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Electronic Theses and Dissertations
8-2016
Positional and Match Action Profiles of Elite
Women’s Field Hockey Players in Relationship to
the 2015 FIH Rule Changes
Heather A. Abbott
East Tennessee State University
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Positional and Match Action Profiles of Elite Women’s Field Hockey Players in
Relationship to the 2015 FIH Rule Changes
_______________
A dissertation
presented to
the faculty of the Department of Exercise and Sport Sciences
East Tennessee State University
In partial fulfillment
of the requirements for the degree
Doctor of Physiology in Sport Physiology and Performance
_______________
by
Heather Anne Abbott
August 2016
_______________
Kimitake Sato, Ph.D., Chair
Michael H. Stone, Ph.D.
Satoshi Mizuguchi, Ph.D.
Dave Hamilton, MS.
Keywords: Global Positioning System, Time Motion Analysis, Team Sport,
Substitutions, Field Sports, Time on Pitch
1
ABSTRACT
Positional and Match Action Profiles of Elite Women’s Field Hockey Players in
Relationship to the 2015 FIH Rule Changes
by
Heather A. Abbott
The objective of this dissertation was to examine the action profiles of elite field hockey
players in relationship to the 2015 FIH rule change. The following are major findings of
the dissertation:
Study 1 – Relative action profiles before the rule change revealed that defenders work at
a lower meter per minute (m/min) when compared with all other positions, and that
forwards, midfielders, and screens perform similar m/min during a game. Examination of
pre rule change difference from the 1st to the 2nd half play showed that elite level field
hockey players are able maintain high-intensity actions in zone 6 throughout the game by
increasing actions in zones 1 and 2, and decreasing actions in zones 4 and 5.
Study 2 – Action profiles after the rule formatting change revealed the team was unable
to match the percent of distance covered in zones 4 and 5 during the 1st quarter all in
subsequent quarters. The low intensity actions in zone 1 and 2 gradually increased, while
m/min gradually declined. However the percent of distance covered in zone 6 showed no
statistically significant change. When positional differences were examined forwards
covered the greatest percent of distance in zones 5 and 6, followed by midfielders,
2
screens, and defenders. This pattern varies for zone 4, within which the midfielders
possesses the greatest percent distance covered.
Study 3 – Relative action profile comparisons for the team, pre to post the 2015 rule
change did not indicate a significant change in zones 2, 3, 4, 5, and 6. However zone 1
experience a statistically significant decrease. Positional analysis showed statistically
significant changes for midfielders only. The changes were a decrease in zone 1, and
increase in zone 5 and 6 during the first half of the game, and decrease in zone 1 and
m/min during the second half of the game.
A major focus of the US Women’s National Team is to develop the athletes’ physical
capacity to maintain and repeat high intensity actions. The combination of physical
preparation and tactical strategies allow the team to express high m/min and numerous
high intensity actions throughout a match.
3
Copyright 2016 by Heather A. Abbott
All Rights Reserved
4
DEDICATION
This dissertation is dedicated to my family. This journey would not have been
possible without your love, support and encouragement.
5
ACKNOWLEDGEMENTS
Dr. Kimi Sato - for his advice and endless patience throughout this project. It
would not have been possible without his support and assistance in the analysis and
subsequent write-ups.
Dr. Michael Stone – for his willingness to share his knowledge and experience, I
am forever grateful.
Dr. Satoshi Mizuguchi – for his constructive criticism, and for tell me what I
needed to hear instead of what I wanted to hear.
Dave Hamilton - for allowing me the opportunity to work with the US Women’s
National Field Hockey Team, and providing me with endless advice on GPS match
analysis.
The US Women’s National Field Hockey Team – for volunteering to participate
in this research. Without their participation, none of this would have been possible.
My colleagues at East Tennessee State University – for motivating me during my
educational pursuits.
Thank you
6
TABLE OF CONTENTS
Page
ABSTRACT...............................................................................................................2
DEDICATION ...........................................................................................................5
ACKNOWLEDGEMNTS .........................................................................................6
LIST OF TABLES .....................................................................................................11
LIST OF FIGURES ...................................................................................................13
Chapters
1. INTRODUCTION ................................................................................................14
Rational for Research/Statement of Problem .....................................................17
Objective of Research ........................................................................................17
Significance........................................................................................................18
Operational Definitions ......................................................................................20
2. COMPREHENSIVE LITATURE REVIEW .........................................................21
Introduction ........................................................................................................21
Existing Literature .............................................................................................21
Field Hockey Rules ............................................................................................22
Time Motion Analysis History ..........................................................................24
Global Positioning System History....................................................................25
Global Positioning System Validity and Reliability ..........................................26
Field Hockey Specific Global Positioning System Validation ..........................30
Global Positioning System and Micro Technology Metrics .............................34
Distance......................................................................................................34
7
Velocity ......................................................................................................35
Inertial Movement Analysis .......................................................................38
Player Load and Metabolic Power .............................................................39
Global Positioning System Action Profiles in Field Hockey.............................40
Action Profile Comparison ........................................................................41
Monitoring .................................................................................................42
Game vs. Practice.......................................................................................43
Fatigue........................................................................................................45
Problems/Considerations/Match Variability ..............................................47
Summary ............................................................................................................47
3. STUDY 1: Positional and Match Action Profiles of Elite Women’s Field Hockey
Players ........................................................................................................................49
Abstract ..............................................................................................................50
Introduction ........................................................................................................51
Methods..............................................................................................................53
Experimental Approach to the Problem .....................................................53
Athletes ......................................................................................................53
Procedures ..................................................................................................................54
Statistical Analysis .....................................................................................................55
Terms/Definitions .....................................................................................56
Results ................................................................................................................57
Descriptors .................................................................................................57
Position Differences ...................................................................................61
8
1st vs. 2nd Half ............................................................................................64
Discussion ..........................................................................................................66
Practical Application ..........................................................................................68
References ..........................................................................................................69
4. STUDY 2 International Women’s Field Hockey Action Profiles Post 2015 Rule
Change .......................................................................................................................75
Abstract ..............................................................................................................76
Introduction ........................................................................................................77
Methods..............................................................................................................78
Athletes Subjects........................................................................................78
Data Collection ..........................................................................................78
Data Analysis .............................................................................................81
Statistical Analysis .............................................................................................82
Results ................................................................................................................83
Descriptive .................................................................................................83
Team Quarter Comparison .........................................................................85
Quarter Comparison by Position................................................................89
Discussion ..........................................................................................................99
Conclusion .........................................................................................................100
References ..........................................................................................................101
5. STUDY 3 – Changes in Elite Field Hockey Player Action Profile due to 2015
Rule Change ...............................................................................................................104
Abstract ..............................................................................................................105
9
Introduction ........................................................................................................106
Methods..............................................................................................................107
Athletes Subjects........................................................................................107
Data Collection ..........................................................................................107
Data Analysis .............................................................................................110
Statistical Analysis .............................................................................................111
Results ................................................................................................................112
Team Full Game Pre vs. Post Rule Change ...............................................112
Team Pre 1st vs. Post 1st and Pre 2nd vs. Post 2nd ...........................................................112
Position Pre 1st vs. Post 1st and Pre 2nd vs. Post 2nd ....................................................112
Discussion ..........................................................................................................121
Conclusion .........................................................................................................122
References ..........................................................................................................123
6. SUMMARY AND FUTURE INVESTIGATIONS ..............................................127
REFERENCES ..........................................................................................................147
APPENDIX:ETSU Institutional Review Board Approval Form...............................148
VITA ........................................................................................................................149
10
LIST OF TABLES
Table
Page
2.1 Validation GPS: devices, sampling frequency, and criterion measures ..............33
2.2 GPS Velocity Zones.............................................................................................37
3.1 Tournament Schedule and Outcome ....................................................................58
3.2 Positional Playing Time .......................................................................................58
3.3 Champions Challenge and World Cup Player and Position Action Profiles .......69
3.4 Absolute Number of efforts, Maximum and Average Distance Covered in Top
nkl3 Velocity Zones ...................................................................................................60
3.5 Descriptive Data for Relative Velocity Zones .....................................................62
3.6 ANOVA Main Effect for Position .......................................................................62
3.7 Relative Percent Distance covered in Velocity Zones Between Positions ..........63
3.8 Team Changes Relative Percent Distance covered from 1st to 2nd half ...............65
4.1 Tournament Schedule and Outcome ....................................................................78
4.2 Absolute Positional Action Profiles for the Full Game .......................................82
4.3 Relative Action Profiles for the Full Game .........................................................82
4.4 Team Relative Action Profiles by Quarter...........................................................84
4.5 Team Relative Action Profiles Between Quarters ...............................................84
4.6a Forwards Relative Action Profiles by Quarter ...................................................89
4.6b Midfielder Relative Action Profiles by Quarter.................................................89
4.6c Screens Relative Action Profiles by Quarter .....................................................90
4.6d Defenders Relative Action Profiles by Quarter .................................................90
4.7a. Forwards effect size across Quarter ..................................................................91
11
4.7b. Midfielders effect size across Quarter ..............................................................91
4.7c. Screens effect size across Quarter .....................................................................92
4.7d. Defenders effect size across Quarter.................................................................92
5.1 Tournament Schedule ..........................................................................................106
5.2 Paired t-test for Team Full Game Action Profiles Pre vs. Post ..........................111
5.3a Paired t-test for Team Full Game Action Profiles Pre1 vs. Post 1 .....................112
5.3b Paired t-test for Team Full Game Action Profiles Pre 2 vs. Post 2 ...................113
5.4a Forwards Pre 1 vs. Post 1 & Pre 2 vs. Post 2 Wilcoxon Signed Rank Tests ....114
5.4b Midfielders Pre 1 vs. Post 1 & Pre 2 vs. Post 2 Wilcoxon Signed Rank Tests .115
5.4c Screens Pre 1 vs. Post 1 & Pre 2 vs. Post 2 Wilcoxon Signed Rank Tests ........116
5.4d Defenders Pre 1 vs. Post 1 & Pre 2 vs. Post 2 Wilcoxon Signed Rank Tests ....117
12
LIST OF FIGURES
Figure
Page
4.1 Team m/min by quarter ........................................................................................85
4.2 Team Relative Percent Distance ..........................................................................86
4.3a Forwards Relative Percent Distance ..................................................................93
4.3b Midfielders Relative Percent Distance...............................................................94
4.3c Screens Relative Percent Distance .....................................................................95
4.3d Defenders Relative Percent Distance .................................................................96
13
CHAPTER 1
INTRODUCTION
Field hockey is an international sport played by both men and women, which has
undergone rapid and radical change within the past ten years. The arrival of synthetic
playing surface (Malhotra, Ghosh, & Khanna, 1983), modernized sticks (Allen, Foster,
Carré, & Choppin, 2012), and rule alterations such as the self-start (Tromp & Holmes,
2011), have changed the physiological requirements of the game. Most recently the
match formatting has been changed from two thirty-five minute halves with a ten minute
half time, into four fifteen minute quarters with two, two minute rest periods, and one ten
minute half time (FIH, 2014).
The sporting actions in field hockey consist of multiple interspersed high intensity
activities, which are multi directional in nature. It requires significant levels of aerobic
and anaerobic capacity, strength and power (Reilly & Borrie, 1992). In field hockey the
number of players on the field at one time is eleven. Normally a team plays with ten field
players, and one goalie (Mitchell-Taverner, 2005). Seven players are available on the
bench for rolling substitutions; expect for the Olympics and Pan American games, in
which only five players are allowed on the bench. The interchanging of players can occur
at any moment, creating the opportunity to maintain high levels of intensity throughout
the game. Various factors such as player position (Gabbett, 2010) (Field Hockey), style
of play (Bradley et al., 2011) (Soccer), level of play (Jennings, Cormack, Coutts, &
Aughey, 2012b) (Field Hockey), age of players (Vescovi, 2014) (Field Hockey), gender
of the athletes (Bradley, Dellal, Mohr, Castellano, & Wilkie, 2014) (Soccer), level of
14
opposition (Lago, 2009) (Soccer), and match status (Lago-Penas, 2012) (Soccer) can
have an impact on the action profiles of players.
Time motion analysis (TMA) techniques have provided valuable information
regarding the action profiles of field players, in numerous sports including field hockey
(Jennings, Cormack, Coutts, & Aughey, 2012a; Jennings et al., 2012b; MacLeod,
Bussell, & Sunderland, 2007; Macutkiewicz & Sunderland, 2011; Spencer, Lawrence, et
al., 2004; Spencer, Rechichi, et al., 2005; Vescovi & Frayne, 2015; White & MacFarlane,
2014; Wyldea, Yonga, Azizb, Mukherjeec, & Chiac, 2014). It has proved to be a useful
tool to quantify the physical output of players during matches (Bloomfield, Polman, &
O'Donoghue, 2007) through either the manual or computer analysis of film. However, the
use of time motion analysis systems has been restricted from most teams due to its
expensive and time intensive nature. The recent introduction of sport specific Global
Positioning System (GPS) technology for TMA has allowed the monitoring of action
profiles by a greater number of field sport teams due to its less expensive nature, and
computerized analysis programs.
The evolution of the sports GPS technology allowed coaches and sports scientist
to determine and compare player’s movements during the games or training. The newer
higher sampling rate has created a more valid tool for measuring distance and velocity
(Castellano, Casamichana, Calleja-Gonzalez, Roman, & Ostojic, 2011; Johnston,
Watsford, Kelly, Pine, & Spurrs, 2014; Portas, Harley, Barnes, & Rush, 2010). However,
using inappropriate analysis procedures can alter the perceived action profiles of field
hockey players (Wehbe, Hartwig, & Duncan, 2014). It has been previously noted that
discrepancies exist when reporting locomotor activities (e.g. walking, jogging, running)
15
with TMA (Gabbett, 2010), and making a comparison between studies is difficult.
Inconsistencies in data processing also create discrepancies in reported measures,
specifically when comparing full game time to the time on pitch. Furthermore, different
GPS analysis procedures can also influence the time-dependent measures reported by
GPS.
The current practice for GPS analysis has essentially been driven by soccer
literature. Normally soccer players are on the pitch for a full game or do not reenter the
game after substitution. However, in field hockey a high number of substitutions are
allowed. Unlimited substitutions allow for offensive and defensive pressure to be applied
throughout the match. With this understanding it is important to acknowledge the need
for different analysis strategies, and the difficulty to generalize women’s soccer data to
field hockey. Full game versus time on pitch analysis alters the description of the work to
rest ratio and physical demands of elite field hockey players (White & MacFarlane,
2013).
It is also important to note that TMA system such as GPS do not measure the
demands of competition rather the outputs of players. Data from GPS systems can be
easily misinterpreted. For example, total distance expressed without the minutes of
match play can be misleading, because total distance covered in related to time on pitch.
The expression of distance per minute of match time is important in sports with unlimited
substitutions like field hockey. However, even the use of distance per minute can be
misleading high-intensity movements are lumped together with low-intensity movements.
Therefore it has led to the implementation of distances being measured in varying
velocity zones. This allows practitioners to locate intense action during matches.
16
However velocity zones can be arbitrarily set leading to the misrepresentation of data.
Regardless, GPS has provided coaches, trainers and sports scientist with a better
understanding of the action profiles of athletes during games and training. This has led to
the ability to tailor and adapt training and recovery programs to individual athlete
profiles.
Rationale for Research/Statement of Problem
New match formatting and clock stoppages during corners and after goals,
implemented by the International Hockey Federation (FIH), are believed to provide teams
the ability to maintain and produce more high intensity actions. The new rule reads:
“…Match Periods (4 x 15 Minute Quarters and time stoppages for the award of Penalty
Corners and Goals), the Penalty Corner Countdown Clock and Video Umpire, which will
only be used at FIH World level Tournaments…” (FIH, 2014). The author suspects that
the implementation of these rules has affected the action profiles of field hockey players,
by allowing for more high-intensity actions, resulting from the additional rest periods.
The available action profiles for women’s field hockey in relationship to rule
changes is scarce. For example only one study exists examining the effect of the 2009,
free-hit rule on the game (Tromp & Holmes, 2011). To the authors knowledge no data
exists on the effects of the 2015 rule changes on women’s field hockey.
Objective of Research
The primary objective of this dissertation was to establish the changes in GPS
game action profiles for the relative variables, m/min, and percent of the total distance
17
covered in zones 1 through 6 from the US Women’s National Field Hockey team players,
for the team and by position, caused by the evolution of game formatting, specifically the
rules adapting play at World level tournaments by the FIH (FIH, 2014).
The secondary objectives of this dissertation included:
1.
Reporting the absolute action profiles including total distance, and total
distance covered in zones 1, 2, 3, 4, 5, 6, pre 2015 rule change for team,
player, and position, and post the 2015 rule change for team, and
position.
2.
Determine the changes in team, and positional relative action profiles
before the 2015 rule change from the 1st to 2nd half of the game.
3.
Determine the team and positional relative action profiles after the 2015
rule change in across the four quarters of a game.
Significance
This type of research is not novel; GPS research has provided useful information
in many field sports such as field hockey (Gabbett, 2010), soccer (Wehbe et al., 2014),
rugby (Sirotic, Knowles, Catterick, & Coutts, 2011), and Australian football (Varley,
Gabbett, & Aughey, 2014), and various studies have investigated the changes in rules on
different sports such as Rugby League (Eaves, Lamb, & Hughes, 2008; Meir, Colla, &
Milligan, 2001), Rugby Union (Williams, Hughes, & O'Donoghue, 2005), Beach
Volleyball (Giatsis, 2003; Ronglan & Grydeland, 2006), and Netball (O'Donoghue,
2012). However, for this dissertation the population, sample size, consistency of team
members, and investigation of the 2015 rule change are innovative. This dissertation uses
18
the US women’s national team, who currently represent an elite standard of women’s
field hockey. The team finished with a World Ranking of 8th in December 2014, and 7th
in December 2015. The data included in this dissertation span a period of four
international tournaments: the Champions Challenge 2014, Rabobank Hockey World Cup
2014, World League 3 Semi Finals, and 2015 Pan American Games. Two international
tournaments were played before to the rule change, and two international tournaments
were played after the rule change. The significance of this research is also strengthened
by the high percentage of returning players allowing for a comparison in almost same
sample. The team members remained constant between the Champions Challenge 2014
and Rabobank Hockey World Cup 2014. Two player changes were made for World
League 3 in comparison to the 2015 roster, and one player change was made for the Pan
American Games compare to the 2015 roster. In addition to the retention of players
across games, the large number of files, and the evaluation of multiple tournaments is
uncommon. Analysis with many observations of the same players are more likely to
reduce discrepancies between matches due to different opponents and tactics and to
reveal underlying commonalities. To the author’s knowledge the effects of 2015 rule
change on games action profiles has never been studied. It is important to define the new
match play action profiles of the team and of each position in order to better prepare
players. This will allow for the continued development of attributes specific to the new
style of play. Knowledge of the progression of the game, and the ongoing changes in
physiological demands will be beneficial for strength and conditioning practitioners when
designing training programs for the development of athletes.
19
Operational Definitions
FIH International Hockey Federation – is the international governing body of field
hockey.
GPS Global position system – is a satellite based navigation system, that provided
location and time information
TMA Time-Motion Analysis – varying techniques used to quantify the action profiles of
players during competitions.
20
CHAPTER 2
COMPREHENSIVE LITERATURE REVIEW
Introduction
The purpose of this review is to examine the current literature surrounding the
methods used to determine GPS action profiles for team sports. The primary focus is field
hockey. However other team-based sports are discussed in the literature review.
