davidson, J.W., and Sloboda, J.A. (1998). Innate talents: Reality or myth? Behavioral
and Brain Sciences, 21, 399-442.
Janelle, C.M. and Hillman, C.H. (2003). Expert performance in sport: Current perspectives and current
issues. In Expert performance in sports: Advances in research on sport expertise (edited by J.L.
Starkes and K.A. Ericsson), pp. 19-47. Champaign, IL: Human Kinetics.
Rowe, D.C. (1998). Talent scouts, not practice scouts: Talents are real. Behavioral and Brain Sciences,
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Rowley, S. (1995). Identification and development of talent in young athletes. In Actualizing talent: A
lifelong challenge (edited by J. Freeman, P. Span, and H. Wagner), pp. 128-143. London: Cassell.
Schulz, R. and Curnow, C. (1988). Peak performance and age among super athletes: Track and field,
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Visscher, C., Elferink-Gemser, M.T., and Lemmink, K.A.P.M. (2004). The role of parental support in
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Chapter
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IV
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VI
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131
Summary
On which performance characteristics distinguish elite youth field hockey players themselves
from sub-elite youth field hockey players? The goal of this thesis is to gain an understanding
of the relation between (the development of) multidimensional performance characteristics
and the performance level in talented youth field hockey players. This goal was addressed by
conducting research within a group of all talented field hockey players, measuring
multidimensional performance characteristics in a sports-specific way, and following talented
players across time by adopting a longitudinal study design.
In order to measure performance characteristics, tests had to be developed. In chapter 2,
the reliability of two field hockey specific sprint and dribble tests was evaluated: the Shuttle
Sprint and Dribble Test (ShuttleSDT) and the Slalom Sprint and Dribble Test (SlalomSDT).
The shuttle sprint and dribble performances of 22 young male and 12 young female field
hockey players were assessed on two occasions within 4 weeks. Twenty one young female
field hockey players took part in the slalom sprint and dribble test twice in a 4 week period.
The ShuttleSDT requires the players to perform three 30 m shuttle sprints while carrying a
hockey stick alternated with shorts periods of rest and, after a 5 minute rest, three 30 m shuttle
sprints alternated with rest while dribbling a hockey ball. The SlalomSDT requires the players
to run a slalom course and, after a 5 minute rest, to dribble the same slalom with a hockey
ball. It was concluded that the ShuttleSDT and the SlalomSDT are reliable measures of sprint
and dribble performances of young field hockey players.
To determine the relation between multidimensional performance characteristics and level
of performance in talented youth field hockey players, in chapter 3 elite youth players (n = 38,
mean age 13.21, sd = 1.26) were compared with sub-elite youth players (n = 88, mean age
14.17, sd = 1.26) on anthropometric, physiological, technical, tactical and psychological
characteristics. Multivariate analyses with factors of performance level and gender, and with
age as a covariate, showed that the elite youth players scored better than the sub-elite youth
players on technical (dribble performance in a peak and repeated shuttle run), tactical (general
tactics; tactics for possession and non-possession of the ball) and psychological variables
(motivation) (p < 0.05). The most discriminating variables were tactics for possession of the
ball, motivation and performance in a slalom dribble. Thereby, age discriminated between
both performance groups, indicating that the elite youth players were younger than the subelite players
To reveal performance characteristics, which may have power for predicting future elite
field hockey players, in chapter 4 we made a comparison between 30 elite and 35 sub-elite
youth players in terms of anthropometric, physiological, technical, tactical and psychological
characteristics measured on three occasions, each separated by a time interval of one year.
Mean age of the players on the first measurement was 14.2 years (sd = 1.1). Repeated
134
measures analyses of covariance with factors of performance level and measurement, and with
age as a covariate, showed that the elite players scored better than the sub-elite players on
technical and tactical variables. Female elite youth players also scored better on interval
endurance capacity, motivation and confidence. Future elite players seem to excel in tactical
skills by the age of 14 already. They also stand out in specific technical skills and develop
these together with the interval endurance capacity better than sub-elite youth players in the
two subsequent years.
Chapter 5 describes a study with the goal to better understand the mechanisms that
underlie the development of the interval endurance capacity in talented youth field hockey
players in the 12-19 age band. In a period of three years 393 measurements were taken and
longitudinal changes in interval endurance capacity were investigated using the multilevel
modelling program MlwiN. A polynomial model of order 2 with gender-specific intercepts,
linear terms and level 1 variances adequately represents the variance structure of the data.