Existing Literature
The existing literature on field hockey is sparse. Podgórski and Pawlak (2011)
published “A Half Century of Scientific Research in Field Hockey” with the goal of
illuminating the status of research field hockey publications from 1960 to 2010. The
number of citations for field hockey (7,459) pales in comparison to other field based team
sports such as lacrosse (22,700), cricket (182,759), soccer (258,943), and football
(1,025,038). The research publications were obtained from PubMed and EBSCO host
databases, and www.scholar.google.pl, by entering the term “FIELD HOCKEY”. Upon
further analysis the authors deduced that only 208 of the 7,459 citations directly related to
field hockey, with the majority of the peer-reviewed research examining: the injuries
occurring during training and games, skill development, tactics and match play, as well as
the anthropometric and physical characteristics of players.
There is a lack of research examining the specific game and practice demands of
field hockey from elite population. Several studies claim to involve elite populations, but
the use of the word elite can be often be misrepresented (Dwyer & Gabbett, 2012;
Wassmer & Mookerjee, 2002). The minuscule amount of research involving elite field
21
hockey players presents with methodological limitations. Issues include lack of
information on the reliability of the analysis method, low sample size, generalizing one
players action profile for a whole positional group, and varying team rosters. Podgórski
and Pawlak (2011) showed an increasing trend in the number of field hockey related
publications. However the numbers of papers are still inferior to other field sports.
Field Hockey Rules
Field hockey is an internationally played sport, with the two largest tournaments
based competitions being the Olympics and the World Cup. It is played between two
teams of eleven players, normally ten field players, and a goalkeeper (Mitchell-Taverner,
2005). The objective of the game is to score a spherical ball, with a circumference
between 224 and 235 mm, and a weight between 156 and 163 grams in into the
opponents 3.66 meters wide by 2.14 meters high goal (FIH, 2014). The game is played at
the international level on 91.4 meters by 55 meters’ artificial water based turf. Field
hockey shares similar tactical and structural concepts with soccer. However aspects of
field hockey are unique to the sport, and must be taken into consideration when analyzing
field hockey specific research (Hodun, Clarke, De Ste Croix, & Hughes, 2016). The
unique aspect of field hockey includes: body positions due to the use of sticks (Reilly &
Borrie, 1992; Wdowski & Gittoes, 2013), rolling substitutions (Tromp & Holmes, 2011),
penalty corners (Vinson et al., 2013), and the drag flick (de Subijana, Juarez, Mallo, &
Navarro, 2011; Laird & Sutherland, 2003).
Historically field hockey has been an intermittent high intensity sport consisting
of two 35 minute halves (FIH, 2012). Numerous rule changes, implemented by the
22
International Hockey Federation (FIH) over the years have transformed the game of field
hockey with some having more impact than others. Some rules have had more noticeable
effects on the game. In 1975 when the FIH developed a common set of rules for men and
women, in 1992 unlimited substitutions were allowed, in 1998 the off sides rule was
removed from the game, in 2009 self-pass rule was implemented, and most recently in
2015 the time parameters of the game were changed (FIH, 2014). The action demands of
the sport are in transition due to the most recent rule change with international matches
being played in four 15 minute quarters. In addition to decreasing the game time from 70
minutes to 60 minutes, clock stoppages have been introduced. The game clock is stopped
during corners, and a countdown clock, independent from the game clock, of 40 seconds
is started. The offensive team has 40 seconds to complete the awarded corner. The game
clock is also stopped after a goal is scored. The clock is stopped, and a 40 second
countdown clock independent of the game clock is started, this gives the team scoring
team time to celebrate. The rule change effects the official game time, but with the ability
to stop the clock, the actual length of a game can vary. For example, if three corners
occurred in the first quarter of a game the official game time maybe 15 minute. However,
the length of play would be 17 minutes. International players, coaches, and sport
scientists have recognized the impact of the game formatting rule change on substitution
tactics, and game intensity. However to the author’s knowledge no empirical evidence of
the effect of the 2015 game formatting rule change has been presented.
Time motion analysis is commonly used in team sports to track player
movements, and create player action profiles (Gabbett, 2010; Jennings et al., 2012a;
Macutkiewicz & Sunderland, 2011; Vescovi & Frayne, 2015; White & MacFarlane,
23
2014; Wyldea et al., 2014). Global positioning systems specifically allow for the
movement patterns such as total distance, distances covered at various speeds, and time
on pitch. With the ever-evolving rules in field sports, performance analysis, and player
action profiles provided by GPS can be a helpful tool in assessing the effects of different
rules.
Time Motion Analysis History
Time motion analysis is a method used for quantifying the action profiles
performed by athletes. It involves tracking the movements of athletes, through either the
manual or computer analysis of film, or GPS. Video based TMA has proved to be a
useful tool to quantify the physical output of players during field based sporting events
(Bloomfield et al., 2007; Rienzi, Drust, Reilly, Carter, & Martin, 2000). Early research
involving TMA utilized video cameras, and could only captured the data of a single
player (Carling, Bloomfield, Nelsen, & Reilly, 2008). The nature of time and labor
intensive analysis made it difficult for use in the team environment. However, it was
shown to be reliable for examining movements of individual players (Duthie, Pyne, &
Hooper, 2003). The introductions of sophisticated computer analysis systems have
reduced the time-intensive nature of video analysis (Carling et al., 2008; Gray & Jenkins,
2010). However, the systems are expensive, require costly stadium based instillation, and
require intensive calibrations. Recently sporting GPS technology and combined GPS and
accelerometer systems have become popular in training and match quantification. The
relatively inexpensive, and portable GPS technology has allowed performance tracking to
be used by a greater number of field sports.
24
Global Position Systems History
Current GPS technology uses satellites to construct the geographical location and
movements of a receiver, which allows for the tracking of individual players. However,
until the 1980s GPS technology was restricted for military use only. When the technology
was finally made available for civilian use, the system was created with deliberate error.
This made precision measurement impossible. In 2000, the US Department of Defense
reduced the deliberate error in the system allowing for an increase in accuracy of nondifferential GPS technology (Terrier, Ladetto, Merminod, & Schutz, 2000). This change
allowed for lighter, smaller and cheaper units to become available to the general public.
Specific sports units were then developed to withstand heat, moisture, and sporting
collisions. Current units are light and compact enough to wear under an athletic jersey in
specially designed harnesses. Specialized GPS units and associated computer analysis
provided the ability to both collect and analyze data relevant to the performance of
athletes. The growth of the sports focused GPS industry resulted in GPS technology
integrated with accelerometers and gyroscopes to allow for the collection of physical
performance markers by individual athletes (Coutts & Duffield, 2010; D. Jennings, S.
Cormack, A. J. Coutts, L. J. Boyd, & R. J. Aughey, 2010b; Rampinini et al., 2007;
Varley, Fairweather, & Aughey, 2012).
The application of GPS technology in the context of team sport has overcome
many of the limitations of video-based TMA. The evolution manual video analysis to
GPS allows coaches and sports scientist to determine and compare player’s movements
during real time games or training. The low cost of GPS technology and portability of the
system allow for data to be collected in many outdoor settings.
25
Global Positioning System Validity and Reliability
The validity and reliability of GPS systems when assessing movement demands
of team sports are influenced by several factors: GPS sampling frequency, speed,
distance, and path of movements. The current trend in team sports is 10 Hz nondifferential GPS unit integrated with a 100 Hz tri-axial accelerometer, gyroscope, and
magnetometer. However the above mention measures must be considered when using
any GPS units, and especially when examining previous publications.
Sampling frequency also known as sampling rate is the number of samples
obtained during one second of collection. The sampling frequency of the technology
influences the accuracy, reliability, and validity of data. Early GPS units operated at 1
Hz, while newer GPS units sample at frequencies of 5, 10, and even 15 Hz (Cummins,
Orr, O'Connor, & West, 2013). As the sampling rates of GPS signals have increased, the
accuracy of the measuring has also increased. Studies that compared units operating at 5
Hz to older units operating at 1 Hz found that the increased sampling frequency increased
the accuracy of measurements (Coutts & Duffield, 2010; Jennings, Cormack, Coutts,
Boyd, & Aughey, 2010a; MacLeod, Morris, Nevill, & Sunderland, 2009; Petersen, Pyne,
Portus, & Dawson, 2009; Portas et al., 2010; Randers et al., 2010).
Jennings et al. (2010a) assessed the validity and reliability of distance data
measured by GPS units sampling at 1 Hz and 5 Hz during movement patterns common to
team sports. They found that the accuracy decreased as speed of locomotion increased in
straight line and the change of direction (COD) running. When comparing the accuracy
of distance collected between a 1 Hz and a 5 Hz GPS, the standard error of estimate was
calculated as the percent differences between the know distance and the GPS measured
26
distance. For a standing start, 10-m sprint was the percent difference between the known
and GOPS measured was 32.4 % for units collecting at 1 Hz and 30.9 % for units
collecting at 5 Hz. The increased sampling rate improved the GPS devices standard error.
However, the 5 Hz systems were still limited in its ability to assess short, high speed,
linear and COD running. Given that the most common sprint distance in team sports such
as soccer, rugby, and field hockey are between 10-20 meters (Spencer, Bishop, Dawson,
& Goodman, 2005), using 5 Hz GPS to measure distance traveled at very high velocities
may be inappropriate, especially if older units that sample at low frequencies are used for
data collection.
Studies comparing GPS units sampling at 5 Hz and 10 Hz found superior
accuracy for units sampling at 10 Hz when measuring velocity during straight-line
running, accelerations, and decelerations, when compared to a tripod mounted laserbased criterion measure (Rampinini et al., 2015; Varley et al., 2012). The superiority of
10 Hz units over a linear distance of 15 and 30 meters is shown by Castellano et al.
(2011). The researchers used video and timing gates to show accurate distance
measurements taken by a unit sampling at 10 Hz. They noted an increase in accuracy as
the distances increased. The intra-device reliability was greater over 30 meters than 15
meters. The inter-device reliability showed CV = 1.3%for 15 meters 30 meters and CV =
0.7% 30 meters. They found that the accuracy of the units improved with increased
distance, and the standard error of measure between the known distance and distance
measured by the GPS units was 10.9% when running 15 meters, but it was only 5.1% for
the 30 meters trails. Regardless of the sampling frequency used, accuracy is reduced as
movement velocity increases and distances covered decrease. Portas et al. (2010)
27
reported measurement error between the know distance and GPS estimated distance to be
lowest during walking, which was measured at 1.8 meters per seconds, and produced of
0.7 % and highest during running at 5.6 %. Jennings et al. (2010a) showed that the
measurement accuracy of both 1 Hz and 5 Hz GPS systems improved as distances
covered increases during straight-line movements, COD, and a team sport running
simulation circuit. The difference between criterion and GPS measured distance ranged
from 9.0% to 32.4%. Total distance over the simulated running circuit exhibited the
lowest variation (CV 3.6%) while sprinting over 10 meters demonstrated the highest (CV
77.2% at 1 Hz). These studies provide justification for the use of newer, higher
frequency, GPS units in intermittent high intensity team sports.
In addition to sampling frequency the movements used during validity and
reliability testing must be considered. A study by Rampinini et al. (2015) examined a
team sport specific running courses varying intensities. The courses required the
participants to perform acceleration and deceleration movements. The authors examined
two GPS units, sampling at 5 Hz and 10 Hz. The authors found that when compared to
radar gun sampling at 32 Hz the 10 Hz GPS unit showed a 1.9% error for total distance
and a 4.7% error for high speed running.
Steps have even been taken to examine specific field sport conditioning. For
example, the convergent validity and the test–retest reliability of GPS devices for
measuring repeated sprint ability was studied by Barbero-Álvarez, Coutts, Granda,
Barbero-Álvarez, and Castagna (2010). Results showed a strong correlation between peak
speed measures with the GPS device and repeated sprint ability test performance
measured against timing gates (r=0.87 for 15 meters, and r= 0.94 for 30 meters) These
28
data supports the use of 1Hz GPS for measuring repeated sprint ability performance
testing in field sports, suggesting that GPS can be used in place of timing gates for RSA
performance testing.
When examining the validity and reliability of GPS units it is also import to
consider the unit’s manufacture, and model. In a study by Randers et al. (2010) the
authors compared four different TMA systems, a video-based time-motion analysis
system, a semi-automatic multiple-camera system, and two GPS systems, one sampling a
5 Hz and one sampling at 1 Hz. The total distance covered during a soccer match was
different between a semi-automatic multiple-camera system (10.8 ± 0.8 km), a videobased time–motion analysis system (9.5 ± 0.7 km), a 5 Hz GPS device (10.7 ± 0.7 km)
and a 1 Hz GPS device (9.5 ± 0.9 km). The distance covered in high-intensity running
was 2.7 ± 0.5, 1.6 ± 0.4, 2.0 ± 0.6 and 1.7 ± 0.4 km for the semi-automatic multiplecamera system, video-based system, 5 Hz GPS and 1 Hz GPS, respectively. Sprinting
distance during the match was different between the four systems, with recorded values
of 0.4 ± 0.2, 0.4 ± 0.2, 0.4 ± 0.2 and 0.2 ± 0.2 km for the semi-automatic multiplecamera system, video-based system, 5 Hz GPS and 1 Hz GPS, respectively. These results
suggest that absolute distances taken from different time-motion analysis technologies
should not be used interchangeably.
When examining the previously presented information it is difficult to compare
the validation studies due to the multiple criterion measures used to validate the GPS
units. Therefore, the validation of GPS has methodological limitations that must be taken
into account when assessing results. Criterion measures ranged from: electronic timing
gates (Barbero-Álvarez et al., 2010; Castellano et al., 2011; Coutts & Duffield, 2010;
29
Jennings et al., 2010a; MacLeod et al., 2009; Petersen et al., 2009; Portas et al., 2010),
radar gun (Rampinini et al., 2015), Tripod-mounted laser (Varley et al., 2012), VICON
(Duffield, Reid, Baker, & Spratford, 2010),Theodolite (Gray & Jenkins, 2010) and videobased time-motion analysis system, and a semi-automatic multiple-camera system
(Randers et al., 2010). The use of trundle wheel or tape measure, and timing gates can
introduce error. For example, there is error in the ability of a trundle wheel to accurately
measure distance, and in the timing gates to accurately measure time (Barbero-Álvarez et
al., 2010; Castellano et al., 2011; Coutts & Duffield, 2010; Jennings et al., 2010a;
MacLeod et al., 2009; Petersen et al., 2009; Portas et al., 2010). The methodological
limitations of the trundle wheel or tape measure, and timing gates are most apparent
when the research by Duffield et al. (2010) is examined. They used VICON to validate
two 1 Hz GPS units and two 5Hz GPS units for total distance and speed. Both devices
showed statically significant differences to the VICON for distance and speed. The interunit reliability ranged from r=0.10-0.70, and CV was 4% for 1 Hz units, and 25% for 5
Hz units. When a precision system was used to examine GPS validity it showed that both
devices underestimate distance and speed. It also showed inter-unit error exist,
establishing the need for player to wear the same units across data collection.
Field Hockey Specific Global Positioning System Validation
Various research has been done to validate GPS for general field sports, including field
hockey (MacLeod et al., 2009). MacLeod et al. (2009) examined the validity of nondifferential GPS specifically for movement patterns used during field hockey. They
conducted a TMA of international field hockey players to determine the movement
30
patterns expressed during games. With the TMA information, the authors created a circuit
including a T-shaped shuttle, a straight-line shuttle, a straight sprint, and a zigzag shuttle
to replicated players’ game movements. Each shuttle was measured against timing gates
for mean speed and total distance was measured with a trundle wheel. All participants
completed 14 laps with the intent of the total distance covered in an international match.
A limitation of the validation is in the available equipment; the GPS units only collected
at 1 Hz, and the accelerometer at 100 Hz. The Pearson correlation for the mean speed
recorded by the timing gates and the GPS for the shuttle movements is r = 0.99. However
the GPS overestimated distances for the T-shuttle, straight-line shuttle and straight-line
shuttle sprint by 0.1 meters, 0.2 meters and 0.1 meters respectively. The mean distance
covered reported by the GPS was 6820.5 meters, whereas, the actual distance covered
was 6818 meters reported by the trundle wheel. The authors concluded that the nondifferential GPS system used in the study provided a valid measurement tool for
assessing player movement patterns typically seen in field hockey. However the
previously noted practical limitations of sampling frequency affect the potential precision
of the data. With all of the previously mentioned validation studies the inherent error of
the criterion measure must be taken into consideration. There is human error in the ability
of the trundle wheel to actually measure the distance covered, and determining the exact
starting point with GPS software can be difficult. In addition to this the timing gates are
not a gold standard of measurement and carry inherent error. A summary of the GPS
devices, sampling frequency, and criterion measure used for validation can be found in
Table 2.1.
31
With the current information we know that the velocity and distance of a task
directly influence the validity of the GPS measurements, and that GPS units with a higher
sampling rate enhance the validity of the data. While the addition of high intensity short
duration, and COD movement patterns decrease the validity of GPS results. The validity
of GPS units is task and time dependent, and must be considered when presenting the
results of data collection. With all the above taken into consideration we can then assume
that studies conducted with 1 Hz GPS units and even 5 Hz GPS units provide a poor
representation of sport action profiles, especially high intensity interval sports, in which
short distances are covered at high intensities
32
Table 2.1 Validation GPS: devices, sampling frequency, and criterion
measures
Study
MacLeod, Morris, Nevill, & Sunderland,
2009
GPS Device
Frequency
Task
Measure
Parameter
1 HZ
Field Hockey SC
ETT, TW
Distance, Speed
Petersen, Pyne, Portus, & Dawson, 2009
SPI Elite
MinimaxX, SPI-10, SPIPro
5 Hz, 1 Hz
Cricket SC
ETG
Distance, Speed
Barbero-Álvarez, Coutts, Granda, BarberoÁlvarez, & Castagna, 2010
SpiElite
1 Hz
LR
ETG
Coutts & Duffield, 2010
SPI-10, SpieElite WiSPI
1 Hz
Team Sport SC
ETG
Peak Velocity
Distance, Peak
Velocity
Jennings, S. Cormack, Coutts,Boyd, &
Aughey, 2010
MinimaxX
1 Hz, 5 Hz
LR, COD, Team Sport SC
ETG
Distance, Speed
Jennings, Cormack, Coutts, Boyd, &
Aughey, 2010
Duffield, Reid, Baker, & Spratford, 2010
MinimaxX v2.5
5 Hz
LR, COD, Team Sport SC
GPS Unit
Between Unit
MinimaxX v2.0, SpiElite
1 Hz, 5 Hz
Tennis SC
VICON
Distance, Velocity
Portas, Harley, Barnes, & Rush, 2010
MinimaxX Version 2.5,
1 Hz, 5Hz
LR, COD, Soccer SC
TW
Distance
Gray, Jenkins, Andrews, Taaffe, & Glover,
2010
SpiElite
1 HZ
LR
Theodolite
Distance
Randers et al., 2010
MinimaxX v2.0, SpiElite
1 Hz, 5Hz
Soccer Match Running
Video
Distance
Castellano, Casamichana, CallejaGonzalez, Roman, & Ostojic, 2011
MinimaxX v4.0
10 Hz
LR
ETG, Video
Varley, Fairweather, & Aughey, 2012
V2.0 vs. MinimaxX
5 Hz, 10 Hz
LR Acc, Dec
ETG
Waldron, Worsfold, Twist, & Lamb, 2011
SPI-Pro
5 Hz
LR
ETG
Johnston et al., 2012
MinimaxX
5 Hz
Team Sport SC
Rader
Distance
Instantaneous
Velocity
Distance, Peak
Velocity
Distance, Peak
Velocity
Rampinini et al., 2015
SPI-Pro and MinimaxX
5 Hz, 10 Hz
LR, Acc, Dec
Radar
Distance, Speed,
Metabolic Power
Johnston, Watsford, Kelly, Pine, & Spurrs,
2014
MinimaxX S4 vs. SPIProX
10 Hz, 15 Hz
Team Sport SC
ETG
Distance, Speed
Notes: LR=Linear Runs, SC=Simulation Circuit, Acc=Acceleration, Dec=Deceleration, ETG= Electronic Timing Gates, TW=Trundle Wheel
33
Global Positioning System and Micro Technology Metrics
It is important to note that GPS does not measure the physical demands of a
competition; rather it measures the output of a player. Therefor action profile should be
used when refereeing to the metrics reported by GPS. There is extensive information on
the action profiles of athletes from GPS, accelerometer, gyroscope, and magnetometer
combined systems. The information produced by these systems includes total distance,
distance per minute, distance and time in velocity zones, time on pitch, COD,
acceleration, and deceleration. These metrics have been applied to detect fatigue in
matches, identify periods of most intense play, and differentiate action profiles by
position, competition level, and sport.
Distance
Total distance covered is a useful metric in steady state scenarios, but team sports
are intermittent in nature. In team sports with unlimited substitutions reporting total
distance travel can be especially misleading. The expression of distance per minute of
match time is more useful for individual athletes, because it provides a reference to the
time the athlete spent in game. Total distance can be misleading when examining athlete
profiles because high-intensity movements are lumped together with low-intensity
movements. The logical progression of total distance measures is to break movement into
distance per minute and then further into minutes spent in different velocity zones.
34
Velocity
Velocity zones measure the distances covered at a velocity. The velocity zones
can be manipulated within the analysis systems of the GPS. Therefore the definition of
the velocity band for standing, walking, jogging, running and sprinting often differs
between researchers. Recent research suggests the number of high-intensity activities
performed, and the time spent in each high-intensity zone, and the total distances covered
in each high-intensity zone are important metrics (Cummins et al., 2013). It has been
recommended that sport scientist use velocity ranges and sprint definition previously
published in the literature, but large variations in the definitions and inability to agree on
set definitions have resulted in confusion among researchers. Although recommendations
for reporting GPS data for field hockey have been presented there is generally a wide
range of reporting methods employed in the literature (Dwyer & Gabbett, 2012) (Table
2.2) A recent trend in the field hockey literature has been to simplify the velocity zones
into low, moderate, high, and very high intensity running classifications (Vescovi &
Frayne, 2015; White & MacFarlane, 2014) or into low and high intensity classifications
(Jennings et al., 2012a, 2012b), but these classifications also vary greatly. Regardless it
seems that the existing velocity ranges used in publications have been arbitrarily
determined. Several prominent field hockey specific publication (Lythe & Kilding, 2011;
White & MacFarlane, 2013) derive their velocity zones from Men’s International Soccer
Video based time motion analysis (Di Salvo et al., 2007).
For the purpose of this dissertation velocity zones from previously published
international women’s field hockey will be used (Macutkiewicz & Sunderland, 2011).
The velocity zones used will be: zone 1 standing (0-.16 m/s), zone 2 walking (.19-1.6
35
m/s), zone 3 jogging (1.69-3.05 m/s), zone 4 running (3.08-4.16 m/s), zone 5 fast running
(4.19-5.27 m/s), zone 6 sprinting (>5.27 m/s). The velocity zones, designating the action
profiles were originally adapted from definitions provided by Bangsbo and Lindquist
(1992) and Lothian and Farrally (1992). These studies were performed on soccer and
field hockey populations, respectively (Table 2.2).
36
Table 2.2 GPS Velocity Zones
Study
Gender
Type
Population
Australian FH
League
National and
International
FH
Velocity Zones (m/s)
Zone 1
Zone 2
Zone 3
Low 0 - 1
Mod 1 - 5
High 5 - 7
Low .1 - 4.17
High > 4.17
W
GPS
Jennings, Cormack,
Coutts, & Aughey, 2012
M
GPS
Jennings, Cormack,
Coutts, & Aughey, 2012
M
GPS
Australian FH
Low .1 - 4.17
High > 4.17
W
GPS
DI FH
Low 0-2.22
Mod 2.254.44
High 4.475.55
M
GPS
Stand 0-0.16
Walk .19-1.6
Jog 1.69-3.05
M
TMA
Stand 0-0.16
Walk .19-1.6
Jog 1.69-3.05
W
GPS
Stand 0-.1
Walk .2-1.7
Jog 1.8-3.6
W
GPS
Stand 0-0.16
Walk .19-1.6
Jog 1.69-3.05
M
GPS
Low 2.77 4.16
Mod 4.165.27
High Speed
5.27- 6.38
M
GPS
U-18 Asia Cup
FH
Low > 4.17
High < 4.17
M
TMA
Australian FH
NA
M
GPS
Australian FH
NA
M&W
TMA
Non-elite FH
NA
W
TMA
National
League FH
NA
TMA
International
Soccer
NA
Gabbett, 2010
Vescovi & Frayne, 2015
Lythe & Kilding, 2011
Di Salvo et al., 2007)
Dwyer & Gabbett, 2012
Macutkiewicz &
Sunderland, 2011
White & MacFarlane,
2014
Wyldea, Yonga, Azizb,
Mukherjeec, & Chiac,
2014
Spencer et al., 2005
Polglaze, Dawson,
Hiscock, & Peeling,
2014
McManus & Stevenson,
2007
Lothian & Farrally, 1992
Bangsbo & Lindquist,
1992
National New
Zealand
International
Soccer
Australian State
league FH
International
FH
Scottish
National FH
Notes: FH=Field Hockey M=Male W=Female
37
Zone 4
Zone 5
Max 5.588.88
Run 3.084.16
Run 3.084.16
Fast Run
4.19-5.27
Fast Run
4.19-5.27
Run 3.7-5.3
Sprint >5.4
Run 3.084.16
Sprint >
6.38
Fast Run
4.19-5.27
Zone 6
Sprint
>5.27
Sprint
>5.27
Sprint
>5.27
Inertial Movement Analysis
Some research suggests that distances covered, whether total or at varying
velocities, neglects to consider stresses imposed through acceleration, deceleration, and
impact forces (McLellan, Lovell, & Gass, 2011; Varley et al., 2012). The addition of
inertial sensors, sometimes referred to as micro technology, in GPS units has allowed for
the measuring of jumps, accelerations, deceleration, COD, and levels of impact. Due to
the high number of changes of direction involved in team sports it has been suggested
that these forces be included when analyzing and compiling the action profiles of field
sport athletes. The integration GPS with accelerometer, gyroscopes and magnetometers
has made the estimation of COD effects possible. Accelerometers are motion sensors that
detect linear acceleration, while gyroscopes are motion sensors that detect angular
velocity. Accelerometers and gyroscopes are often used together because they
complement each other’s ability to provide precise acceleration, deceleration, and COD
data. Magnetometers have a sensor that detects the Earth’s magnetic field and measure
the orientation relative to magnetic North. The combination of these micro sensors allows
for the inertial movement analysis metrics.
Cummins et al. (2013) suggests that in addition to reporting distances traveled at
various velocities and user-defined velocity zones the total number of accelerations and
decelerations of the athletes should be reported. However this information can also be
misleading because uses can set the threshold for what constitutes as an acceleration and
deceleration. Similar to the velocity zones, practitioners are able to set thresholds for low,
medium, high, and very high accelerations and decelerations. The lack of universal
definitions must be taken into consideration when reviewing research.
38
Player Load and Metabolic Power
In addition to the breakdown of IMA data, several GPS analysis companies have
created algorithms that combine accelerometer and gyroscopic data into a single value.
Catapult Sports has named their instantaneous sum of the three axes of acceleration
derived from the combination of accelerometer and gyroscopic data “Player Load”.
Player Load is derived from a mathematical formula that accounts for accelerations and
decelerations in all three dimensions. Player Load has demonstrated a strong relationship
to heart rate, blood lactate levels and rating of perceived exertion (Casamichana,
Castellano, Calleja-Gonzalez, San Roman, & Castagna, 2013; Scott, Lockie, Knight,
Clark, & Janse de Jonge, 2013). Metabolic Power is calculated by an algorithm using
known measured oxygen uptake and accelerometer data to describe energy expenditure
of whole body movements (Walker, McAinch, Sweeting, & Aughey, 2016). The
reliability of metabolic power estimated from GPS based algorithms in field sports is
poor with the typical error of the estimate between GPS derived metabolic power and a
VO2 portable gas analyzer was 19.8% (Buchheit, Manouvrier, Cassirame, & Morin,
2015). Previously, energy expenditure estimations in soccer have been done by video
match analysis, but more research is needed for GPS applications (Osgnach, Poser,
Bernardini, Rinaldo, & di Prampero, 2010). More research is needed to improve the
algorithms used for Player Load and Metabolic Power. For the purpose of this
dissertation, Player Load and Metabolic Power are dependent on individual player
attributes, and therefore not generalizable to position profiling.
39
Global Positioning System Action Profiles in Field Hockey
The GPS and micro technology derived metrics presented above have been used
to quantify the physical output of players during matches (Bloomfield et al., 2007). This
technology has been utilized in team sports to advance the understanding of a variety of
field sports demands including: Soccer (Buchheit, Mendez-Villanueva, Simpson, &
Bourdon, 2010; Wehbe et al., 2014), Rugby Union (Cunniffe, Proctor, Baker, & Davies,
2009), Rugby League (Gabbett, Jenkins, & Abernethy, 2012; McLellan & Lovell, 2013;
McLellan et al., 2011; Sirotic et al., 2011), Rugby Sevens (Granatelli et al., 2014),
Australian Football (Coutts, Quinn, Hocking, Castagna, & Rampinini, 2010; Gray &
Jenkins, 2010; Young, Hepner, & Robbins, 2012), and Field Hockey (Jennings et al.,
2012a; Lythe & Kilding, 2011; Macutkiewicz & Sunderland, 2011; Vescovi & Frayne,
2015; White & MacFarlane, 2014). GPS application through the analyses of action
profiles has enhanced our understanding of the intermittent nature that characterizes team
sports (Spencer, Bishop, et al., 2005). GPS has been used to compare athletes between
positions, age groups and levels of performance. It has also been used to monitor loads
acquired during games and training, in addition to monitoring, comparing games to
practices, and examining the time course of games and tournaments. Before examining
the extensive uses of GPS in field hockey a standard for data collection and analysis must
be defined.
The current practice for GPS analysis has essentially been driven by soccer
literature. Normally soccer players are on the pitch for a full game or do not reenter the
game after substitution. However, in field hockey a high number of substitutions are
allowed. This tactic allows for offensive and defensive pressure to be applied throughout
40
the match, and is used at the elite levels because the strategy relies on an even
distribution of talent throughout team. Rolling substitutions allow players additional
ability to recover off the field, before returning to play. Full game versus time on pitch
analysis alters the description of the work to rest ratio and other time demands of elite
field hockey players (White & MacFarlane, 2013). It has been suggested that elite field
hockey is played at a higher intensity than other team sports with the same relative
distances (Aughey, 2011a), and all previously reported data represents field hockey as an
intermittent high intensity sport (Boyle, Mahoney, & Wallace, 1994; Gabbett, 2010;
Jennings et al., 2012b; Lythe & Kilding, 2011; Polglaze, Dawson, Hiscock, & Peeling,
2014; Spencer, Lawrence, et al., 2004; Spencer, Rechichi, et al., 2005; White &
MacFarlane, 2014). With this understanding it is important to acknowledge the need for
different analysis strategies, and the inability to generalize women’s soccer data to field
hockey.
Action Profile Comparisons
The production of different profiles within sports has drawn attention to the
different running patterns observed at varying levels of the same sport (Jennings et al.,
2012b), throughout a competitive season (Spencer, Bishop, & Lawrence, 2004), through
a tournament scenario (Higham, Pyne, Anson, & Eddy, 2012) and between genders
(Bradley et al., 2014). This information has led to a better understating of long-term
athlete development, and the action profile traits need to perform at a high level within
the sport. To the authors knowledge only one study has compared different levels of field
hockey player’s specifically male national-level Australian player to their international
41
counterparts (Jennings et al., 2012b). The authors determined that international players
cover 13.9% more total distance and 42.0% more distance at high speed running (>15
km/hr) then national players. Within the international population the midfielders
performed the greatest total distance, and achieve the greatest high intensity running
distance when compared to forwards and defenders. The authors also noted a decrease in
total distances from the first to second half of the game for both national and international
players. Macutkiewicz and Sunderland (2011) reported the action profiles of 25 female
field hockey players over 13 international matches, in respect to positional demands.
They attributed the differences in demands to the substitution tactics used by the team.
Macutkiewicz and Sunderland (2011), Spencer, Lawrence, et al. (2004) and MacLeod et
al. (2007) all reported that defender spend more time in low-intensity actions then
midfielder, and forwards. However the exact distances covered are hard to define
between the studies due to the variability in methods used, and the differences in
movement classifications. Comparisons between studies are difficult due to the varying
definitions.
Monitoring
Perhaps the most common, and daily use for GPS within teams sports is for
monitoring purposes. Results from studies using GPS devices to monitor team sport
players during competitions have provided clearer guidelines for training practices, and
the monitoring of practices has led to a better understanding of player movement needs.
The GPS devices also allow for team (Gabbett et al., 2012) and positional (Bloomfield et
al., 2007) based monitoring during competition and practice. The ability to monitor
42
individuals allows sport scientists to examine the influence of training volume, intensity,
and frequency on athletic performance, specifically as an external marker. Generally
performance abilities improve as training volume, intensity, and frequency increase, but
negative adaptations to exercise training are dose related. High incidence of illness and
injury occurring when training loads is too high, resulting in overtraining. GPS
monitoring can be used to assist in proper training prescriptions (Casamichana et al.,
2013; Scott et al., 2013). The use of GPS as a monitoring tool has provided insight into
weekly or day- by-day external load management (Scott et al., 2013). This information
can then be used to improved periodization, and optimize the performance in training and
games, and may help the detection of injury, fatigue and overtraining (Cummins et al.,
2013).
Game vs. Practice
Monitoring procedures have developed into comparing action profiles of games,
practices, and drills within practices. Gabbett et al. (2012) examined the demands of
rugby league competitions and compared those demands to tradition rugby league
practices. They found that the high intensity physical demands of competition were not
well matched by those observed in traditional conditioning, repeated high-intensity effort
exercise, and skills training activities. Individual drills and small-sided games have been
examined to provide a better understanding of a player’s actions under different
constraints. Gabbett and Mulvey (2008) investigated the movement patterns of smallsided games used in soccer training and compared them to different levels of competition.
They found that while small-sided games offer players opportunities to engage in
43
movement patterns similar to those in games they often lack the high-intensity repeated
sprints performed in games. Compiling the available drill, and small-sided game research
produced by GPS has enabled coaches to design and implement conditioning practices
that stress the appropriate physical requirements of the sport. This information provides
insight for coaches on the modifications needed for training action’s to better prepare
rugby players.
Similar small game vs. practice research has been done with men’s and women’s
field hockey drills with the intent of providing coaches with a better understanding of the
activities occurring during game based training. Gabbett (2010) examined 19 training
appearances and 32 games from an elite population of women’s field hockey players, that
included or members of the Australia women's national field hockey team. The data were
examined for positional profiles during both game based training practices and
competitions. Only game based training session that included minimal coaching was
included. The researchers found that the time spent in specific velocity zones during the
small-sided games training didn’t reflect the action profiles of the matches. With this
information the coaches and athletes examined can then modify the training group size,
drill design, or drill complexity to better simulate the demands of competition. However
without the specific details about the drills performed during the training sessions the
time spent in varying velocity zones is limited in its usefulness for other coaches,
White and MacFarlane (2014) used GPS data from the men’s Scottish National
Team, and 75-competition analysis from 8 games, and 37 training analysis from 4
practice sessions. The practice data ware segmented into four categories: running,
technical, tactical, and small-sided games, providing insight into the training sessions
44
themselves. The researchers also stated the purpose of the drills was technical and tactical
development in preparation for competition, while running was designated as player
conditioning. Similar to Gabbett (2010), White and MacFarlane (2014) attempted to
compensate for the start and stop nature of practice, which is believed to decrease the
overall intensity. Their GPS data breakdown including omitting breaks in-between drills.
They found that even though training duration was longer for practices than matches the
sprinting and high intensity distances were not different between games and practices.
The authors included descriptions of the practices and training competed, and concluded
that training environments can be manipulated to produce similar game action profiles.
When compiling game vs. practice research it is easy to appreciate the multiple
variables in training that can produce large differences in the reported distances covered
and high intensity actions. Without the context of a training plan and the constant
monitoring of training load, the nuisances of the specificity of training are lost. It can
only therefore be generalized that under the right conditions drills can provide an
adaptive stimulus resembling those recorded in games.
Fatigue
GPS has been used to examine the construct of fatigue in field sports, with the
rationale that fatigue manifests as a decrease in performance. Researchers approached the
concept of in game fatigue by splitting the data into distinct time frames to establish
whether work rate varies with time or different tasks. Segments have been broken down
with in games for first and second halves (Bradley & Noakes, 2013; Granatelli et al.,
2014), by quarters (Coutts et al., 2010), and into 5 minute (Bradley & Noakes, 2013), and
45
1 minute (Granatelli et al., 2014) segments. Researchers have also examined the changes
in action profiles across tournaments (Higham et al., 2012; Spencer, Rechichi, et al.,
2005). The premises are based on the comparison of the overall work rate under different
time constraints and the correlations of work rate with indices of the fatigue.
However data with different teams have shown confounding results. Individual
teams and players pace differently (Black & Gabbett, 2014), while substitution strategies
(Bradley & Noakes, 2013), styles of play (Rampinini, Impellizzeri, Castagna, Coutts, &
Wisloff, 2009), opposition (Aughey, 2011b; Bradley & Noakes, 2013), match score
(Black & Gabbett, 2014; Bradley & Noakes, 2013), and level of player (Rampinini et al.,
2009), impact work rate. Assuming fluctuations in work rate are due to fatigue is
extremely misleading. It has recently been shown that pacing may occur in prolonged
high-intensity intermittent team sports (Duffield, Coutts, & Quinn, 2009). Which could
skew the interpretation of GPS-based fatigue. The introduction of substitutions must also
be considered, due to the various rules within different sports. The information available
on pacing measured by GPS comes from sports with limited substitutions like rugby
(Black & Gabbett, 2014) and soccer (Gabbett, Wiig, & Spencer, 2013), and therefore
might not be practical to apply to an unlimited substitution sport like field hockey. With
the above research taken into consideration this dissertation plans to only report GPS data
as athlete profile data, or simply the actions performed by the position or athlete during
the game.
Spencer, Rechichi, et al. (2005) examined an international men’s tournament
consisting of three games player within four days. Across the games the time spent
standing increased, and the time spent in the jogging decreased. The authors concluded
46
that the time between games was to short too allow for recovery, and the subsequent
games were negativity impacted. The authors used a positional based analysis and
accounted for time of pitch. The author note that substitution strategy, playing style of
opposition, changes in tactic, and environmental conditions could have influenced the
results. It is also important to note that each game was won or lost by a one-point goal
differential. Regardless residual fatigue could be a factor in tournament scenarios for
international field hockey players, and recovery techniques should be utilized to
encourage the dissipation of fatigue. This is in contrast to other studies, which show no
changes across a tournament. However direct comparison between studies is difficult.
Problems/Considerations/Match Variability
It is important to note the match-to-match variability that occurs for action
profiles (Gregson, Drust, Atkinson, & Salvo, 2010). There are numerous factors that may
contribute to the variations reported within GPS team sport data. Previously mentioned
factors include: substitution strategies (Bradley & Noakes, 2013), styles of play and
tactics (Rampinini et al., 2009), opposition (Aughey, 2011b; Bradley & Noakes, 2013),
match score (Black & Gabbett, 2014; Bradley & Noakes, 2013), and level of player
(Rampinini et al., 2009), impact work rate. This is combined with the fact that past
studies could be outdated, and lack relevance to the current game.