Differential effects of sub-elite for age and gender significantly improved the model. During
adolescence both male and female elite youth players have a more promising development
pattern of their interval endurance capacity than sub-elite youth players. Explaining variables
besides age are percentage body fat, additional training, and motivation.
From the previous studies it becomes clear that psychological skills are important in
distinguishing elite and sub-elite youth field hockey players. To find out if this finding is
specific for field hockey players or can be generalized to other sports, in Chapter 6 attention is
paid to psychological skills of talented athletes in field hockey, basketball, volleyball, speed
skating, and swimming. To reveal the relationship between psychological skills and
performance level within a talent group, 458 talented youth athletes (mean age 14.80, sd =
1.52) filled in the Dutch Youth Version of the Psychological Skills Inventory for Sports with
scales for motivation, confidence, anxiety control, mental preparation, team emphasis, and
concentration. A performance level (elite versus sub-elite) by type of sport (team sports versus
individual sports) by gender multivariate analysis of covariance (2 X 2 X 2) with age as a
covariate resulted in significant effects. Psychological skills distinguished between more and
less successful talented athletes, especially in females. In general, psychological profiles
differed between males and females and between team sport athletes and individual sport
athletes. However, for discrimination of elite and sub-elite youth athletes, motivation and
mental preparation were useful indicators that are independent of gender and type of sport.
The most discriminating variable between elite and sub-elite youth field hockey players is
tactical skill. However, in the previous studies tactical skills were measured by the opinion of
the trainer. To avoid depending solely on the trainer, purpose of the study described in chapter
7 was to develop a practical, reliable, and valid instrument to measure tactical skills in sports
135
directly by the player. Nineteen trainers and 415 competitive youth field hockey and soccer
players (mean age 15.9, sd = 1.6; 283 boys and 132 girls) selected by their age, gender, and
performance level participated. With the trainers, 34 questions were formulated involving
tactical skills. Factor analysis resulted in the Tactical Skills Inventory for Sports (TACSIS).
Scales were labeled Positioning and Deciding, Knowing about Ball Actions, Knowing about
Others, and Acting in Changing Situations, covering all aspects of tactical skills regarding
declarative versus procedural knowledge, and attack and defense. Internal consistency and
test-retest measures for reliability (except Knowing about Ball Actions) were within
acceptable limits. Elite players scored better than non-elite players, supporting construct
validity. In conclusion, the TACSIS is suitable for measuring tactical skills in youth field
hockey and soccer players in sports practice.
Based on the results, it is concluded that this thesis contributes to a clearer understanding
of the relation between (the development of) multidimensional performance characteristics
and the performance level in talented youth field hockey players, and is a relevant step in
unraveling the mechanisms of how one achieves greatness in sports. However, the definition
of talent used in the present thesis is still vague: what exactly does it mean when a player is
‘better than peers during training and competition’ and how can we measure ‘the potential to
become an elite performer in the future’? With caution because the talented players from this
study have not yet reached expert performance in adulthood, and with acknowledging the
limitations of this study, it is concluded that a talented field hockey player with the greatest
chance of succeeding is a player with a relatively high level of performance in field hockey
specific physiological characteristics, excellent technical skills, excellent tactical skills, and a
very high motivation at the age of fourteen already. This, however, is not enough. A player
also has to have potential to reach elite status in the future. Elite players need less time to
develop better performance characteristics, meaning that a talented player has to increase his
or her performance characteristics at a relatively fast pace for many years in a row. To sustain
the long road to the top, investing enormous amounts of time preparing for the international
sporting arena, again motivation is essential. In conclusion, an elite player distinguishes
him/herself from a sub-elite player not by physiological or anthropometric characteristics but
by excellent technical, tactical and psychological skills. In the guidance of young talented
players to the top more attention has to be paid to these skills.
136
Samenvatting
Nederland heeft een lange hockeyhistorie en is één van de toonaangevende landen als het om
tophockey gaat. De meeste tophockeyers zijn begonnen met hun sport toen ze 7 jaar oud
waren en allemaal hebben ze veel tijd en energie geïnvesteerd in hun hockeyloopbaan voordat
ze de top bereikten. Huidige jeugdspelers hebben dan ook een lange weg te gaan naar de top.