Summary
The goal of this chapter was to provide a theoretical basis for the experimental
chapters of this dissertation. It provided an overview of the existing TMA literature,
47
specifically GPS. GPS literature conducted on field hockey players in reference to
positions, age groups and levels of performance, games and practices were examined.
Sport specific GPS units provide coaches and sport scientists with action profiles
produced by athletes. The validity and reliability of GPS systems when assessing
movement demands of team sports are influenced by several factors: GPS sampling
frequency, speed, distance, and path of movements, which must be taken into
consideration when examining and conducting research. Information in produced from
GPS, accelerometer, gyroscope, and magnetometer combined systems. The information
gained from these systems includes total distance, distance per minute, distance and time
in velocity zones, time on pitch, COD, acceleration, and deceleration.
The intent of this dissertation was to use GPS derived metrics to examine the
action profiles produced by elite level field hockey players before and after a game
formatting rule change. The purpose was to provide coaches and sport scientist with a
depiction of the changes caused by the 2015 FIH game formatting rule change.
48
CHAPTER 3
POSITIONAL AND MATCH ACTION PROFILES OF ELITE WOMEN’S FIELD
HOCKEY PLAYERS
Authors: Heather A Abbott1, Kimitake Sato1, Satoshi Mizuguchi1, Dave Hamilton2,
Michael H. Stone1
Affiliations: Center of Excellence for Sport Science and Coach Education Department of
Exercise and Sport Sciences, East Tennessee State University, Johnson City, TN, USA1
USA Field Hockey, Manheim, PA2
Prepared for Submission to Journal of Strength and Conditioning Research
49
ABSTRACT
The purpose of this study was to examine the action profiles of the US Women’s
National Field Hockey Team recorded with GPS during the 2014 Champions Challenge
and World Cup. Eighteen female international field hockey players (age 25.5 ± 0.17
years, body mass 61.1 ± 4.7 kg) were tracked over thirteen international matches, giving
a total of 195-player match analysis. The descriptive data (playing time, odometer,
absolute distances and relative percent distances covered zone 1 standing (0-.16 m/s),
zone 2 walking (.19-1.6 m/s), zone 3 jogging (1.69-3.05 m/s), zone 4 running (3.08-4.16
m/s), zone 5 fast running (4.19-5.27 m/s), zone 6 sprinting (>5.27 m/s), and velocity zone
efforts, maximum effort distance, and average effort distance in zones 4,5, and 6) in
reference to both player and position are presented as the mean ± standard deviation
(SD). A series of ANOVAs were used to compare the relative percent distances covered
between positions, and a paired sample t-test was used to compare the relative percent
distances for the team between each half. The findings revealed that defenders work at a
lower meter per minute (m/min) when compared with all other positions, and that
forwards, midfielders, and screens perform similar m/min during a game. It is possible
for elite level field hockey players to maintain high-intensity actions in zone 6 from the
1st half to 2nd half of the game by increasing actions in zones 1 and 2, and decreasing
actions in zones 4 and 5.
KEY WORDS: Global Positioning System, Time Motion Analysis, Team Sports,
Substitutions, Field Sports
50
INTRODUCATION
Field hockey is a multifactorial team sport, with interspersed high-intensity activities that
are multi-directional in nature; it requires significant levels of aerobic and anaerobic
power, strength, and power (5, 8, 24). Field Hockey has undergone fairly rapid and
radical changes within the past ten years. For example, the arrival of synthetic playing
surfaces (19), modernized sticks (1), and new rule alterations such as the self-start (28),
have changed the physiological requirements of the game.
Time-motion analysis techniques, such as global positioning systems (GPS), have
provided valuable information regarding the action profiles of field hockey players (7, 8,
10, 11, 17, 18, 20, 31, 33, 34). It is important to note that GPS does not measure the
demands of competition, but rather the outputs of players. GPS units have been shown to
be reliable and valid for measuring distance and velocity with the accuracy improving as
sampling frequency increases (12, 21, 29). A high sampling frequency is especially
important for intermittent high-intensity sports such as field hockey, which involve
numerous high-speed and short distance actions.
Early field hockey publications might not accurately represent the actions of field players
due to the recent rule changes (28), and varying analysis procedures used (32). It has
been previously noted that discrepancies exist when reporting locomotor activities (eg.
walking, jogging, running) with TMA (8), and making a comparison between studies is
difficult. Moreover, inconsistencies in data processing also create discrepancies in
reported measures, specifically when comparing full game time to the time on pitch.
51
Different GPS analysis procedures can influence the time-dependent measures reported
by GPS. This is specifically evident within sports with rolling substitutions, such as field
hockey. The tactic of rolling substitutions creates offensive and defensive pressure
throughout the match and is utilized at the elite level. This strategy relies on an even
distribution of talent throughout the team. The efficient use of this strategy affects an
athlete’s ability to produce repeat high-intensity actions. Previous research that uses full
game GPS rather than time on pitch analysis, resulting in a misrepresentation of the time
dependent factors provided by GPS. Time on pitch analysis produce higher relative
distance results then analysis of full games (124 m/min vs. 78 m/min, p ≤ .0001)
(32). However, absolute measures such as distances covered, the number of efforts and
distance of efforts are unaffected (32). In addition to varying analysis methods, the level
of players (11), level of competition (14), age of players (30), gender of players (6),
match status (15), and tactical style (23) utilized by the team has the potential to affect
the reported positional profiles. Therefore, it is important to characterize the style of play
implemented at the highest level in the US.
To the author’s knowledge, the unique and aggressive style of play used by the US
Women’s National Team has not been profiled. Therefore, the aim of this study was to
assess the action profiles of elite women’s field hockey players in the US by team, player,
and position across a game, and to determine if action profiles differed between the 1st
and 2nd halves of a match.
52
METHODS
EXPERIMENTAL APPROCACH TO THE PROBLEM
GPS technology allowed for the measurement of absolute variables (total distance
covered, distance covered in zones 1,2,3,4,5, 6, and velocity zone efforts, maximum
effort distance, and average effort distance in zones 4,5, and 6), and relative variables
(m/min, percent of the total distance covered in zones 1, 2, 3, 4, 5, and 6) from the US
Women’s National Field Hockey Team. The relative variables were used to determine the
differences in action profiles because not all players received the same amount of playing
time. The relative action profiles were analyzed for differences between positions across
a game, and between 1st and the 2nd half for the team.
ATHLETES
Eighteen members of the US Women’s National Field Hockey Team age (25.54 ± .17
years, body mass 61.12 ± 4.66 kg) participated in this study. The team finished with a
World Ranking of 8th in December 2014. Goalies were removed from GPS analysis, but
included in physical player profiles. GPS recordings were collected from the 2014
Champions Challenge and 2014 World Cup (Table 1). The team members remained
constant between the two international tournaments. The US Women’s National Team
finished the 2014 Champions Challenge with the 1st ranking, sustaining 5 wins, and 1
loss. During the 2014 World Cup, the team finished with a 4th place ranking, after
winning 4, tying 1, and losing 2 games. The East Tennessee State University Institutional
review board approved this study and each athlete read and signed a written informed
consent document.
53
PROCEDURES
All GPS data collection was performed on outdoor water based turf. Data was collected
during the 2014 Champion’s Challenge in Glasgow, Scotland and the 2014 Rabobank
Hockey World Cup in The Hague, Netherlands, giving a total of 195 player-match files
for analyses from thirteen games. All GPS data was acquired using Catapult Minimax X
(Catapult Innovations, Melbourne, Australia), which samples at 10 Hz. This device had
previously been shown to be valid for distance and velocity during linear and sport
specific movements (12, 13, 21, 29). The GPS units were worn within a specifically
designed bib with a neoprene pouch. The bibs hold the units between the shoulder blades
of the athlete. Each player wore the same device across all matches. All players were
acclimated to wearing the GPS units as a result of previous practice, and game
monitoring.
The GPS data was downloaded using the manufacturer-supplied software (Logan Plus,
version 5.1.7). The downloaded data was edited to only include time spent on the field of
play, during the regulation game time. The data were exported to Microsoft Excel for
further analysis. Individual player action profiles were identified and players were
categorized into four positions: forwards, midfielders, screens, and defenders. Similar
player categorization for field hockey has only been utilized in one prior study (20).
Players were substituted into the same positions; therefore, each play only contributes to
one positional group. Time on the substitution bench was not analyzed or included in any
calculations of rest or low-intensity exercise. The velocity zones were chosen from
previously published international field hockey research (stand 0-.16 m/s, walk .19-1.6
54
m/s, jog 1.69-3.05 m/s, run 3.08-4.16 m/s, fast run 4.19-5.27 m/s, and sprint>5.27 m/s)
(17, 18). The velocity zones were originally adapted from definitions provided by
Bangsbo and Lindquist (2) and Lothian and Farrally (16). The studies consisted of video
based time motion analysis performed on soccer (2) and field hockey (16) populations.
STASTICAL ANAYLSIS
Each individual athlete’s data files were combined across games, and the mean ± SD was
calculated for absolute and relative measures. Absolute data was constructed using two
varying methods: a player method, and a positional method. The player absolute data
represents the mean ± SD for the total number of players used in that positional group.
The absolute position data represents the work performed by the positional group
regardless of the number of players substituting into that position. The players were
separated into four different positions: forwards, midfielders, screens, and defenders, and
player and positional representation were constructed. The benefit of positional reporting
is that it negates the substitution strategy used by the US Women’s National Team, and
presents the mean ± SD of the position regardless of how many players played in that
positional group during the game. Reporting total distances is in the context of player or
position is limited in its usefulness because distance covered is directly related to the
amount of time a player spent on the pitch. Therefore, relative percent distances covered
were used to examine the differences between positions and the team changes across the
game. Total distance covered was considered relative to the total number of minutes
played for each athlete (meters per minute), and the total distance covered in each
velocity zone (1-6) was considered relative to the total distance covered, resulting in the
55
relative percentage distance covered for each velocity zone.
The number of efforts, maximum, and average distance covered in velocity zones 4, 5,
and 6 are presented as mean ± SD. A paired sample t-test was run to compare 1st vs. 2nd
half relative data. The difference between playing positions across a game was analyzed
using a series of 1-way ANOVAs, with post hoc Bonferroni tests. All statistical
procedures were performed with SPSS (IBM, New York, NY). Statistical significance
was set at p - value <0.05, and all data is reported as mean ± SD. The effect size was
calculated from the ratio of the mean difference to the SD. Cohen’s d effect sizes (d)
were assessed using the following criteria: trivial <. 2, small .2-.6, moderate .61-1.20,
large 1.21-2.00 and very large 2.01-4.0 (9).
TERMS/DEFINATIONS
Player = Absolute action profiles divided by the total number of players (team data were
divided by 16, forwards were divided by 4, midfielders were divided by 5, screens were
divided by 3, and defenders were divided by 4)
Position = Absolute action profiles divided by the number of positions on the field (team
data were divided by 10, forwards were divided by 2, midfielders were divided by 3,
screens were divided by 2, and defenders were divided by 3)
Relative = m/min, Percent of distance covered in velocity zones 1, 2, 3, 4, 5, and 6
relative to the total distance covered.
56
RESULTS
DESCRIPTORS
Playing time varied based on the player, and position (Table 2). No inferential statistics
were applied. However, simple numerical comparisons revealed defenders and screens
appear to average the highest playing time per match, followed by the midfielders, and
forwards. The absolute action profile characteristics of the games are presented in Table
3. Action profiles were reported in both the context of position and player. Player profiles
represent the absolute distances covered by the members of the US Women’s National
Team. Player data is specific to the teams’ personnel, tactics, substitution strategy, and
opposition. A positional breakdown of the data provides coaches with the absolute total
distance covered by each position on the team. This information is still specific to the
formation that the US Women’s National Team uses (2 forwards, 3 midfielders, 2
screens, and 3 defenders).
57
Table 1. Tournament Schedule and Outcome
Tournament
Finish
Location
Champions
1st
Glasgow,
Challenge I 2014
Scotland
Rabobank Hockey
World Cup 2014
4th
The Hague,
Netherlands
Date/Opponent/Score
4/27/2014 4/28/2014 4/30/2014 5/1/2014 5/3/2014 5/4/2015
ESP
RSA
IRL
INDIA
ESP
IRL
3 to 1
1 to 2
3 to 0
1 to 0
3 to 1
3 to 1
6/1/2014 6/3/2014 6/6/2014 6/8/2014 6/10/2014 6/12/2014 6/14/2014
ENG
ARG
CHN
GER
RSA
AUS
ARG
2 to 1
2 to 2
5 to 0
4 to 1
4 to 2
2 to 2
1 to 2
Table 2. Positional Playing time
Position
N
Average Minutes
Forwards
4
38.25
Midfielders
5
41.40
Screens
3
57.00
Defenders
4
57.50
58
SD
1.26
8.26
3.00
11.41
Table 3. Champions Challenge and World Cup Player and Position Action Profiles
N
Odometer (m)
Player
16 3701
Position
10 5921
Forward Player
4
3091
Forward Position
2
6181
Midfielder Player
5
3264
Midfielder Position 3
5441
Screen Player
3
2293
Screen Position
2
3439
Defender Player
4
5912
Defender Position 3
7882
Data Presented as mean ± SD
±
±
±
±
±
±
±
±
±
±
1675
1675
1094
1094
985
985
701
701
1378
1378
Zone 5 Fast Zone 6
Zone 1 StandZone 2 Walk Zone 3 Jog
Zone 4 Run
Run 4.19- Sprint >5.27 m/ min
0-.16 m/s
.19-1.6 m/s 1.69-3.05 m/s 3.08-4.16 m/s
5.27 m/s
m/s
36
58
28
57
31
51
21
32
61
82
±
±
±
±
±
±
±
±
±
±
22
22
12
12
13
13
9
9
26
26
891
1426
716
1431
802
1337
563
844
1425
1901
±
±
±
±
±
±
±
±
±
±
443
443
258
258
291
291
254
254
467
467
1270
2032
1008
2017
1119
1865
804
1206
2071
2761
59
±
±
±
±
±
±
±
±
±
±
604
604
350
350
355
355
324
324
511
511
923
1477
782
1565
783
1305
543
814
1523
2031
±
±
±
±
±
±
±
±
±
±
442
442
238
238
232
232
136
136
390
390
432
691
414
827
378
630
260
389
648
864
±
±
±
±
±
±
±
±
±
±
191
191
175
175
141
141
27
27
165
165
151
241
151
301
151
252
107
160
183
244
±
±
±
±
±
±
±
±
±
±
77
77
120
120
83
83
31
31
49
49
117
188
117
234
120
200
118
177
114
152
±
±
±
±
±
±
±
±
±
±
9
9
11
11
11
11
11
11
9
9
Table 4. Absolute Number of efforts, Maximum and Average Distance covered in Top 3 Velocity Zones
Velocity Zone 4
Velocity Zone 5
Velocity Zone 6
Team
Forwards
Midfielders
Screens
Defenders
Velocity
Zone
Efforts
104 ± 24
91 ± 4
97 ± 27
132 ± 12
104 ± 30
Max
Effort
Distance
(m)
50 ± 6
49 ± 5
53 ± 8
54 ± 0
46 ± 5
Average
Effort
Distance
(m)
13 ± 1
13 ± 1
14 ± 1
14 ± 0
12 ± 1
Max
Velocity
Effort
Zone
Distance
Efforts
(m)
50 ± 13 37 ± 5
54 ± 3 36 ± 2
52 ± 16 39 ± 4
62 ± 8 44 ± 2
37 ± 11 32 ± 2
60
Average
Effort
Distance
(m)
13 ± 1
12 ± 1
13 ± 1
14 ± 1
12 ± 1
Velocity
Zone
Efforts
15 ± 5
17 ± 1
16 ± 5
16 ± 4
8 ± 3
Max
Effort
Distance
(m)
38 ± 6
42 ± 4
40 ± 5
40 ± 4
29 ± 2
Average
Effort
Distance
(m)
16 ± 1
17 ± 1
17 ± 1
17 ± 1
15 ± 0
POSITION DIFFERENCE
Main effect data is represented in table 6, and the p valve and d for all the interactions is
presented in Table 7. Results of the ANOVAs showed statistically significant differences
in zones 2, 3, 5, and 6 and m/min (Table 6). The post hoc tests indicated that forwards
and midfielders did not had any statistically significant differences in relative percent
distance covered in each zone and m/min (Table 7). Screens had only one statistical
difference when compared to forwards, in which screens covered statistically less relative
percent distance covered in zone 6 than forwards (Tables 5 and 7). On the other hand,
defenders covered statistically greater relative distance in zones 3 and 2 than forwards
and midfielders, respectively, but statistically shorter relative percent distance covered in
zones 5 and 6 and m/min than forwards and midfielders. Defenders also covered shorter
relative percent distance in zone 5 and m/min than screens.
61
Table 5. Descriptive Data for Relative Zones
Relative Zones
Zone 1 0 -.16m/s
Zone 2 .17 - 1.68 m/s
Zone 3 1.69 - 3.07 m/s
Zone 4 3.08 - 4.18 m/s
Zone 5 4.19 -5.27 m/s
Zone 6 > 5.27 m/s
m/min
Team
1.06 ±
24.12 ±
33.11 ±
24.89 ±
12.04 ±
4.75 ±
120.58 ±
Note: All data is mean +/- SD
Forwards
Midfielders
Screens
Defenders
0.31
0.91 ± 0.13 0.96 ± 0.31
1.04 ± 0.15 1.37 ± 0.43
4.83 22.62 ± 2.00 21.39 ± 4.22 22.77 ± 1.43 30.03 ± 4.87
3.61 29.94 ± 1.72 32.36 ± 1.65 32.73 ± 4.99 37.49 ± 1.46
3.23 25.53 ± 1.94 26.12 ± 2.57 26.78 ± 2.35 21.28 ± 3.46
3.04 14.00 ± 0.42 13.68 ± 1.35 12.65 ± 2.33 7.58 ± 1.89
2.04
6.96 ± 0.76 5.47 ± 1.16
3.98 ± 1.42 2.21 ± 0.72
10.25 125.75 ± 3.86 125.20 ± 10.89 123.67 ± 1.53 107.25 ± 5.74
Table 6. ANOVA Main Effect for Position
Relative Zones
Df
F
Zone 1 0 -.16m/s
(3,12)
2.038
Zone 2 .17 - 1.68 m/s
(3,12)
4.832
Zone 3 1.69 - 3.07 m/s
(3,12)
6.329
Zone 4 3.08 - 4.18 m/s
(3,12)
3.398
Zone 5 4.19 -5.27 m/s
(3,12) 14.901
Zone 6 > 5.27 m/s
(3,12) 15.691
m/min
(3,12)
6.119
sig.
0.162
0.020
0.008
0.054
0.000
0.000
0.009
62
Table 7. Relative Percent Distance covered in Velocity
Zones Between Positions
Forward vs.
Forward vs.
Mid
Screen
Relative Zones
P
ES
P
ES
Zone 1 0 -.16m/s
1.000 0.214
1.000 -0.921
Zone 2 .17 - 1.68 m/s
1.000 0.372
1.000 -0.084
Zone 3 1.69 - 3.07 m/s 1.000 -1.436
1.000 -0.749
Zone 4 3.08 - 4.18 m/s 1.000 -0.260
1.000 -0.580
Zone 5 4.19 -5.27 m/s
1.000 0.317
1.000
0.807
Zone 6 > 5.27 m/s
0.313 1.516 *0.016
2.610
m/min
1.000 0.067
1.000
0.709
* Significance, ES = Effect Size, P= p-value
Forward vs.
Midfielder vs.
Defender
Screen
P
ES
P
ES
0.279 -1.450 1.000 -0.320
0.082 -1.991 1.000 -0.436
*0.007 -4.730 1.000 -0.102
0.259
1.514 1.000 -0.267
*0.001
4.697 1.000 0.542
*0.000
6.443 0.424 1.148
*0.021
3.783 1.000 0.197
63
Midfielder vs.
Screen vs.
Defender
Defender
P
ES
P
ES
0.356 -1.094 0.983 1.031
*0.024 -1.896 0.135 -2.024
0.062 -3.293 0.175 -1.293
0.133
1.586 0.114 1.857
*0.000
3.717 *0.007 2.390
*0.003
3.391 0.258 1.578
*0.018
2.062 *0.069 3.910
1st VS. 2nd HALF
The team differences determined as the relative percent distance cover in each velocity
zone were used to determine if the team expressed any changes in action profile from the
1st to the 2nd half of the game. The results showed a moderate increase in the percent of
distance cover in zones 1 and 2 and a moderate decrease in the percent distance covered
in zones 4 and 5 from the 1st half to the 2nd half (Table 8). Trivial decreases were
produced in zones 3 and 6, and a moderate decrease was noted in m/min. However, no
relative data showed statistically significance difference between the 1st and 2nd half of
the game.
64
Table 8. Team Changes Relative Percent Distance covered form the 1st to 2nd Half
Std. Error
pRelative Zones
Periods
N
Mean ± SD
Mean
value
Zone 1 0 -.16m/s
1st half 16 0.94 ± 0.20
0.05
0.107
2nd half 16 1.14 ± 0.44
0.11
Zone 2 .17 - 1.68 m/s
1st half 16 22.34 ± 3.48
0.87
0.105
2nd half 16 25.11 ± 5.66
1.42
Zone 3 1.69 - 3.07 m/s
1st half 16 32.54 ± 2.73
0.68
0.848
2nd half 16 32.30 ± 4.08
1.02
Zone 4 3.08 - 4.18 m/s
1st half 16 25.11 ± 2.43
0.61
0.172
2nd half 16 23.51 ± 3.85
0.96
Zone 5 4.19 -5.27 m/s
1st half 16 12.47 ± 2.71
0.68
0.221
2nd half 16 11.14 ± 3.29
0.82
Zone 6 > 5.27 m/s
1st half 16 4.82 ± 2.01
0.50
0.688
2nd half 16 4.52 ± 2.17
0.54
m/min
1st half 16 121.75 ± 11.50
2.87
0.147
2nd half 16 116.06 ± 10.08
2.52
65
Effects
Size
Qualitative
Descriptor
-0.59
Moderate
-0.59
Moderate
0.07
Trivial
0.49
Moderate
0.44
Moderate
0.14
Trivial
0.53
Moderate
DISCUSSION
This study investigated the action profiles of US Women’s National Team during the
2014 Champions Challenge and 2014 World Cup. In this study, the defenders appear to
have spent greatest time on the pitch (Table 2) and covered the highest absolute distances
by player and position (Table 3). Playing time is important in determining the absolute
distances covered during a match (20). Defenders tactical position allows them to
moderate and control the field. It is the goal of a defender to organize and control space
on the pitch. These positional requirements allow the defenders to recover on the field
resulting in less substitution and more time spent on the pitch. Forwards appear to have
spent the least amount of time on the field (Table 2). This is most likely due to high
intensity and repeated runs performed by the forwards resulting in more frequent
substitutions. Playing time differences for positions have previously been noted by
Macutkiewicz and Sunderland (18). A difference in playing time between positional
groups is a product of the unlimited substitution rule.
The team action profiles of the US Women’s national team showed that players covered a
16.8% of the total distance in zones 5 and 6. This is higher than previously reported
6.1%, for men’s by Lythe and Kilding (17), and 6.4% for women’s by Macutkiewicz and
Sunderland (18) under the same velocity zone definitions. When comparing positional
differences for relative data, the results showed that defenders covered less relative
percent distance than forwards in zones 3, 5, 6 and m/min. Previous studies reported that
defenders cover less distance at higher intensity than forwards, midfielders, and screens
(5, 8, 10, 18), which represent the link of action profiles to the tactical demands specific
66
to positional roles. Previous research (8) has suggested position specific conditioning
based on the differences reported between positions. However, the percent difference in
high intensity velocity zones and variation in significance between zones should be
considered to justify the need for position specific conditioning. When examining the
mean ± SD for relative percent distance covered in zones 3, 5 and 6 between forwards
and midfielders the relative percent distances and m/min are similar. No statistical
differences existed between forwards and midfielders. The lack of significance
contradicts the suggested need for prescription of additional position-specific highintensity-type training between forwards, midfielders and screens.
Examination of the average and maximum effort distance covered by the team in velocity
zones 4, 5, 6 suggests that an average effort is between 13 and 16 meters, while a max
effort ranges between 40 and 50 meters (Table 4). Short-duration, high-intensity efforts
replicating match requirements should be used during training (26). The variation of the
percent relative distance covered in the varying velocity zones (Table 5) demonstrates the
need for a balanced development of aerobic and anaerobic capacities for field hockey
players. This agrees with previous research that concluded field hockey to be an
aerobically demanding sport with frequent anaerobic efforts (8, 24). Athletes need to
possess the ability to perform intermittent high-intensity actions and recovery, from those
activities quickly because sprinting is not evenly distributed throughout a game (26).
Increasing an athlete’s top speed can allow them to produce higher relative speeds during
a match, while increasing athletes’ aerobic capacity will allow for a higher critical speed
to be maintained while recovering between intermitted bouts of high-intensity (3, 4, 30).
67
When comparing the 1st to the 2nd half for team relative percent distances no statistical
significance was concluded. More slightly more distance was covered in the second half
in zones 1, 2, and 3 (Table 8). This agrees with the increase in lower work intensity (011km/hr) reported by Spencer, Rechichi, Lawrence, Dawson, Bishop and Goodman (27)
for men’s field hockey. The results also agree with the decrease in zone 4 from the 1st to
2nd half. It seems that players compensate for the high intensity of the game by decreasing
their moderate-intensity actions and increasing their low-intensity actions to allow for the
maintenance of high-intensity actions from the 1st to 2nd half. The increase in lowintensity actions may be needed to assist in the ability to recover from high-intensity
actions. Optimal utilization of rolling substitutions may cease the increase in low
intensity actions from the 1st to 2nd half by allowing players to recover off the pitch. It has
been suggested that high-intensity running is a much more important indicator of success
than total distances covered (22, 25, 26). It is important to note that the difference in
relative percent distance covered across halves is not considered statistically for any
relative data, this is likely due to the using a high number of rolling substitutions
throughout the match, which is speculated to permit the maintenance of intensity.
PRACTICAL APPLICATION
High-intensity sprints taking place in zone 6 only make up a small portion of match
actions (5-8%) in elite field hockey. Actions performed in zones 4 (12-13%), and 5, (2526%), represent a greater portion of the game then zone 6. Due to the intermittent nature
of field hockey, there is a necessity to repeat high-intensity actions throughout a game,
and the ability to perform those actions is recognized as a key determinant of success (3).
68
Unfortunately high-intensity actions create fatigue. Positional differences indicate that
defenders covered less relative percent distance in zones 5 and 6 when compared to
forwards and midfielders. However, the percent difference covered in velocity zones 5
and 6 is not great enough to justify positional specific conditioning.
When comparing the 1st half to the 2nd half of the game, tends in the data show players
increasing their percent distance covered in low-velocity zones, and decreasing their
percent distance covered in moderate-velocity zones, presumably allowing them to
maintain the percent distance covered in the high-velocity zones. However, no
statistically significant results were produced were in any relative velocity zone when
comparing the 1st half to the 2nd half of the game. It is clear that field hockey players
require a high level of aerobic fitness to cover the reported absolute distances (Table 3),
and recover from high-intensity efforts (Table 4 and 5).
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74
CHAPTER 4
WOMEN’S ELITE FIELD HOCKEY ACTION PROFLIES POST 2015 RULE
CHANGE
Authors: Heather A Abbott1, Kimitake Sato1, Satoshi Mizuguchi1, Dave Hamilton2,
Michael H. Stone1
Affiliations: Center of Excellence for Sport Science and Coach Education Department of
Exercise and Sport Sciences, East Tennessee State University, Johnson City, TN, USA1
USA Field Hockey, Manheim, PA2
Prepared for Submission to International Journal of Sport Science and Coaching
75
Abstract
The object of this paper was to analyze the action profiles of the US Women’s’ National
Field Hockey Team using GPS technology, after the 2015 International Hockey
Federation (FIH) game time formatting change. The purposes were to present coaches
with an elite level standard of the new game, and to examine action profile changes
across the game. Actions were recorded over two international tournaments, during
twelve games, resulting in one hundred forty-eight files for sixteen athletes (age 20.5 ±
2.5 years, body weight 60.6 ± 5.2 kg). The descriptive data was presented for playing
time, odometer, absolute distances covered zone 1 standing (0-.16 m/s), zone 2 walking
(.19-1.6 m/s), zone 3 jogging (1.69-3.05 m/s), zone 4 running (3.08-4.16 m/s), zone 5 fast
running (4.19-5.27 m/s), and zone 6 sprinting. Total distance covered was also considered
relative to the total number of minutes played for each athlete ((meters per minute
(m/min)). A total distance covered in each velocity zone (1-6) was also considered
relative to the total distance covered in percentage. A series of repeated measures
ANOVAs with post hoc tests revealed statistical significance (p>.05) for team data
relative data across quarters. Meters per minute in 1st vs. 3rd and 1st vs. 4th quarters had
large effect sizes (1.2-2.0), and 1st vs. 2nd quarters had moderate (0.61-1.2) effects sizes.
Statistical significance was also found for velocity zones 4 and 5 when examining 1st vs.
2nd, 1st vs. 3rd, and 1st vs. 4th quarters. Another series of repeated measures ANOVAs on
positional data revealed that all positions produce the greatest m/min during quarter 1,
and relative distance covered in zones 1 and 2 statistically increased throughout the game.
Key Words: Global Positioning System, Time Motion Analysis, Team Sports,
Substitutions, Game Formatting
76
Introduction
In the United States, women’s field hockey is played from under twelve developmental
programs to college level, and an athlete’s career can culminate with the highest level of
play on the Women’s National Team. Historically the game has been played during two
thirty-five minute halves, with a ten minute half time, and a continually running clock
(FIH, 2012). New match duration and clock stoppage for corners and goals have been
implemented. The change in the game time requirements and clock management rules
has created a new match format at the international level. The new rule reads: “These
currently include Match Periods (4 x 15 Minute Quarters and time stoppages for the
award of Penalty Corners and Goals), the Penalty Corner Countdown Clock and Video
Umpire, which will only be used at FIH World level Tournaments…” (FIH, 2014)
Athletes are provided with two minutes of rest between quarters 1st and 2nd, and 3rd and
4th, with a ten minutes half time in-between quarters 2nd and 3rd. The rule changes are
expected to impact physiological and tactical demands of the game. Therefore, the
purpose of this study was to provide coaches with a descriptive profile of elite field
hockey players under the new FIH match formatting. Additional analysis was done to
determine the team and positional differences between the four quarters of match play. .
Findings of this study are intended to aid in developments of training methods specific to
the new match formatting
77
Methods
Athletes
Seventeen elite level field hockey players (age 20.5 ± 2.5 years, body weight 60.6 ± 5.2
kg) participated in this study. Goalies were not included in this study. The East Tennessee
State University Institutional Review Board approved this study. Each athlete read and
signed a written informed consent. A total of seventeen field players’ game action
profiles were examined. Sixteen field players participated in the 2015 World League 3
Semi Finals while only fifteen field players participated in the 2015 Pan American
Games. This was due to the limitation on team size during the Pan American games,
because of its designation as an Olympic qualifier, the team carried fifteen field players
and one goalie due to the bench limitation of sixteen. In comparison sixteen field players
and two goalies were carried during the 2015 World League 3 Semi Finals. Roster
changes across the tournaments resulted in the removal of two field player, and one
goalie from the 2015 World League 3 Semi Finals roster, and the addition of one new
field player to the Pan American games roster. This resulted in seventeen different
athletes involvement in data collection. Players’ files were divided into 4 positional
groups: forwards, midfielder, screens, and defenders.
Data Collection
The action profiles of the US Women’s National Field Hockey Team were measured
using GPS sporting technology. Action profile data was collected using Optimye S5 GPS
units (Catapult Innovations, Team S4, Melbourne, Australia), sampling at 10 Hz. This
device has been shown to be valid for distance and velocity during linear and sport
78
specific movements (Johnston, Watsford, Kelly, Pine, & Spurrs, 2014; Rampinini et al.,
2015; Varley, Fairweather, & Aughey, 2012).
Data was collected during the 2015 World League 3 Semi Finals in Valencia, Spain
during 7 matches, and the 2015 Pan American Games in Toronto Canada during six
matches (Table 1). The US Women’s National Team finished 5th during the World
League 3 Semi Finals, and 1st during the 2015 Pan American Games. The team’s
international ranking culminated in a world ranking of 7th during 2015. All matches were
played on a water based, synthetic turf, which was in compliance with FIH field of play
rules (FIH, 2014). GPS units were positioned between the scapulae in a custom built
harness worn under the uniform. Players wore the same units across both tournaments.
All players were acclimated to wearing the GPS units as a result of previous practice, and
game monitoring. GPS data were downloaded and analyzed using proprietary software
(LoganPlus, version 5.1.7). The downloaded data were edited to only include time spent
on the field of play, during the regulation game time. The data was then exported to a
Microsoft Excel spreadsheet for further analysis.
79
Table 1. Schedule
Tournament
Finish
Location
World
League 3
Semi Finals
5th
Valencia,
Spain
Pan
American
Games
1st
Toronto,
Canada
Games/Opponent/Score
6/11/2015
URU
2 to 0
6/13/2015
RSA
4 to 1
6/14/2015
GER
2 to 2
7/13/2015
URU
7/15/2015
CHIL
7/17/2015
CUBA
5 to 0
2 to 0
12 to 0
80
6/16/2015
IRL
0 to 2
7/20/2015
DOM
REP
15 to 0
6/18/2015
ARG
0 to 3
6/20/2015
IRL
6 to 1
7/22/2015
CAN
7/24/2015
ARG
3 to 0
2 to 1
6/21/2016
ESP
3 to 1
Data Analysis
One hundred and forty-eight game action profiles were collected over twelve matches.
The velocity zones were set in relationship to previously published international field
hockey research (Macutkiewicz & Sunderland, 2011). The velocity zones used were:
zone 1 stand 0-.16 m/s, zone 2 walk .19-1.6 m/s, zone 3 jog 1.69-3.05 m/s, zone 4 run
3.08-4.16 m/s, zone 5 fast run 4.19-5.27 m/s, and zone 6 sprint>5.27 m/s. These specific
action profiles were originally adapted from definitions provided by Bangsbo and
Lindquist (1992) for Men’s international soccer athletes and Lothian and Farrally (1992)
for Women’s National League field hockey athletes.
Each individual athlete’s data files were averaged across games to obtain absolute and
relative measures. This data processing was chosen to account for several missing files
due to a few corrupted data files. The sample size for this study was seventeen, but each
athlete had a varying number of full game files to produce their sample. Players were
divided into four positional groups: forwards, midfielder, screens, and defenders. Players
were substituted into the same positional group, but not always into the same positional
role (ex: right midfielder vs. center midfielder). In order to provide an estimate of
position’s absolute action profiles , data obtained from all players was average across the
number of players playing the particular position at any point in time on the field (e.g.
forward position). This provided an evaluation for each field position, not the individual
players that play the position. This processing procedure was chosen to account for the
variation of the number of players between the tournaments. Team relative action profiles
and positional relative action profiles were then determined for each quarter. It is
81
important to note that for these analyses, not all players played an equal number of
minutes, and the new match formatting caused variation between quarter lengths.
Therefore, a total distance covered was considered relative to the total number of minutes
played for each athlete (m/min), and total distance covered in each velocity zone (1-6)
was considered relative to the total distance covered in percentage to account for the
difference in time on pitch. Analysis of the relative variables allows for comparisons for
the team across quarters, and for positions across quarters. Team relative action profiles
across the quarters, and positional relative action profiles were determined for each
quarter.
Statistical Analysis
All statistical procedures were performed with SPSS on relative data (IBM, New York,
NY). A series of repeated measures ANOVA’s were used to analyze the differences for
the team between quarters. For team data ANOVA’s statistical significance was set at
p≤0.05, and all data were summarized in mean ± standard deviation (SD) with associated
95% confidence intervals. Cohen’s D effects size were evaluated as trivial <. 2, small .2.6, moderate .61-1.20, large 1.21-2.00, and very large 2.01-4.0 (Hopkins, Marshall,
Batterham, & Hanin, 2009). Thesmallsamplesizesforpositionaldatawerenot
deemedappropriateforinferentialstatistics.However,visual representation of
positional action profiles during all quarters is shown in Graphs 3a (forwards), 3b
(midfielders), 3c (screens), and 3d (defenders).
82
Results
Descriptive
The absolute distances covered by positions follow the same descending pattern for
odometers in meters as, the time position time on pitch (Table 2). It has been previously
noted that playing time is important in determining the absolute distances covered during
a match (Jennings et al., 2012). Examination of the relative variables, which account for
the varied playing time, showed that forwards covered the greatest percent distance in
zones 5 and 6, followed by midfielders, screens, and defenders (Table 3). This pattern
varies for zone 4, within which the midfielders possess the greatest percent distance
covered.
83
Table 2. Absolute Player and Positional Action Profiles for the Full Game
#
Odometer (m)
Position
10
8823
Forward Position
Midfield Position
Screen Position
Defender Position
2
3
2
3
11965
7534
8767
8056
± 1776
± 314
± 954
± 1653
± 972
Zone 1
Dist Stand
0-.16 m/s
38
46
29
54
32
± 29
± 7
± 12
± 20
± 5
Zone 2 Dist
Walk .19-1.6
m/s
Zone 3 Dist
Jog 1.69-3.05
m/s
± 573
± 163
± 306
± 579
± 493
2883 ± 680
3576 ± 174
2260 ± 374
2765 ± 567
3121 ± 411
2141
2737
1537
2084
2384
Zone 4 Dist
Run 3.084.16 m/s
2166
2911
2029
2365
1675
± 479
± 66
± 330
± 462
± 199
Zone 5 Dist
Fast Run
4.19-5.27 m/s
1134
1797
1201
1105
646
±
±
±
±
±
Zone 6 Dist
Sprint >5.27
m/s
272
461
37
198
77
99
898
478
393
199
±
±
±
±
±
All Data are represented as mean ± SD
Table 3. Relative Action Profiles for the Full
Game
Team
Forwards
Midfielders
Screens
Defenders
Zone 1 0 -.16m/s
0.42 ± 0.44
0.37 ± 0.12 0.37 ± 0.21
0.59 ± 0.21 0.40 ± 0.04
Zone 2 .17 - 1.68 m/s
±
±
±
22.53
8.29
22.67
2.09 20.61
5.71
23.20 ± 4.34 29.45 ± 4.22
Zone 3 1.69 - 3.07 m/s
29.91 ± 9.75
29.79 ± 2.05 29.81 ± 2.08
31.47 ± 1.68 38.64 ± 1.58
Zone 4 3.08 - 4.18 m/s
22.54 ± 7.28
24.48 ± 1.72 26.81 ± 2.10
26.77 ± 0.57 20.78 ± 1.93
Zone 5 4.19 -5.27 m/s
±
±
±
11.91
5.01
15.16
1.50 15.99
2.33
13.10 ± 3.02 8.22 ± 2.33
Zone 6 > 5.27 m/s
4.98 ± 2.79
7.53 ± 1.36 6.42 ± 0.99
4.88 ± 2.00 2.51 ± 0.98
m/min
108.14 ± 33.40 121.56 ± 6.74 125.28 ± 12.37 118.66 ± 8.84 108.34 ± 5.49
All Data are represented as mean ± SD, Velocity zone data is represented as a % of the total distance covered
84
140
65
73
17
64
Team Quarter Comparison
The effect of the game quarter on team m/min was statistically significant for 1st vs. 2nd,
1st vs. 3rd, and 1st vs. 4th; decreases were also statistically significant for 2nd vs. 3rd, and
2nd vs. 4th quarters (Table 5). The large effects size produced when comparing 1st vs. 3rd
and 1st vs. 4th may represent an opportunity for the implementation of recovery
techniques during the 10 minute half time (Table 5). A gradual decrease in m/min is
expressed comparative across all quarters in relationship to the 1st quarter. The changes
for the 3rd vs. 4th quarters were not statistically significant. The changes in m/min across
a game are visually represented in Graph 1.