De meeste tophockeyclubs hebben een jeugdopleiding om de prestatiebepalende kwaliteiten
van getalenteerde spelers vanaf ongeveer 12 jaar verder te ontwikkelen. In dit onderzoek
wordt een getalenteerde hockeyer gedefinieerd als een hockeyer die beter presteert dan
leeftijdsgenoten en bovendien de potentie heeft om de top te halen. De beste jeugdhockeyers,
in dit onderzoek de jeugdige toppers genoemd, spelen niet alleen in de jeugdopleiding van hun
club maar ook in een districts- of nationale jeugdselectie van de Koninklijke Nederlandse
Hockey Bond (KNHB). Jeugdige subtoppers hockeyen daarentegen alleen in de
jeugdopleiding van hun eigen club. Zowel jeugdige toppers als subtoppers spelen met hun
team op het hoogste nationale niveau van hun leeftijdscategorie. Van alle talenten zullen
uiteindelijk maar weinig daadwerkelijk in staat zijn om ook bij de senioren het hoogste niveau
te halen. Relevante vragen zijn: wat zijn kenmerken van getalenteerde hockeyers? Wie haalt
de top en wie niet? Op welke prestatiebepalende kwaliteiten onderscheiden jeugdige toppers
zich van jeugdige subtoppers? Het doel van het huidige onderzoek is het geven van meer
inzicht in de relatie tussen (de ontwikkeling van) prestatiebepalende kwaliteiten en het
prestatieniveau bij jeugdige getalenteerde hockeyers. Het gaat dus om het krijgen van meer
inzicht in de kwaliteiten die getalenteerde hockeyers moeten bezitten en ontwikkelen om door
te kunnen groeien naar de top. De multidimensionele prestatiebepalende kwaliteiten zijn de
kwaliteiten die de hockeyprestatie bepalen: de antropometrische eigenschappen (lengte,
gewicht, vetpercentage), de fysiologische kwaliteiten (maximale shuttle sprint, herhaalde
shuttle sprint, slalom sprint en interval uithoudingsvermogen), de technische kwaliteiten
(maximale shuttle dribbel, herhaalde shuttle dribbel en slalom dribbel), de tactische
kwaliteiten (algemene tactiek, tactiek bij balbezit en tactiek bij niet-balbezit) en de mentale
kwaliteiten (motivatie, zelfvertrouwen, angstcontrole, mentale voorbereiding, teamoriëntatie
en concentratie). Om het bovengenoemde doel te bereiken, zijn de prestatiebepalende
kwaliteiten op een sportspecifieke manier gemeten binnen een groep getalenteerde hockeyers
in de leeftijd van 12-18 jaar. Tevens zijn de talenten gevolgd in de tijd door middel van een
longitudinaal onderzoeksdesign.
Voor het meten van prestatiebepalende kwaliteiten zijn valide en betrouwbare
testmethoden nodig. In hoofdstuk 2 wordt de ontwikkeling van twee hockeyspecifieke sprinten dribbel tests beschreven: de Shuttle Sprint en Dribbel Test (ShuttleSDT) en de Slalom
Sprint en Dribbel Test (SlalomSDT). Om de betrouwbaarheid van de tests te bepalen, hebben
34 jeugdige hockeyers (12 meisjes en 22 jongens; gemiddelde leeftijd 14.9 jaar,
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standaarddeviatie 1.5) twee keer deelgenomen aan de ShuttleSDT. Aan de SlalomSDT hebben
21 hockeymeisjes twee keer deelgenomen. De conclusie is dat de ShuttleSDT en de
SlalomSDT betrouwbare testen zijn voor het meten van de sprint en dribbel kwaliteiten van
jeugdige hockeyers.
In hoofdstuk 3 is de relatie tussen de multidimensionele prestatiebepalende kwaliteiten en
het prestatieniveau bij getalenteerde hockeyers nader bestudeerd. Allereerst is de testbatterij
beschreven waarmee de verschillende prestatiebepalende kwaliteiten gemeten kunnen worden.
De testbatterij bestaat uit bepaling van de lengte, het gewicht en het vetpercentage, de
ShuttleSDT, de SlalomSDT, de Interval Shuttle Run Test (ISRT), de ‘Tactiek in Sport’
vragenlijst en de Nederlandstalige jeugdversie van de Psychological Skills Inventory for
Sports (PSIS-jeugd). Er is een vergelijking gemaakt tussen 38 jeugdige toppers (17 meisjes en
21 jongens; gemiddelde leeftijd 13.2 jaar, standaarddeviatie 1.3) en 88 jeugdige subtoppers
(46 meisjes en 42 jongens; gemiddelde leeftijd 14.2 jaar, standaarddeviatie 1.3) voor wat
betreft hun antropometrische, fysiologische, technische, tactische en mentale kwaliteiten. Een
multivariate analyse met prestatieniveau (toppers versus subtoppers) en geslacht als factoren
en met leeftijd als covariaat laat zien dat jeugdige toppers beter scoren dan jeugdige
subtoppers op technische (maximale en herhaalde shuttle dribbel), tactische (algemene tactiek,
tactiek bij balbezit en tactiek bij niet-balbezit) en mentale kwaliteiten (motivatie). Uit een
discriminant analyse blijken tactiek bij balbezit, motivatie en slalom dribbel de meest
discriminerende variabelen tussen jeugdige toppers en subtoppers. Omdat de toppers jonger
waren dan de subtoppers maakt leeftijd eveneens onderscheid tussen beide groepen.
Om de prestatiebepalende kwaliteiten te achterhalen welke wellicht toekomstig
hockeysucces kunnen voorspellen, zijn de getalenteerde hockeyers in de tijd gevolgd. In
hoofdstuk 4 is een vergelijking gemaakt tussen 30 jeugdige toppers (15 meisjes en 15 jongens;
gemiddelde leeftijd op het eerste meetmoment 13.9 jaar, standaarddeviatie 1.0) en 35 jeugdige
subtoppers (18 meisjes en 17 jongens; gemiddelde leeftijd op het eerste meetmoment 14.4
jaar, standaarddeviatie 1.2) voor wat betreft hun antropometrische, fysiologische, technische,
tactische en mentale kwaliteiten. Er zijn metingen verricht gedurende drie wedstrijdseizoenen
en er is een multivariate analyse met herhaalde metingen uitgevoerd voor jongens en meisjes
afzonderlijk met prestatieniveau (toppers versus subtoppers) en meetmoment (t1 versus t2
versus t3) als factoren en met leeftijd als covariaat. Deze wijst uit dat jeugdige toppers beter
presteerden op technische en tactische tests dan jeugdige subtoppers. Bij de meisjes scoorden
de toppers daarnaast beter dan de subtoppers op het interval uithoudingsvermogen, de
motivatie en het zelfvertrouwen. Bij zowel de jongens als de meisjes lijken toekomstige
tophockeyers al op veertienjarige leeftijd uit te blinken in tactiek. Ze vallen tevens op door
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hun uitstekende techniek en bovendien verbeteren ze hun prestatiebepalende kwaliteiten beter
dan subtoppers.
Uit de resultaten van hoofdstuk 4 blijken jeugdige toppers onder meer hun interval
uithoudingsvermogen beter te ontwikkelen dan jeugdige subtoppers in de leeftijd van 14 tot
16 jaar. In hoofdstuk 5 wordt nader ingegaan op de achterliggende mechanismen in de
ontwikkeling hiervan. Met behulp van een multilevel analyse zijn ontwikkelingscurven voor
jeugdige toppers en subtoppers in de leeftijdscategorie van 12 tot 19 jaar tot stand gekomen.
Deze curven zijn gemaakt voor zowel jongens als voor meisjes. Het interval
uithoudingsvermogen kan daarmee aan de hand van leeftijd, vetpercentage, extra
trainingsuren en motivatie voorspeld worden. Tijdens de adolescentie laten, zowel bij de
jongens als bij de meisjes, de toppers een positievere ontwikkeling van hun interval
uithoudingsvermogen zien dan de subtoppers.
Uit hoofdstuk 3 en 4 kan geconcludeerd worden dat mentale kwaliteiten een belangrijk
verschil vormen tussen jeugdige hockeytoppers en -subtoppers. Om te achterhalen of deze
bevinding hockeyspecifiek is of gegeneraliseerd kan worden naar meerdere sporten, wordt in
hoofdstuk 6 dieper ingegaan op de mentale kwaliteiten van jeugdige getalenteerde hockeyers,
basketballers, volleyballers, schaatsers en zwemmers. Om inzicht te krijgen in de relatie
tussen mentale kwaliteiten en het prestatieniveau binnen een talentengroep, hebben 458
getalenteerde jeugdige sporters (gemiddelde leeftijd 14.8 jaar, standaarddeviatie 1.5) de PSISjeugd ingevuld. Deze vragenlijst bevat schalen voor motivatie, zelfvertrouwen, angstcontrole,
mentale voorbereiding, teamoriëntatie en concentratie. Een multivariate analyse met
prestatieniveau (toppers versus subtoppers), geslacht en type sport (teamsport versus
individuele sport) als factoren en met leeftijd als covariaat resulteert in significante effecten.