A statistically significant increase in the percent of distance covered in zone 2 was noted
from 1st vs. 3rd, 1st vs. 4th, 2nd vs. 3rd and 2nd vs. 4th. Increasing the percent of time spent
walking is also indicative of fatigue, but the effect sizes for these changes were small to
trivial. The percent of distance covered in zones 4 and 5 decreased when comparing 1st
vs. 2nd, 1st vs. 3rd and 1st vs. 4th quarters, but no statistical significance occurred when
comparing zone 6 across the game. A visual representation of the team changes across a
game for actions in zones 1, 2, 3, 4, 5, and 6 are presented in Graph 2. The cumulative
effect of this information illuminates the general increase in low-intensity actions such as
walking, and a decrease in running, and fast running in 1st vs. 2nd, 1st vs. 3rd, and 1st vs. 4th
quarters. However, a more in-depth examination of positional changes across the game
was needed to determine the appropriate corrective actions.
85
Table. 4 Team Relative Action Profiles by Quarter
Q1
Q2
Q3
95% CI
Mean SD
Zone 1 0 -.16m/s
Lower
Q4
95% CI
Upper
Mean SD
Lower
95% CI
Upper
Mean SD
Lower
95% CI
Upper
Mean SD
Lower
Upper
0.31
±
0.18
0.21
0.41
0.34
±
0.14
0.27
0.42
0.41
±
0.22
0.30
0.53
0.47
±
0.29
0.32
0.62
Zone 2 .17 - 1.68 m/s
19.93
±
4.21
17.69
22.18
21.26
±
5.49
18.34
24.19
22.25
±
4.80
19.69
24.81
22.48
±
5.13
19.74
25.21
Zone 3 1.69 - 3.07 m/s
30.46
±
4.70
27.95
32.96
30.36
±
4.93
27.73
32.99
30.75
±
4.33
28.44
33.05
30.58
±
4.43
28.22
32.95
Zone 4 3.08 - 4.18 m/s
25.83
±
2.25
24.63
27.03
24.20
±
2.69
22.76
25.63
23.61
±
2.86
22.09
25.14
23.68
±
2.76
22.21
25.16
Zone 5 4.19 -5.27 m/s
15.31
±
3.64
13.37
17.25
13.96
±
3.74
11.96
15.95
13.23
±
2.68
11.80
14.66
13.00
±
3.69
11.03
14.97
6.11
±
2.36
4.85
7.37
5.70
±
2.28
4.49
6.92
5.35
±
2.00
4.29
6.42
5.43
±
2.01
4.36
6.50
126.87
±
8.53
122.33
131.42
119.27
±
11.11
113.35
125.19
113.43
±
11.10
107.51
119.34
113.18
±
10.87
107.39
118.97
Zone 6 > 5.27 m/s
m/min
*Velocity zone data is represented as a % of the total distance covered
Table 5. Team Relative Action Profiles Between Quarters
1st vs. 2nd *
Zone 1 0 -.16m/s
Zone 2 .17 - 1.68
m/s
Zone 3 1.69 - 3.07
m/s
Zone 4 3.08 - 4.18
m/s
Zone 5 4.19 -5.27
m/s
Zone 6 > 5.27 m/s
m/min
1st vs. 3rd
1st vs. 4th
p
valu
e
ES
Qual
p
value
ES
Qual
p
value
ES
1.00
-0.19
Trivial
0.14
-0.51
Small
0.10
-0.68
0.10
-0.27
Small
1.00
0.03
1.00
0.00
-0.51
Small
1.00
0.02
0.02
0.00
0.66
0.37
0.17
0.77
Moderate
Small
0.00
0.00
Trivial
0.12
Moderate
0.00
-0.54
0.86
0.65
0.35
1.36
Moderate
Moderate
0.01
0.00
Small
0.18
Large
0.00
0.85
Qual
p
value
ES
Qual
p
value
ES
Qual
Moderate
0.23
-0.39
Small
0.09
-0.58
Small
0.52
-0.24
Small
-0.05
Trivial
0.04
Trivial
-0.02
Trivial
0.07
Trivial
Small
0.03
-0.19
Trivial
Moderate
0.44
0.61
Moderate
0.31
Small
1.00
Large
0.00
86
0.05
-0.23
Small
1.00
-0.08
Trivial
0.63
1.40
3rd vs. 4th *
ES
1.00
-0.03
Trivial
2nd vs. 4th
Qual
1.00
0.02
Trivial
0.00
2nd vs. 3rd *
p
valu
e
1.00
-0.05
Trivial
Trivial
1.00
0.21
0.10
0.22
Small
0.16
Trivial
1.00
Small
0.00
0.53
1.00
0.19
Small
1.00
Trivial
1.00
0.26
Small
0.13
Trivial
1.00
-0.04
Trivial
Small
1.00
0.02
Trivial
0.55
Graph 1.
m/min
Teamm/minbyQuarter
140.00
135.00
130.00
125.00
120.00
115.00
110.00
105.00
100.00
m/min
Quarter1
Quarter2
Quarter3
Quarter4
Note: n=16
87
Graph 2.
PercentofTotalDistance
TeamRelativePercentDistancebyQuarter
36.00
34.00
32.00
30.00
28.00
26.00
24.00
22.00
20.00
18.00
16.00
14.00
12.00
10.00
8.00
6.00
4.00
2.00
0.00
Quarter1
Quarter2
Quarter3
Quarter4
Zone10-.16m/s
Zone2.17- 1.68 Zone31.69- 3.07 Zone43.08- 4.18 Zone54.19-5.27 Zone6>5.27m/s
m/s
m/s
m/s
m/s
Note: n=16
88
Quarter Comparison by Position
Figure 6a display the relative mean ± SD and 95% confidence interval for all the action
profiles variables per quarter. Figure 7a displays the effects size and qualitative
descriptors for effect size across quarters. Forwards produced the highest m/min in
quarter 1 compared with all other quarters. Forward’s m/min decreased for 1st vs. 2nd, 1st
vs. 3rd, and 1st vs. 4th quarters. However, when m/min were examined in reference to the
previous quarter the effect sizes gradually decreased, depicting a progressive decline
throughout the game.
Figure 6b display the relative mean ± SD and 95% confidence interval for all the action
profiles variables per quarter. Figure 7b displays the effects size and qualitative
descriptors for effect size across quarters. Similar to forwards, midfielders produced the
highest m/min in the 1st quarter, and the effects sizes were moderate for 1st vs. 2nd, large
for 1st vs. 3rd, and large for 1st vs. 4th. It’s important to note that midfielders showed a
moderate to large effect size in zones 4 and 5 for 1st vs. 2nd, 1st vs. 3rd, and 1st vs. 4th.
Figure 6c display the relative mean ± SD and 95% confidence interval for all the action
profile variables per quarter. Figure 7c displays the effects size and qualitative descriptors
for effect size across quarters and showed a progressive decrease across the game.
Similar to forwards and midfielders, screens produced the highest m/min in the 1st quarter
and the effects sizes were moderate for 1st vs. 2nd, large for 1st vs. 3rd, and large for 1st vs.
4th. They also showed a decline in m/min in subsequent quarters compared to the 1st.
Screens maintained their percent distance covered in zones 5 and 6 across quarters.
89
However, they did express a decrease represented as a large effect size in zone 4 when
comparing 1st vs. 2nd, 1st vs. 3rd and 1st vs. 4th quarters.
Figure 6d displays the relative mean ± SD and 95% confidence interval for all the action
profiles variables per quarter. Figure 7d displays the effects size and qualitative
descriptors for effect size across quarters. Defenders produced the highest m/min in
quarter 1. However, the difference for m/min when comparing the 1st vs. 2nd was trivial.
When comparing 1st vs. 3rd and 1st vs. 4th moderate effect sizes very produced. Defenders
experience a decline in the amount of time spent running when comparing the 1st vs. 4th
quarter. The decrease in running during 1st vs. 4th was accompanied by increases in the
amount of time standing in 2nd vs. 3rd and 2nd vs. 4th.
90
Table 6a.
Forwards
Relative Action
Profiles by
Quarter
Q1
Q2
Q3
95% CI
Forwards
Zone 1 0 .16m/s
Zone 2 .17 1.68 m/s
Zone 3 1.69 3.07 m/s
Zone 4 3.08 4.18 m/s
Zone 5 4.19 5.27 m/s
Zone 6 > 5.27
m/s
m/min
Mean SD
Lower
Upper
Mean SD
95% CI
Lower
Upper
Mean SD
95% CI
Lower
Upper
Mean SD
Lower
Upper
±
0.15
0.10
0.47
0.37
±
0.13
0.24
0.50
0.41
±
0.18
0.19
0.63
0.44
±
0.23
0.17
0.71
20.00
±
3.09
16.32
23.68
21.98
±
4.91
16.92
27.03
22.77
±
4.36
18.44
27.10
23.13
±
5.20
18.39
27.88
32.31
±
3.78
28.26
36.37
31.34
±
5.11
26.64
36.04
32.34
±
5.01
27.96
36.73
31.35
±
4.95
26.95
35.76
26.14
±
1.83
24.08
28.20
24.60
±
1.97
22.29
26.91
24.78
±
2.57
22.32
27.25
24.33
±
3.24
21.94
26.73
14.78
±
3.47
11.30
18.27
13.73
±
3.45
10.29
17.16
12.94
±
2.25
10.39
15.48
13.61
±
5.06
9.88
17.33
6.49
±
2.98
4.09
8.89
6.51
±
2.83
4.38
8.65
5.23
±
2.35
3.30
7.15
5.55
±
2.05
3.46
7.64
129.66
±
6.04
121.08
138.2
5
121.31
±
8.71
109.92
132.70
114.76
±
11.18
103.35
126.16
116.69
±
12.09
105.5
7
127.81
Q1
Q2
95% CI
95% CI
0.28
Table 6b.
Midfielder
Relative
Action
Profiles by
Quarter
Midfielders
Zone 1 0 .16m/s
Zone 2 .17 1.68 m/s
Zone 3 1.69 3.07 m/s
Zone 4 3.08 4.18 m/s
Zone 5 4.19 5.27 m/s
Zone 6 >
5.27 m/s
m/min
Q4
Mean SD
Lower
Upper
Mean SD
Q3
95% CI
Lower
Upper
Q4
Mean SD
95% CI
Lower
Upper
Mean SD
95% CI
Lower
Upper
0.41
±
0.26
0.20
0.61
0.42
±
0.18
0.28
0.57
0.55
±
0.31
0.30
0.79
0.71
±
0.43
0.40
1.01
21.37
±
4.67
17.26
25.49
23.04
±
5.47
17.38
28.69
23.78
±
3.60
18.94
28.62
24.43
±
4.90
19.12
29.73
28.22
±
1.37
23.69
32.76
28.59
±
1.58
23.34
33.85
29.44
±
2.14
24.54
34.34
28.81
±
2.53
23.88
33.73
27.40
±
2.20
25.09
29.70
26.11
±
2.24
23.53
28.70
25.20
±
1.63
22.44
27.95
25.11
±
2.18
22.43
27.78
16.44
±
3.72
12.54
20.33
14.44
±
4.04
10.59
18.28
13.31
±
2.89
10.47
16.16
13.22
±
3.40
9.06
17.38
6.16
±
2.24
3.48
8.84
5.33
±
1.24
2.94
7.72
5.57
±
1.86
3.42
7.73
5.68
±
1.51
3.35
8.01
129.51
±
12.14
119.91
139.10
119.72
±
17.2
9
106.98
132.46
114.1
3
±
15.0
5
101.38
126.88
111.08
±
15.12
98.64
123.51
91
Table 6c.
Screens
Relative
Action
Profiles by
Quarter
Q1
Q2
95% CI
Screens
Zone 1 0 .16m/s
Zone 2 .17 1.68 m/s
Zone 3 1.69 3.07 m/s
Zone 4 3.08 4.18 m/s
Zone 5 4.19 5.27 m/s
Zone 6 >
5.27 m/s
m/min
Mean SD
Mean SD
Q3
95% CI
Q4
Mean SD
95% CI
Mean SD
95% CI
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
0.31
±
0.13
0.08
0.55
0.33
±
0.06
0.16
0.50
0.38
±
0.15
0.10
0.66
0.41
±
0.12
0.06
0.75
22.98
±
3.99
18.23
27.73
24.07
±
6.09
17.54
30.59
25.05
±
5.55
19.46
30.65
24.67
±
5.65
18.54
30.80
34.45
±
7.24
29.21
39.68
34.27
±
7.33
28.20
40.33
32.20
±
7.61
26.54
37.86
33.54
±
7.44
27.85
39.22
25.46
±
3.37
22.80
28.12
21.28
±
3.28
18.30
24.26
20.63
±
4.08
17.45
23.81
20.43
±
1.26
17.34
23.52
12.33
±
4.95
7.83
16.83
10.48
±
4.24
6.04
14.92
11.21
±
4.05
7.93
14.49
10.26
±
3.70
5.46
15.07
4.46
±
3.09
1.36
7.55
3.61
±
2.86
0.86
6.37
3.77
±
1.89
1.28
6.26
4.24
±
3.81
1.54
6.93
124.57
±
11.95
113.49
135.65
111.91
±
12.7
9
97.20
126.62
106.2
2
±
12.5
0
91.50
120.94
107.15
±
6.71
92.79
121.51
92
Table 6d.
Defenders
Relative
Action
Profiles by
Quarter
Q1
Q2
95% CI
Defenders
Zone 1 0 .16m/s
Zone 2 .17 1.68 m/s
Zone 3 1.69 3.07 m/s
Zone 4 3.08 4.18 m/s
Zone 5 4.19 5.27 m/s
Zone 6 >
5.27 m/s
m/min
Mean SD
Upper
Mean SD
Q4
Mean SD
Lower
Upper
95% CI
Mean SD
Lower
Upper
95% CI
Lower
Upper
0.24
±
0.19
0.04
0.45
0.23
±
0.12
0.08
0.37
0.31
±
0.19
0.06
0.55
0.33
±
0.20
0.03
0.63
16.12
±
3.43
12.00
20.23
16.49
±
4.57
10.84
22.15
17.96
±
4.50
13.12
22.80
18.06
±
3.69
12.75
23.36
27.37
±
3.67
22.84
31.91
27.98
±
4.46
22.72
33.23
28.97
±
2.09
24.06
33.87
29.19
±
2.43
24.26
34.11
24.15
±
1.01
21.85
26.45
23.97
±
2.26
21.38
26.55
22.81
±
1.71
20.05
25.56
23.89
±
2.08
21.22
26.57
17.08
±
2.21
13.19
20.98
16.37
±
2.36
12.52
20.21
15.04
±
1.12
12.19
17.88
14.08
±
1.87
9.92
18.24
6.83
±
1.00
4.15
9.50
6.64
±
1.24
4.25
9.02
6.47
±
1.57
4.32
8.63
5.93
±
0.86
3.60
8.26
122.47
±
4.39
112.87
132.07
121.78
±
6.15
109.04
134.52
116.46
±
7.12
103.71
129.21
115.42
±
8.21
102.98
127.85
Table 7a. Forwards
effects size across
Quarter
Forwards
Zone 1 0 -.16m/s
Zone 2 .17 - 1.68 m/s
Zone 3 1.69 - 3.07 m/s
Zone 4 3.08 - 4.18 m/s
Zone 5 4.19 -5.27 m/s
Zone 6 > 5.27 m/s
m/min
Lower
Q3
95% CI
1st vs. 2nd
ES
-0.66
-0.48
0.22
0.81
0.31
-0.01
1.12
Qual
Moderate
Small
Small
Moderate
Small
Trivial
Large
1st vs. 3rd
ES
-0.77
-0.73
-0.01
0.61
0.63
0.47
1.66
Qual
Moderate
Moderate
Trivial
Small
Moderate
Small
Large
1st vs. 4th
ES
-0.80
-0.73
0.22
0.69
0.27
0.37
1.36
Qual
Moderate
Moderate
Small
Moderate
Small
Small
Large
93
2nd vs. 3rd
ES
-0.24
-0.17
-0.20
-0.08
0.27
0.50
0.65
Qual
Small
Trivial
Trivial
Trivial
Small
Small
Moderate
2nd vs. 4th
ES
-0.34
-0.23
0.00
0.10
0.03
0.39
0.44
Qual
Small
Small
Trivial
Trivial
Trivial
Small
Small
3rd vs. 4th
ES
-0.13
-0.08
0.20
0.15
-0.17
-0.15
-0.17
Qual
Trivial
Trivial
Trivial
Trivial
Trivial
Trivial
Trivial
Table 7b. Midfields
effects effect size across
Quarters
Midfielders
ES
Qual
1st vs. 3rd
ES
Qual
1st vs. 4th
ES
Qual
2nd vs. 3rd
ES
Qual
2nd vs. 4th
ES
Qual
3rd vs. 4th
ES
Qual
Zone 1 0 -.16m/s
-0.07
Trivial
-0.47
Small
-0.83
Moderate
-0.48
Small
-0.86
Moderate
-0.42
Small
Zone 2 .17 - 1.68 m/s
-0.33
Small
-0.58
Small
-0.64
Moderate
-0.16
Trivial
-0.27
Small
-0.15
Trivial
Zone 3 1.69 - 3.07 m/s
-0.25
Small
-0.68
Moderate
-0.29
Small
-0.45
Small
-0.10
Trivial
0.27
Small
Zone 4 3.08 - 4.18 m/s
0.58
Moderate
1.13
Large
1.05
Moderate
0.47
Small
0.46
Small
0.05
Trivial
Zone 5 4.19 -5.27 m/s
0.51
Moderate
0.94
Moderate
0.90
Moderate
0.32
Small
0.33
Small
0.03
Trivial
Zone 6 > 5.27 m/s
0.46
Small
0.29
Small
0.25
Small
-0.15
Trivial
-0.25
Small
-0.06
Trivial
m/min
0.66
Moderate
1.12
Large
1.34
Large
0.34
Small
0.53
Small
0.20
Small
Table 7c. Screens
effect size across
Quarters
Screens
Zone 1 0 -.16m/s
1st vs. 2nd
ES
Qual
1st vs. 3rd
ES
Qual
1st vs. 4th
ES
Qual
2nd vs. 3rd
ES
Qual
2nd vs. 4th
ES
Qual
-0.17
Trivial
-0.48
Small
-0.75
Moderate
-0.43
Small
-0.84
Moderate
Zone 2 .17 - 1.68 m/s
-0.21
Small
-0.43
Small
-0.35
Small
-0.17
Trivial
-0.10
Zone 3 1.69 - 3.07 m/s
0.02
Trivial
0.30
Small
0.12
Trivial
0.28
Small
Zone 4 3.08 - 4.18 m/s
1.26
Large
1.29
Large
1.98
Large
0.18
Zone 5 4.19 -5.27 m/s
0.40
Small
0.25
Small
0.47
Small
0.29
Small
0.27
Small
0.06
1.02
Moderate
1.50
Large
1.80
Zone 6 > 5.27 m/s
m/min
1st vs. 2nd
3rd vs. 4th
ES
Qual
-0.22
Small
Trivial
0.07
Trivial
0.10
Trivial
-0.18
Trivial
Trivial
0.34
Small
0.07
Trivial
-0.18
Trivial
0.06
Trivial
0.24
Small
Trivial
-0.07
Trivial
-0.19
Trivial
-0.16
Trivial
Large
0.45
Small
0.47
Small
-0.09
Trivial
94
Table 7d. Defenders
effect size across
Quarters
Defenders
ES
Zone 1 0 -.16m/s
0.27
Small
-0.33
Small
-0.42
Small
-0.09
-0.15
Trivial
Trivial
-0.46
-0.54
Small
Small
-0.54
-0.58
0.10
0.31
Trivial
Small
0.95
1.16
Moderate
Moderate
0.16
1.47
Zone 6 > 5.27 m/s
0.17
Trivial
0.27
Small
m/min
0.13
Trivial
1.02
Moderate
Zone 2 .17 - 1.68 m/s
Zone 3 1.69 - 3.07 m/s
Zone 4 3.08 - 4.18 m/s
Zone 5 4.19 -5.27 m/s
1st vs. 2nd
Qual
1st vs. 3rd
ES
Qual
1st vs. 4th
ES
0.97
1.07
Qual
2nd vs. 3rd
ES
2nd vs. 4th
3rd vs. 4th
Qual
ES
Qual
ES
Qual
-4.58
Very Large
-4.53
Very Large
-0.10
Trivial
Small
Small
-0.32
-0.28
Small
Small
-0.38
-0.34
Small
Small
-0.02
-0.10
Trivial
Trivial
Trivial
Large
0.58
0.72
Small
Moderate
0.04
1.08
Trivial
Moderate
-0.57
0.62
Small
Moderate
Moderate
0.12
Trivial
0.67
Moderate
0.43
Small
Moderate
0.80
Moderate
0.88
Moderate
0.14
Trivial
Graph 3a.