In het algemeen is het mentale profiel van jongens anders dan dat van meisjes en het mentale
profiel van teamsporters anders dan dat van individuele sporters. Desalniettemin maken
mentale kwaliteiten onderscheid tussen meer en minder succesvolle sporters, vooral bij
meisjes. Op motivatie en mentale voorbereiding scoren jeugdige toppers beter dan subtoppers.
Deze mentale kwaliteiten zijn, ongeacht geslacht of type sport, goede indicatoren voor het
onderscheid tussen jeugdige toppers en subtoppers.
De meest discriminerende kwaliteit tussen jeugdige hockeytoppers en –subtoppers is
tactiek. In hoofdstuk 3 en 4 is tactiek gemeten aan de hand van het oordeel van de trainer. Dit
oordeel is echter mogelijk beïnvloed door het prestatieniveau van de speler. Om deze
beïnvloeding te ondervangen, wordt in hoofstuk 7 de ontwikkeling beschreven van een
praktisch toepasbaar, betrouwbaar en valide meetinstrument om tactiek te meten. In
samenwerking met 19 trainers is een vragenlijst met 34 vragen betreffende tactiek opgesteld.
Nadat 415 jeugdige wedstrijdhockeyers en –voetballers (283 jongens en 132 meisjes;
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gemiddelde leeftijd 15.9, standaarddeviatie 1.6) deze vragenlijst hebben ingevuld, is een
factor analyse uitgevoerd. Dit resulteerde in de Tactical Skills Inventory for Sports (TACSIS),
de vragenlijst voor tactische vaardigheden van sporters. De vragenlijst bevat vier schalen:
‘Positie kiezen en besluitvorming’, ‘Inzicht in acties met de bal’, ‘Inzicht in anderen’ en
‘Omgaan met veranderingen’. Deze schalen bevatten alle aspecten van tactiek voor wat betreft
het onderscheid tussen declaratieve kennis (‘weten wat je moet doen’) en procedurele kennis
(‘het doen’) en het onderscheid tussen aanval en verdediging. Interne consistentie en testhertest betrouwbaarheid (behalve de schaal ‘Inzicht in acties met de bal’) zijn acceptabel tot
goed. De construct validiteit wordt ondersteund door de bevinding dat jeugdige toppers hoger
scoorden dan jeugdige subtoppers. De conclusie was dat de TACSIS geschikt is om in de
praktijk tactische vaardigheden te meten bij jeugdige hockeyers en voetballers.
Op basis van de resultaten wordt geconcludeerd dat dit onderzoek meer inzicht geeft in de
relatie tussen de (ontwikkeling van) multidimensionele prestatiebepalende kwaliteiten en het
prestatieniveau bij jeugdig getalenteerde hockeyers en een relevante stap is in het ontrafelen
van het mysterieuze begrip talent. Er zijn echter nog veel onduidelijkheden. Zo is bijvoorbeeld
de definitie van ‘talent’ nog steeds vaag en verdient het de aanbeveling om de onderliggende
prestatiebepalende kwaliteiten nader te bestuderen. Voorzichtig, omdat de talenten de top nog
niet gehaald hebben en met inachtneming van de beperkingen van het onderzoek, wordt
geconcludeerd dat een getalenteerde hockeyer de grootste kans heeft om te slagen als hij of zij
al op veertienjarige leeftijd een hoog niveau heeft van hockeyspecifieke fysiologische
kwaliteiten, een uitmuntende techniek heeft en vooral een uitmuntende tactiek combineert met
een zeer hoge motivatie. Dit is echter nog niet genoeg. Een speler moet ook de potentie
hebben om de top te halen. Omdat jeugdige toppers in vergelijking met jeugdige subtoppers
minder tijd nodig hebben om betere prestatiebepalende kwaliteiten te ontwikkelen, betekent
dit dat een talent zijn of haar kwaliteiten gedurende vele jaren in een relatief hoog tempo moet
ontwikkelen. Om de lange weg naar de top vol te kunnen houden is motivatie wederom
essentieel. Kortom, een jeugdige tophockeyer onderscheidt zich van een jeugdige subtopper
niet zozeer door antropometrische of fysiologische kwaliteiten, maar juist door een
uitstekende techniek, tactiek en mentale kwaliteiten. Binnen talentontwikkeling zou vooral
aandacht moeten worden besteed aan deze kwaliteiten.
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Voor trainers, coaches, spelers, ouders en andere hockey enthousiastelingen biedt dit
onderzoek relevante aanknopingspunten:
-
Houd rekening met het multidimensionele karakter van hockey: een talent is meer
dan een technisch goede speler.