PercentofTotalDistance
FowardsRelativePercentDistancebyQuarter
50.00
45.00
40.00
35.00
30.00
25.00
20.00
15.00
10.00
5.00
0.00
Quarter1
Quarter2
Quarter3
Quarter4
Zone10-.16m/s
Zone2.17- 1.68 Zone31.69- 3.07 Zone43.08- 4.18 Zone54.19-5.27 Zone6>5.27m/s
m/s
m/s
m/s
m/s
Note: n=5
95
Graph 3b.
PercentofTotalDistance
MidfieldersRelativePercentDistancebyQuarter
50.00
45.00
40.00
35.00
30.00
25.00
20.00
15.00
10.00
5.00
0.00
Quarter1
Quarter2
Quarter3
Quarter4
Zone10-.16m/s
Zone2.17- 1.68 Zone31.69- 3.07 Zone43.08- 4.18 Zone54.19-5.27 Zone6>5.27m/s
m/s
m/s
m/s
m/s
Note: n=4
96
PercentofTotalDistance
Graph 3c.
ScreensRelativePercentDistancebyQuarter
50.00
45.00
40.00
35.00
30.00
25.00
20.00
15.00
10.00
5.00
0.00
Quarter1
Quarter2
Quarter3
Quarter4
Zone10-.16m/s
Zone2.17- 1.68 Zone31.69- 3.07 Zone43.08- 4.18 Zone54.19-5.27 Zone6>5.27m/s
m/s
m/s
m/s
m/s
Note: n=3
97
PercentofTotalDistance
Graph 3d.
50.00
45.00
40.00
35.00
30.00
25.00
20.00
15.00
10.00
5.00
0.00
DefendersRelativePercentDistancebyQuarter
Quarter1
Quarter2
Quarter3
Quarter4
Zone10-.16m/sZone2.17- 1.68m/s
Zone31.69- 3.07m/s
Zone43.08- 4.18m/s
Zone54.19-5.27m/sZone6>5.27m/s
Note: n=4
98
Discussion
This study was an investigation of the action profiles of US Women’s National Team
during the 2015 World League 3 Semi-Finals, and the 2015 Pan American games (Table
1). These tournaments represent games played with the 2015 FIH game time formatting
change. It should be noted that disparities exist between the rankings of competition
during the two tournaments. It has also been reported that more high intensity running is
performed when teams play against better opposition (Rampinini, Coutts, Castagna,
Sassi, & Impellizzeri, 2007). Due to the team’s success at the Pan American Games, the
US national team may not have had to perform as much intensity running as the opposing
team. However the opposition during the World League 3 Semi-Finals was considered to
be a high caliber, possibility resulting in more high intensity running.
It is important to note that the official time of a game has changed from 70 minute to 60
minute. However, the newly implemented clock stoppage for corners, and goals can
drastically affect the game length. The new match formatting rules stop the clock for
corners and goals. The official game clock is stopped on a corner, or after a goal, and an
independent running clock of forty seconds begins. Once the forty seconds ends, the
official game clock resumes. The quarter lengths ranged from 15 minutes to 24 minutes.
The actual game time will depend on the number of corners and goals awarded during the
game. For this reason, caution must be used when examining m/min as an indicator of
fatigue, because changes in m/min may be due to the varying durations in quarter length.
The seeming increases in low intensity actions during the clock stoppage could also
decrease the reported m/min. To retain ecological validity corner and goal clock
99
stoppages were not removed when calculating time on pitch. Future research should
examine the effect of removing the additional time from corners and goals on action
profiles.
Previous research has suggested that a large sample size is needed to provide true average
action profiles for elite level athletes (Carling, 2013; Cummins, Orr, O'Connor, & West,
2013). Due to the small sample size p-values were not determined for the positional data
across quarters. While this was a specific limitation, it should be noted that this study is
one of only a few on elite level field hockey players. Thus, this study is a first step in
characterizing the US women’s elite level of play, and allowing for comparisons with
other countries elite athletes. The data reported in this paper is specific to the 2015 US
Women’s National Team during both the World League 3 Semi-Finals, and the Pan
American Games. Using high-level athletes allows for a unique opportunity to examine
elite performance. Although it adds uncontrollable variability due to competitors, team
tactics, injuries, and other environmental factors, ecologically valid measurements are of
value to the sport science research.
Conclusion
To the author’s knowledge, this is the first study to examine the action profiles of
international field hockey player’s during games played under the newly implemented
match duration and clock stoppage rules. The findings demonstrated that the teams low
intensity actions in zone 2 gradually increased, while the percent distance covered in
zones 4 and 5 decreased when comparing 1st vs. 2nd, 1st vs. 3rd and 1st vs. 4th quarters.
100
However, no statistical significance occurred when comparing zone 6 across the game.
The US women’s national team increased the percent distance covered walking and
decreased the distance running and fast running to possibility maintain the percent
distance covered sprinting across a game.
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103
CHAPTER 5
CHANGES IN ELITE FIELD HOCKEY PLAYER ACTION PROFLIES DUE TO THE
2015 RULE CHANGE
Authors: Heather A Abbott1, Kimitake Sato1, Satoshi Mizuguchi1, Dave Hamilton2,
Michael H. Stone1
Affiliations: Center of Excellence for Sport Science and Coach Education Department of
Exercise and Sport Sciences, East Tennessee State University, Johnson City, TN, USA1
USA Field Hockey, Manheim, PA2
Prepared for Submission International journal of sport Science and Coaching
104
Abstract
The objective of this study was to analyze the effects of the 2015 International
Hockey Federation (FIH) rule changes on the United State Women’s National Field
Hockey Team’s game action profiles. The purpose was to determine the magnitude of
effect of the rule change on game action profiles. Comparisons were made between two
international tournaments before and two international tournaments after the match
formatting rule change, resulting in three hundred and forty-three files. Relative actions
profiles consisted of meters per minute (m/min), and the percent of the total distance
covered in zone 1 standing (0-.16 m/s), zone 2 walking (.19-1.6 m/s), zone 3 jogging
(1.69-3.05 m/s), zone 4 running (3.08-4.16 m/s), zone 5 fast running (4.19-5.27 m/s), and
zone 6 sprinting (>5.27 m/s). The results indicated that team’s relative action profiles
(n=16) across a game were only statistically significant in zone 1 (p=0.0000005) pre to
post rule change. After the rule change, the team’s percent of the total distance in the 1st
half was statically lower in zones 1 (p= 0.0000002), and greater in 5 (p= 0.02) and 6 (p=
0.02), and, in the 2nd half, lower in zone 1 (p= 0.000009). Meters per minute were also
statistically lower in the 2nd half (p= 0.003). Wilcoxon signed ranks tests revealed
statistical significance changes for midfielders in both halves after the rule change (1st
half: decrease in zone 1 (p= 0.04), increase in zone 5 (p= 0.04) and 6 (p= 0.04) and 2nd
half: decrease in zone 1 (p= 0.04) and m/min (p= 0.04)).It may not be possible for this
elite team to experience increases in game action profiles However, the tactical position
of midfielders, with the opportunity to play offensively and defensive, may benefits from
the additional 2 minute rest in between quarters under the new rules.
Key Words: Global Positioning System, Team Sports, Substitutions, Game Formatting
105
Introduction
Global Positioning System (GPS) has have provided coaches, trainers and sports scientist
with an understanding of the action profiles of athletes during field hockey games and
training (Dwyer & Gabbett, 2012; Gabbett, 2010; Jennings, Cormack, Coutts, & Aughey,
2012a, 2012b; Lythe & Kilding, 2011; Macutkiewicz & Sunderland, 2011; Polglaze,
Dawson, Hiscock, & Peeling, 2014; Vescovi & Frayne, 2015; White & MacFarlane,
2014; Wyldea, Yonga, Azizb, Mukherjeec, & Chiac, 2014). However, the rules of sport
are always evolving. Every two years rule changes are implemented by field hockey
governing body, the FIH. With the new match duration and clock stoppage during
corners and after goals, implemented by the FIH in January 2015, it is important to
determine if elite athlete action profiles have changed. Comparisons need to be made in
order to assess the need for altered high performance training. Comparing the action
profiles pre and post the rule change will allow for the further development of training
recommendations when athletes are transitioning from college (two 35 minute halves) to
international play (four 15 minute quarters). It has been speculated that the rule change
provides teams with the ability to maintain and produce more high intensity actions.
However, this potential alteration in play has not been documented.
The examination of the evolution of the players’ action profiles in response to changes in
the game time constraints due to the 2015 FIH rule changes is novel. The availability of
activity profile changes for women’s field hockey in relationship to rule changes are
scarce. For example only one study exists examining the effect of the 2009, free-hit rule
on the game (Tromp & Holmes, 2011). Performance analysis pre and post rule change
106
can be used to determine the effect of the change on varying aspects of the game and
examine the evolution of the players in response to the change. The information divulged
from this study will allow for the continued development of attributes specific to the new
style of match formatting. Knowledge of the ongoing action profile transitions taking
place will be beneficial for strength and conditioning practitioners when designing
training programs for the development of athletes, specifically those transitioning from
college to the national team.
Therefore the purpose of this study was to determine the effect of the FIH game
formatting rule change on the team and positional action profiles produced by the US
women’s national team during international tournaments before and after the game
formatting rule change.
Methods
Athletes
Twenty players form the US Women’s’ National Field Hockey team (pre rule age 24.8 ±
2.4 years, post rule age 25.5 ± 2.1 years, pre rule body weight 61.1 ± 4.6 kg, post rule
body weight 60.6 +/-5.1 kg) participated in this study. The East Tennessee State
University Institutional Review Board approved this study.
Data Collection
Wearable GPS technology allowed for the measurement of relative variables (m/min, and
percent of the total distance covered in zones 1, 2, 3, 4, 5, and 6) from the US Women’s
107
National Field Hockey Team. The GPS devices used during pre-collection were the
Catapult MinimaxX (Catapult Innovations, Melbourne, Australia), which sample at 10
Hz. The post rule activity profile data were collected using Optimye S5GPS units
(Catapult Innovations, Team S4, Melbourne, Australia), also sampling at 10 Hz. Both
GPS units are valid for measuring distance and velocity during liner and sport specific
movements (Johnston, Watsford, Kelly, Pine, & Spurrs, 2014; Rampinini et al., 2015;
Varley, Fairweather, & Aughey, 2012). Pre GPS data were collected during the 2014
Champions Challenge in Glasgow, Scotland and the 2014 Rabobank Hockey World Cup
in The Hague, Netherlands (Table 1). Post GPS data were collected during the 2015
World League 3 Semi Finals in Valencia, Spain and the 2015 Pan American Games in
Toronto, Canada (Table 1). All matches were played on a water based, synthetic turf,
which were in compliance with FIH field of play rules (FIH, 2014). GPS units were
positioned between the scapulae in a custom built harness worn in compliance with
previous research recommendations (Barrett, Midgley, & Lovell, 2014). Players wore the
same MinimaxX units during pre-collection, and the same Optimye S5 units during post
collection. All players were acclimated to wearing the GPS units as a result of previous
practice, and game monitoring.
108
Table 1. Tournament Schedule
Tournament Finish
Location
Champions
1st
Glasgow,
ChallengeI
Scotland
2014
Rabobank
4th
Hockey
TheHague,
WorldCup
Netherlands
2014
World
League3
SemiFinals
5th
Pan
American
Games
1st
Valencia,
Spain
Toronto,
Canada
4/27/2014
ESP
3to1(14)
6/1/2014
ENG
Date/Opponent/Score/WorldRanking
4/28/2014 4/30/201 5/1/2014 5/3/2014
RSA
4IRL
INDIA
ESP
1to2(11) 3to0(15) 1to0(13) 3to1(14)
6/3/2014 6/6/2014 6/8/2014 6/10/2014
ARG
CHN
GER
RSA
2to1(6)
2to2(2)
5to0(5)
4to1(8)
4to2(11)
6/11/2015
URU
6/13/2015
RSA
6/14/201
5GER
6/16/201
5IRL
6/18/2015
ARG
5/4/2015
IRL
3to1(15)
6/12/2014
AUS
2to2(1to
3)(3)
6/20/2015
IRL
2to0(25)
4to1(11)
2to2(8)
0to2(15)
0to3(2)
6to1(15)
7/13/2015
URU
7/15/2015
CHIL
7/17/201
5CUBA
7/22/2015
CAN
7/24/2015
ARG
5to0(25)
2to0(21)
12to0
(NA)
3to0(19)
2to1(2)
109
7/20/201
5DOM
REP
15to0
(37)
6/14/20
14ARG
1to2(2)
6/21/20
16ESP
3to
1(14)
Data Analysis
Data analyzed included three hundred forty-three samples, collected over four
tournaments matches, which included twenty-six games. GPS data were downloaded
using the manufacturer-supplied software (Logan Plus, version 5.1.7). The GPS data
were edited to only include time spent on the field of play, during the regulation game
time. The time on the substitution bench, and the time during the 2 minute and 10 minute
rest periods were not included in any calculations. This is in accordance with
recommendations from White and MacFarlane (2013). The velocity zones used to
determine the relative percent distance covered were chosen from previously published
international field hockey research (stand 0-.16 m/s, walk .19-1.6 m/s, jog 1.69-3.05 m/s,
run 3.08-4.16 m/s, fast run 4.19-5.27 m/s, and sprint>5.27 m/s) (Lythe & Kilding, 2011;
Macutkiewicz & Sunderland, 2011). GPS Data were then exported to Microsoft Excel for
further analysis
Each individual athlete’s data files were averaged across games to obtain the relative
measures. This data processing was chosen to account for several missing files due to a
few corrupted data files. Players were divided into four positional groups: forwards,
midfielder, screens, and defenders. In order to provide an estimate of a player’s action
profile playing a certain position, data obtained from all players of a particular position
were averaged across those players (e.g. forward player). Players were substituted into
the same positions; therefore each player only contributes to one positional group. It is
important to note that for these analyses, not all players played an equal number of
minutes, and the new match formatting caused variation between quarter lengths. . Total
110
distance covered was considered relative to the total number of minutes played for each
athlete (m/min), and total distance covered in each velocity zone (1-6) was considered
relative to the total distance covered in percentage to account for the difference in time on
pitch. Analysis of the relative variables allows for comparisons for the teams pre to post
rule change. Team relative action profiles across the 1st and 2nd half and positional
relative action profiles were then determined for 1st and 2nd half. The ten minute half
time, that takes place in both the pre and post-game formatting, was chosen to divide the
of game into 1st and 2nd halves
Statistical Analysis
All statistical procedures were performed with SPSS (IBM, New York, NY). Paired
sample t tests were used to compare team full game data pre rule change to the
corresponding team full game data post rule change at the team level. Paired sample t
tests were also used to compare each half of the pre rule change to the corresponding half
of the post rule change at the team level. Differences within a position between the pre
and post rule change were examined for each of the halves using a Wilcoxon signed ranktest due to limited sample sizes. Statistical significance was set at p ≤0.05, and all data
were reported as mean ± SD and 95% confidence interval. Cohen’s d effect sizes (d) were
determined as trivial <0. 2, small 0.2-0.6, moderate 0.61-1.20, large 1.21-2.00 and very
large 2.01-4.0 (Hopkins, Marshall, Batterham, & Hanin, 2009).
111
Results
Team Full Game Pre vs. Post Rule Change
The paired samples t-test used to evaluate the relative actions profiles from pre to post
rule change, indicated no statistical difference in zones 2, 3, 4, 5, 6 and m/min. However,
statistical significance was produced for zone 1 with a very large effect size (d = 2.55) for
the decrease in percent distance covered pre to post rule change in zone 1 (Table 2).
Team pre 1st vs. post 1st and pre 2nd vs. post 2nd
The paired sample t-test indicated statistical significance in zones 1, 5 and 6 during the 1st
half of the game. Statistical differences in zone 1 represented a decrease in the percent
total distance covered from pre to post rule change. Statistical differences in zones 5 and
6 represented an increase in the total percent of distance-covered pre to post rule change,
with small effects sizes (Table 3a). During the 2nd half of the game statistically significant
decreases occurred in zone 1 and m/min (Table 3b).
Position pre 1st vs. post 1st and pre 2nd vs. post 2nd
Mean differences in relative variables during a positional breakdown for per 1st to post 1st
and pre 2nd to post 2nd were assessed with a Wilcoxon Signed Rank Test. No statistical
significance was found for all relative variables for forwards, screens and defenders
(Table 4a, 4c, 4d). Midfielders were found to have statistical changes in both halves of
the game pre to post. They had a statistical decrease in zone 1, and a statistical increase in
zones 5 and 6 pre to post during the 1st half of the game. During the 2nd half of the game,
zones 1 and 3 and m/min had statistical decreases (Table 4b). The Wilcoxon Signed Rank
112
Test showed that all 5 players in the midfielders positional group were involved in the
significant changes during the 1st and 2nd half of the game.
113
Table 2. Paired t-test for Team Full Game Action Profiles Pre vs.