-
Motivatie speelt een essentiële rol in de ontwikkeling van een succesvolle
hockeyloopbaan.
-
Om de top te kunnen halen, moeten de techniek en vooral de tactiek uitmuntend
zijn.
-
Bovendien moet een getalenteerde speler over een relatief hoog basisniveau van
hockeyspecifieke fysiologische kwaliteiten beschikken, wat wil zeggen dat hij of
zij snel moet kunnen sprinten over korte afstanden, de sprints herhaaldelijk moet
kunnen
uitvoeren,
wendbaar
moet
zijn
en
een
zeer
goed
interval
uithoudingsvermogen moet hebben.
Er wordt aanbevolen om tijdens het hele proces van talentontwikkeling regelmatig,
bijvoorbeeld ieder jaar, een prestatieprofiel op te stellen van jeugdige hockeyers. Op deze
manier kan het niveau van de prestatiebepalende kwaliteiten per speler worden vergeleken
met andere getalenteerde hockeyers. Bovendien kan zijn of haar ontwikkeling van deze
kwaliteiten in kaart gebracht worden en deze informatie kan toegepast worden in de
trainingen. Voor het opstellen van het prestatieprofiel kan gebruik gemaakt worden van de
testbatterij zoals die voor dit onderzoek ontwikkeld is. In Vakblad Hockey is de testbatterij
beschreven en worden de prestatieprofielen van getalenteerde jongens en meisjes onder 14,
onder 16 en onder 18 jaar gepresenteerd (Elferink-Gemser et al., 2004a; 2004b).
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List of publications
Peer-reviewed articles
Elferink-Gemser, M.T., Visscher, C., Lemmink, K.A.P.M., Mulder, Th. (2004). Relation
between multidimensional performance characteristics and level of performance in
talented youth field hockey players. Journal of Sports Sciences, 22, 1053-1063.
Elferink-Gemser, M.T., Visscher, C., Richart, H., Lemmink, K.A.P.M. (2004). Development
of the Tactical Skills Inventory for Sports. Perceptual and Motor Skills, 99, 883-895.
Lemmink, K.A.P.M., Elferink-Gemser, M.T., en Visscher, C. (2004). Evaluation of the
reliability of two field hockey-specific sprint and dribble tests in young field hockey
players. British Journal of Sports Medicine, 38, 138-142.
Chapters in books
Visscher, C., Elferink-Gemser, M.T. en Lemmink, K.A.P.M. (2004). The role of parental
support in sports success of talented young Dutch athletes. In: M. Coelho e Silva en R.M.
Malina (eds). Children and Youth in Organized Sports. Coimbra University Press,
Portugal, 123-135.
Proceedings
Elferink-Gemser, M.T., Visscher, C., Lemmink, K.A.P.M. (2002). Relation between
predictors and performance level in talented young field hockey players. In: Proceedings
of the 7th annual congress of the European College of Sport Science, Athens, Greece,
619.
Elferink-Gemser, M.T., Visscher, C., Lemmink, K.A.P.M. (2003). Multidimensional
performance characteristics in talented youth field hockey players – a longitudinal study.
In: Proceedings of the 8th annual congress of the European College of Sport Science,
Salzburg, Austria, 161.
Elferink-Gemser, M.T., Visscher, C., Lemmink, K.A.P.M. (2004). Are today’s young top
players the stars of tomorrow? In: Proceedings of the Young Researcher Seminar,
University of Innsbruck, Austria. Young Researcher Award for best oral presentation.
Lemmink, K.A.P.M., Elferink-Gemser, M.T., Visscher, C. (2003). Interval sprint and interval
endurance capacity of young soccer players. In: Proceedings of the 8th annual congress
of the European College of Sport Science, Salzburg, Austria, 226.
Visscher, C., Elferink-Gemser, M.T., Lemmink, K.A.P.M. (2003). The role of parental
support in sports success of talented young Dutch athletes. In: Proceedings of the 8th
annual congress of the European College of Sport Science, Salzburg, Austria, 143.
144
Nederlandse vakbladen en rapporten NOC*NSF
Elferink-Gemser, M.T. (2004). Talent ontrafeld? Onderzoek Rijksuniversiteit Groningen
afgerond. Hartsticke Bosch, 5, 16-17.
Elferink-Gemser, M.T., Visscher, C., Lemmink, K.A.P.M. (2004). Prestatieprofielen van
jeugdig getalenteerde hockeyers: deel 1. Vakblad Hockey.
Elferink-Gemser, M.T., Visscher, C., Lemmink, K.A.P.M. (2004). Prestatieprofielen van
jeugdig getalenteerde hockeyers: deel 2. Vakblad Hockey.
Elferink-Gemser, M.T., Visscher, C., Lemmink, K.A.P.M. (2005). De weg naar de top:
kwestie van mentaliteit? Mentale kwaliteiten van getalenteerde sporters in de leeftijd van
12-18 jaar. Een onderzoek bij voetballers, hockeyers, volleyballers, basketballers,
schaatsers en zwemmers. NOC*NSF. Sector Topsport/publ./in press.
Elferink-Gemser, M.T., Knoop, J., Lemmink, K.A.P.M., Visscher, C. (2004). Topvoetbal:
kwestie van mentaliteit? De VoetbalTrainer, 125, 40-41.
Visscher, C., Gemser, M.T. en De Greef, M. (1996a). Jeugdige topsporters. Invloed van
ouders en onderwijs. Richting Sportgericht, 5, 263-268.
Visscher, C., Gemser, M.T. en De Greef, M. (1996b). Jeugdige topsporters. Invloed van
ouders en onderwijs (2). Richting Sportgericht, 6, 329-332.
Visscher, C., Gemser, M.T. en De Greef, M. (1997). Talentontwikkeling. De invloed van
ouders en onderwijs. NOC*NSF. Sector Topsport/publ./TO 005.
Visscher, C., Bakema, R., Elferink-Gemser, M.T. en Lemmink, K.A.P.M. (2003). Uitval
binnen het jeugdturnen. Sportgericht, 3, 4-8.
145
Dankwoord
Mijn proefschrift is af! DANK DANK DANK aan een ieder die me daarbij geholpen heeft (en
dat zijn er een heleboel!). Maar…. ik ben nog lang niet klaar….
Ik weet niet meer precies wat ik verwachtte toen ik vijf jaar geleden aan dit talentonderzoek
begon, maar wat ik nog wel weet is dat ik in de veronderstelling leefde dat ik antwoorden op
vragen zou kunnen geven. Dat als het moment van promoveren was aangebroken ik toch
zeker wel één of misschien zelfs wel twee vragen minder had over hoe het eigenlijk zit als
sportieve talenten de top willen halen. Niets is echter minder waar; in ruil voor iets meer
inzicht in de onvoorstelbare complexiteit van de ontwikkeling van een succesvolle
sportloopbaan zijn er legio nieuwe vragen bij gekomen. Dit voortschrijdende inzicht heb ik
vooral en in eerste instantie te danken aan de projectleider van het talentonderzoek, Chris
Visscher. Chris, je creativiteit ligt ten grondslag aan alles wat dit project voorstelt. Je bent
echter veel meer voor mij dan alleen mijn directe begeleider. Jij hebt als geen ander oog voor
dingen die echt belangrijk zijn in het leven. Soms schieten woorden tekort, maar ik denk dat je
wel weet wat ik bedoel.
Het fundament van het sportonderzoek bij Bewegingswetenschappen wordt met Chris
gevormd door Koen Lemmink. Samen vormen jullie een hecht team en ik ben blij dat ik door
jullie allebei begeleid ben. Van jou, Chris, moest een artikel vaak weer helemaal over de kop
omdat de rode lijn er toch nog niet goed genoeg inzat, terwijl jij, Koen, veel meer de ‘puntjes
op de ‘i’ zette. Van jou heb ik enorm veel geleerd over het schrijven van een wetenschappelijk
artikel. Maar waar ik jullie vooral voor wil bedanken is de sfeer waarin we samenwerken;
altijd doelmatig maar vooral altijd gezellig. Natuurlijk wil ik ook mijn promotor, Theo
Mulder, bedanken. Theo, ik moet altijd een beetje lachen om jouw afkeer van topsport en alles
wat daarmee te maken heeft. Gelukkig heeft je schijnbaar onbegrensde interesse in het
menselijk brein en -aanpassingsvermogen ook je enthousiasme voor het talentonderzoek
opgelaaid. Ik hoop in de toekomst nog vaak met je in wetenschappelijk debat te gaan.
Een onderzoek komt niet van de grond zonder sponsoren en mensen in de praktijk die
mee willen denken en werken. In het licht hiervan wil ik allereerst graag Geert Slot van het
NOC*NSF bedanken voor zijn grote persoonlijke betrokkenheid. Ook ben ik Martijn Schakel
en Iwan Doyer van hockeyvereniging Rotterdam en Wim Kemps van hockeyvereniging ’s
Hertogenbosch veel dank verschuldigd. Ook al was het waterveld bezet, zat het
wedstrijdprogramma overvol en moest het trainingsprogramma er op aangepast worden, toch
lukte het ieder jaar weer om de metingen te verrichten. Hiervoor mijn dank aan alle spelers en
speelsters, trainers, coaches, begeleiders en ouders. Niet alleen van de hockeyverenigingen in
Rotterdam en Den Bosch, maar ook van GHHC, GHBS, HC Eelde en het dr. Nassau College
waar we pilot-onderzoeken hebben uitgevoerd. En van de voetbalverenigingen FC Groningen,
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Sc Heerenveen, Be Quick en Helpman, want ook al staat het niet in dit proefschrift, alle
metingen die we bij de hockeyers hebben verricht, zijn ook uitgevoerd bij voetballers. Mijn
dank gaat ook uit naar Marc Lammers en de dames van het nationale hockeyteam die aan de
tests hebben deelgenomen om ‘de afstand tot de top’ te bepalen.
Sinds de start van het project zijn er twaalf studenten afgestudeerd binnen het
talentonderzoek. Zij hebben meegeholpen met de metingen en meegedacht over afzonderlijke
vraagstellingen. Bedankt Mieke, Inge, Marlies, Joke, Karen, Suzanne, Yvonne, Irene, Moniek,
Nynke, Jesper en Ruud. Mieke, jou wil ik graag in het bijzonder bedanken omdat jij ook als
onderzoeksassistent veel hebt bijgedragen aan het talentonderzoek. Verder zijn er natuurlijk
nog de talloze testleiders die herhaaldelijk meehielpen om de organisatie op de testdagen rond
te krijgen en zonder wie het allemaal niet gelukt was. Speciaal wil ik hierbij een aantal
toptestleiders bedanken: Steven, Rienk, Eline en Alien. ‘Jongens’, bedankt!
Het tot een goed einde brengen van een promotietraject is een hele klus. Een klus die een
stuk leuker is met een groep enthousiaste collega’s om je heen. En daar heb ik het enorm mee
getroffen. Eerst op de gang van de derde verdieping in het AZG (ja, ik weet het, het is nu
UMCG), later op de derde bij BW. Ik heb wat afgelachen met Bianca, Leontien, Sandra,
Anuschka, Rients, Wietske, Grieke en Juha. En nu met Esther, Mieke, Suzanne, Henri, de
Robben, Helco, Geir en alle anderen. Als hele goede vriendinnen wil ik ik in het bijzonder
Esther en Bianca bedanken omdat jullie zoveel meer zijn dan collega’s. Ik vind het heel
bijzonder, Esther, dat we ook nog eens kamer- en huisgenootjes zijn en Bianc, ook al botsen
we wel eens met anderen, ik wil nog graag met je in de auto naar het zwembad.
Eigenlijk zou ik nog een hele tijd zo door willen gaan en iedereen willen bedanken die
belangrijk voor me is. Nathalie, Rienk en kleine Tom bijvoorbeeld, of Robert en Iris (nog heel
erg bedankt voor je hulp bij het lay-outen, en...nee, we laten Arjan echt niet nog een keer
winnen!). En Edwin, ook jij bedankt voor je hulp bij de grafieken. En natuurlijk mijn familie.
Papa en mama, Jildou en Volkert, Pieter-Jorn en Corina, pake niet te vergeten, maar natuurlijk
ook Jan en Siny, Annemarie en Henri, Steven en Anna en Clemens en Laura die stuk voor
stuk zo nu en dan hun wenkbrauwen optrokken omdat ik alweeeeer moest werken. En dan zijn
er nog mijn lievelingsneefjes en –nichtjes die ervoor zorgen dat mijn hoofd zo nu en dan weer
even leeg wordt: Sven, Vera-Lou, Nori, Stijn, Linde en Peike Joeri.
Maar het allerbelangrijkste ben jij, Arjan. Jij weet als geen ander hoe belangrijk dit voor
mij is. Zonder jou zou ik het niet redden. Zonder jou wil ik het niet eens redden. Lieve Arjan:
we gingen voor goud en vonden elkaar!
Ook al ben ik blij dat het proefschrift nu af is, ik beëindig dit dankwoord zoals ik het
begonnen ben: mijn proefschrift is af, maar ik ben nog lang niet klaar.
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