Post
Pre Full Game
Mean ± SD
Zone 1 0 1.06 ± 0.32
.16m/s
Zone 2 .17 24.12 ± 4.83
1.68 m/s
Zone 3 1.69 33.11 ± 3.62
3.07 m/s
Zone 4 3.08 24.89 ± 3.23
4.18 m/s
Zone 5 4.19 12.04 ± 3.04
5.27 m/s
Zone 6 > 5.27
4.75 ± 2.04
m/s
m/min
120.58 ± 10.25
Note: ES is effect size, p – value ≤0.05
Post Full Game
Mean ± SD
95%
Confidence
Interval of the
Difference
Mean
Difference
Lower
Upper
t
df
pvalue
ES
Qualitative
Descriptor
0.42
± 0.16
0.65
0.48
0.81
8.35
15
0.00
2.55
Very Large
23.87
± 5.19
0.25
-1.51
2.01
0.30
15
0.76
0.05
Trivial
32.07
± 4.41
1.04
-0.31
2.39
1.64
15
0.12
0.26
Small
24.46
± 2.84
0.43
-0.23
1.09
1.39
15
0.19
0.14
Trivial
13.19
± 3.66
-1.15
-2.51
0.21
-1.81
15
0.09
-0.34
Small
5.52
± 2.35
-0.77
-1.58
0.05
-2.01
15
0.06
-0.35
Small
118.16 ± 9.38
2.42
-1.75
6.58
1.23
15
0.24
0.25
Small
114
Table 3a. Paired t-test for Team Full Game Action Profiles pre 1st vs. post 1st
95%
Confidence
Interval of the
Mean
Difference
st
st
Pre 1 Mean SD Post 1 Mean SD Difference Lower Upper p-value
Zone 1 0 0.97 ± 0.25
0.36 ± 0.12
0.61
0.49
0.73
0.00
.16m/s
Zone 2 .17 23.50 ± 4.72 22.85 ± 5.14
0.65
-0.59
1.88
0.28
1.68 m/s
Zone 3 1.69
33.37 ± 3.59 32.24 ± 4.47
1.12
-0.42
2.66
0.14
- 3.07 m/s
Zone 4 3.08
25.30 ± 3.15 25.03 ± 2.85
0.27
-0.33
0.87
0.35
- 4.18 m/s
Zone 5 4.19
12.19 ± 3.13 13.83 ± 4.08
-1.64
-2.98 -0.30
0.02
-5.27 m/s
Zone 6 >
4.67 ± 2.17
5.70 ± 2.54
-1.03
-1.90 -0.16
0.02
5.27 m/s
m/min
122.24 ± 10.12 124.21 ± 11.02
-1.98
-5.96
2.00
0.31
Note: ES is effect size, p – value ≤0.05
115
t
df
ES
Qualitative
Descriptor
10.63
15
3.11
Very Large
1.12
15
0.13
Trivial
1.56
15
0.28
Small
0.96
15
0.09
Trivial
-2.61
15
-0.45
Small
-2.52
15
-0.44
Small
-1.06
15
-0.19
Trivial
Table 3b. Paired t-test for Team Full Game Action Profiles pre 2nd vs. post 2nd
95%
Confidence
Interval of the Mean
Difference
nd
nd
Pre 2 Mean SD Post 2 Mean SD Difference Lower Upper p-value
Zone 1 0 1.14
0.39
0.49
0.22
0.65
0.44
0.86
0.00
.16m/s
±
±
Zone 2 .17 24.79
4.94 25.06
5.02
-0.27
-1.75
1.20
0.70
1.68 m/s
±
±
Zone 3 1.69 32.91
3.71 32.39
4.02
0.53
-0.93
1.98
0.45
3.07 m/s
±
±
Zone 4 3.08 24.43
3.38 24.04
3.09
0.39
-0.67
1.45
0.45
4.18 m/s
±
±
Zone 5 4.19 11.88
2.96 12.75
3.58
-0.88
-2.12
0.37
0.15
5.27 m/s
±
±
Zone 6 > 5.27
4.80
1.97
5.27
2.16
-0.47
-1.19
0.25
0.18
m/s
±
±
m/min
120.34 ± 10.49 115.45 ± 10.39
4.89
1.90
7.89
0.00
Note: ES is effect size, p – value ≤0.05
116
t
df
6.53
15
-0.40
15
0.77
15
0.78
15
-1.50
15
-1.39
15
3.48
15
ES
Qualitative
Descriptor
2.05
Very Large
-0.06
Trivial
0.14
Trivial
0.12
Trivial
-0.27
Small
-0.23
0.47
Small
Small
Table 4a. Forwards Pre 1st vs. Post 1st & 2 Pre 2nd vs. Post 2nd Wilcoxon Signed Rank Tests
Pre
Post
Quarter 1 Zone 1 0 -.16m/s
Zone 2 .17 - 1.68 m/s
Zone 3 1.69 - 3.07 m/s
Zone 4 3.08 - 4.18 m/s
Zone 5 4.19 -5.27 m/s
Zone 6 > 5.27 m/s
m/min
Quarter 2 Zone 1 0 -.16m/s
Zone 2 .17 - 1.68 m/s
Zone 3 1.69 - 3.07 m/s
Zone 4 3.08 - 4.18 m/s
Zone 5 4.19 -5.27 m/s
Zone 6 > 5.27 m/s
m/min
Note: ES is effect size, p – value ≤0.05
Mean SD
0.90 ± 0.09
21.95 ± 2.09
30.14 ± 1.54
25.89 ± 1.78
14.21 ± 0.57
6.91 ± 1.24
126.83 ± 3.45
0.93 ± 0.17
23.49 ± 1.80
29.84 ± 1.88
25.13 ± 2.24
13.70 ± 0.25
6.91 ± 0.31
126.00 ± 4.34
Mean SD
0.34 ± 0.05
20.83 ± 1.92
29.20 ± 2.35
25.19 ± 2.27
16.20 ± 1.08
8.25 ± 1.20
128.90 ± 7.48
0.34 ± 0.09
23.01 ± 1.46
29.34 ± 1.61
24.56 ± 0.89
15.30 ± 1.23
7.47 ± 1.30
120.67 ± 7.20
117
Z
-1.83
-1.46
-1.10
-1.46
-1.46
-1.46
-0.73
-1.83
-0.73
-1.46
-0.73
-1.46
-1.10
-1.46
pvalue
0.07
0.14
0.27
0.14
0.14
0.14
0.47
0.07
0.47
0.14
0.47
0.14
0.27
0.14
ES
7.26
0.56
0.48
0.34
-2.30
-1.09
-0.36
4.33
0.29
0.28
0.33
-1.80
-0.59
0.90
Qualitative
Descriptor
Very Large
Small
Small
Small
Very Large
Moderate
Small
Very Large
Small
Small
Small
Large
Small
Moderate
Table 4b. Midfielders Pre 1st vs. Post 1st & 2 Pre 2nd vs. Post 2nd Wilcoxon Signed Rank Tests
Pre
Post
Zone 1 0 -.16m/s
Zone 2 .17 - 1.68 m/s
Zone 3 1.69 - 3.07 m/s
Zone 4 3.08 - 4.18 m/s
Zone 5 4.19 -5.27 m/s
Zone 6 > 5.27 m/s
m/min
Quarter 2 Zone 1 0 -.16m/s
Zone 2 .17 - 1.68 m/s
Zone 3 1.69 - 3.07 m/s
Zone 4 3.08 - 4.18 m/s
Zone 5 4.19 -5.27 m/s
Zone 6 > 5.27 m/s
m/min
Note: ES is effect size, p – value ≤0.05
Quarter 1
Mean SD
Mean SD
0.84 ± 0.19
0.36 ± 0.18
20.94 ± 3.96 20.47 ± 5.41
32.75 ± 1.64 29.94 ± 2.44
26.59 ± 2.48 26.56 ± 2.24
13.70 ± 1.61 16.14 ± 3.16
5.18 ± 1.71
6.53 ± 1.27
124.88 ± 11.90 129.82 ± 11.52
1.08 ± 0.39
0.46 ± 0.22
21.81 ± 4.40 22.62 ± 5.58
32.10 ± 2.06 30.41 ± 1.98
25.59 ± 2.63 25.49 ± 3.01
13.63 ± 1.11 14.93 ± 1.94
5.77 ± 0.63
6.10 ± 0.64
126.79 ± 9.89 119.46 ± 13.18
118
Z
-2.03
-0.14
-2.02
-0.41
-2.02
-2.02
-0.67
-2.02
-0.41
-2.02
-0.14
-1.48
-0.94
-2.02
pvalue
0.04
0.89
0.04
0.69
0.04
0.04
0.50
0.04
0.69
0.04
0.89
0.14
0.35
0.04
ES
2.59
0.10
2.44
0.01
-0.97
-0.90
-0.42
1.96
-0.16
0.84
0.03
-0.82
-0.52
0.60
Qualitative
Descriptor
Very Large
Trivial
Very Large
Trivial
Moderate
Moderate
Small
Very Large
Trivial
Moderate
Trivial
Moderate
Small
Small
Table 4c. Screens Pre 1st vs. Post 1st & 2 Pre 2nd vs. Post 2nd Wilcoxon Signed Rank Tests
Pre
Post
Zone 1 0 -.16m/s
Zone 2 .17 - 1.68 m/s
Zone 3 1.69 - 3.07 m/s
Zone 4 3.08 - 4.18 m/s
Zone 5 4.19 -5.27 m/s
Zone 6 > 5.27 m/s
m/min
Quarter 2 Zone 1 0 -.16m/s
Zone 2 .17 - 1.68 m/s
Zone 3 1.69 - 3.07 m/s
Zone 4 3.08 - 4.18 m/s
Zone 5 4.19 -5.27 m/s
Zone 6 > 5.27 m/s
m/min
Note: ES is effect size, p – value ≤0.05
Quarter 1
Mean SD
0.94 ± 0.13
21.79 ± 0.88
32.61 ± 4.58
27.35 ± 1.82
13.11 ± 2.41
4.21 ± 1.68
127.96 ± 2.83
1.14 ± 0.17
23.74 ± 2.04
32.84 ± 5.45
26.21 ± 3.13
12.25 ± 2.29
3.74 ± 1.27
119.53 ± 1.07
Mean SD
0.44 ± 0.13
21.95 ± 4.15
31.26 ± 2.00
26.87 ± 0.63
14.18 ± 3.14
5.31 ± 2.19
124.55 ± 9.78
0.80 ± 0.26
24.71 ± 4.14
31.62 ± 1.41
26.41 ± 0.82
11.97 ± 2.82
4.50 ± 1.91
114.79 ± 7.75
119
Z
-1.60
0.00
0.00
-0.54
0.00
-0.54
-0.54
-1.60
0.00
0.00
0.00
0.00
0.00
-0.54
pvalue
0.11
1.00
1.00
0.59
1.00
0.59
0.59
0.11
1.00
1.00
1.00
1.00
1.00
0.59
ES
3.76
-0.05
0.38
0.35
-0.38
-0.56
0.47
1.58
-0.30
0.31
-0.09
0.11
-0.47
0.86
Qualitative
Descriptor
Very Large
Trivial
Small
Small
Small
Small
Small
Large
Small
Small
Trivial
Trivial
Small
Moderate
Table 4d. Defenders Pre 1st vs. Post 1st & 2 Pre 2nd vs. Post 2nd Wilcoxon Signed Rank Tests
Pre
Post
pMean SD
Mean SD
Z
value
1.24 ± 0.33
0.33 ± 0.08 -1.84 0.07
Quarter 1 Zone 1 0 -.16m/s
Zone 2 .17 - 1.68 m/s
29.52 ± 4.47 28.52 ± 4.41 -1.83 0.07
Zone 3 1.69 - 3.07 m/s
37.92 ± 1.38 38.90 ± 1.61 -0.73 0.47
Zone 4 3.08 - 4.18 m/s
21.58 ± 3.22 21.58 ± 2.34 0.00 1.00
Zone 5 4.19 -5.27 m/s
7.57 ± 1.70
8.29 ± 2.18 -1.10 0.27
Zone 6 > 5.27 m/s
2.14 ± 0.62
2.40 ± 0.94 -0.37 0.72
m/min
110.04 ± 5.90 112.27 ± 6.25 -1.83 0.07
1.44 ± 0.57
0.46 ± 0.02 -1.83 0.07
Quarter 2 Zone 1 0 -.16m/s
Zone 2 .17 - 1.68 m/s
30.60 ± 5.23 30.44 ± 4.09 -0.37 0.72
Zone 3 1.69 - 3.07 m/s
37.05 ± 1.79 38.47 ± 1.66 -0.37 0.72
Zone 4 3.08 - 4.18 m/s
20.95 ± 3.75 19.93 ± 1.53 -0.73 0.47
Zone 5 4.19 -5.27 m/s
7.58 ± 2.07
8.08 ± 2.45 -0.73 0.47
Zone 6 > 5.27 m/s
2.29 ± 0.84
2.63 ± 1.11 -0.73 0.47
m/min
107.23 ± 7.63 105.70 ± 5.12 -1.10 0.27
Note: ES is effect size, p – value ≤0.05
120
ES
3.76
0.23
-0.65
0.00
-0.37
-0.32
-0.37
2.42
0.03
-0.82
0.35
-0.22
-0.34
0.24
Qualitative
Descriptor
Very Large
Small
Moderate
Trivial
Small
Small
Small
Very Large
Trivial
Moderate
Small
Small
Small
Small
Discussion
The official game time has changed from 70 minute to 60 minute. However, under the
newly implemented clock stoppage for corners, and goals, the actual game time will
depend on the number of corners, and goals awarded during the game.
Results indicate that this elite team did not show statistical changes in m/min and percent
distance covered in zones 2 – 6. The percent of distance covered in zone 1 (standing) had
a statistically significant increase. This is possibly due to the refined accuracy of the
Optimye S5 units used during post collection. The team experience decreases across the
1st and 2nd half of the game in zone 1.
During positional examination midfielders showed statically significance differences in
the 1st and 2nd half of the game pre to post. It is possible that the 2 minute rest provided
players with the ability to passively recover from the repeated sprints associated with
field hockey. This would be particularly important for midfielders, whose tactical
demands result in the playing both offense and defense. Their greater involvement gives
players less recovery time on pitch, compared to forwards and defender. The high
intensity intermittent activities of field hockey occur above most players’ critical speed
(Spencer, Bishop, Dawson, & Goodman, 2005; Spencer et al., 2004), resulting in the
need to return below critical speed to recover. The recovery time from the 2 minute rest
periods in between quarter might allow the athlete to subsequently work above critical
speed again.
121
Recovery is dictated by a multitude of factors. The more anaerobic mechanisms used
during an action the more likely it takes a longer time to recover below critical speed.
The farther below critical speed an athlete is allowed to rest, the more likely they will
recover faster. Passive recovery may facilitate quicker recovery than active recovery
(Buchheit et al., 2009). Upon numerical observation of the relative percent of total
distance covered by position, midfielders cover the least percent distance in zones 1, 2,
and 3 compared to screens and defenders. All three zones are indicative of active
recovery on pitch. Previous research has noted that forwards are substituted more often
than midfields (MacLeod, Bussell, & Sunderland, 2007; Polglaze et al., 2014) allowing
for passive recovery off the pitch.
Strategic techniques likely allow the US Women’s National Team to produce high m/min
compared other field hockey team, and other sports. The team produced 120.58 ± 10.25
m/min before the rule change and 118.16 ± 9.38 m/min after the rule change. Lythe and
Kilding (2011) reported an average of 116.6 m/min during men’s field hockey. This is
less than the average m/min produced by the Women’s National Team (Pre 1 - 124.88
±11.90 Post 1- 129.82 ±11.52 and Post 1 126.79 ± 9.89 to Post 2 119.46 ± 13.18).
However it is important to note that m/min are affect by the number of substitutions and
analysis methods used (Varley, Gabbett, & Aughey, 2014).
Conclusion
A major focus of the US Women’s National Team was to develop the athletes’ physical
capacity to maintain and repeat high intensity actions. The combination of physical
122
preparation and tactical strategies, such as player substitutions, allow the team to express
high m/min and high intensity actions throughout a match. Due to these previously used
substitutions techniques, and the high level of play examined in this study, the addition of
2 minute rest periods allowed the team to produce statistically significant high relative
action profiles during the entire game. However midfielders experienced changes during
the new rule system, possibly due to the tactical requirements of the position. They had a
decrease in zone 1, and an increase in zones 5 and 6 pre to post during the 1st half of the
game, and a decrease in zones 1 and 3 and m/min during the 2nd half of the game.
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126
CHAPTER 6
SUMMARY AND FUTURE INVESTIGATIONS
The following conclusions are draw from the presented literature review, and the
findings from the studies in this dissertation. The relative action profiles before the rule
change revealed that defenders work at a lower meter per minute (m/min) when
compared with all other positions, and that forwards, midfielders, and screens perform
similar m/min during a game, and percent of total distance covered in zone 1 through 6.
This data supports the need for position specific conditioning with defenders.
Examination of pre rule change difference from the 1st to the 2nd half play showed that
elite level field hockey players are able maintain high-intensity actions in zone 6
throughout the game by increasing actions in zones 1 and 2, and decreasing actions in
zones 4 and 5.
The relative action profiles after the rule changed revealed the team was unable to
match the percent of distance covered in zones 4 and 5 during the 1st quarter all in
subsequent quarters. The low intensity actions in zone 1 and 2 gradually increased, while
m/min gradually declined. When positional differences were examined forwards covered
the greatest percent of distance in zones 5 and 6, followed by midfielders, screens, and
defenders. This pattern varies for zone 4, within which the midfielders possesses the
greatest percent distance covered.
The comparison of relative action profile comparisons for the team, pre to post
rule change did not indicate a significant change in zones 2, 3, 4, 5, and 6. However zone
1 experience a statistically significant decrease. Positional analysis of relative action
profiles pre to post rule change showed statistically significant changes for midfielders
127
only. The changes were a decrease in zone 1, and increase in zone 5 and 6 during the first
half of the game, and decrease in zone 1 and m/min during the second half of the game.
The combination of physical preparation, tactical strategies, and elite level of the athlete’s
profiles resulted in high intensity actions throughout a match. Further research, specific to
the US Women’s national field hockey team is needed in the utilization of recovery
strategies during 2 minute rest periods and 10 minute half time. This could allow the team
to reduce the increases in in zones 1 and 2, and dampen the reduction in m/min and
percent distance covered in zones 4 and 5.
Further research done on the new FIH game formatting should consider removing the 40
second running clock during corners, and goals to allow for a better understand of the
quarter system. Standardizing the time in each quarter would allow for the examination of
m/min in relation to fatigue. In addition to examining the current definition of velocity
bands used in the literature.
Understanding the requirements of field hockey is important for developing
effective training programs. The action profiles of elite level athlete’s provide coaches
with the ability to assess athlete’s current level of fitness in relationship to those produced
at the elite level. The more accurately the published action profiles represent the actual
action produced in a game the more useful the profiles are for coaches. Previous research
using 1 and 5 Hz GPS units may have underestimated the actions performed by athletes.
Field Hockey athlete profiling needs to be examined with GPS units sampling at 10 or 15
Hz.
Athlete game action profiling is also needed on a large-scale data collection
including multiple elite women’s field hockey teams should be conducted to allow for
128
sport rather than team descriptive analysis. This process could be assisted with the
creation of standard velocity zone definitions. A standard definition of velocity zones is
needed to allow for between study comparisons. This will allow for comparisons of
varying levels, and ages of athletes. The ability to compare velocity zones between
studies will allow for the development of long-term athlete development specific to field
hockey training.
129
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APPENDIX
ETSU Institutional Review Board Approval Form
Office for the Protection of Human Research Subjects Box 70565 Johnson City, Tennessee 37614-1707
Phone: (423) 439-6053 Fax: (423) 439-6060
IRB APPROVAL – Initial Expedited Review
April 11, 2016
Heather Abbott
Re: Analysis of the US Women’s National Field Hockey team in relationship to 2015 FIH rule
change
IRB#:c0316.9sw
ORSPA #:
The following items were reviewed and approved by an expedited process:
New protocol submission xForm, references, PI CV, variables list
On March 31, 2016, a final approval was granted for a period not to exceed 12 months and will
expire on March 30, 2017. The expedited approval of the study will be reported to the convened
board on the next agenda.
A waiver or alteration of informed consent has been granted under category 45 CFR 46.116(d).
The research involves no more than minimal risk because it involves a retrospective analysis of data
only. The waiver or alteration will not adversely affect the rights and welfare of the subjects as the
athletes whose data is involved have already given consent to have their data added to the research
repository whereby data may be drawn for research purposes. The research could not practicably be
carried out without the waiver or alteration as many of the athletes in the study population are no
longer available to consent. Providing participants additional pertinent information after participation
is NOT appropriate.
The following enclosed stamped, approved Informed Consent Documents have been stamped
with the approval and expiration date and these documents must be copied and provided to each
participant prior to participant enrollment:
n/a retrospective
Federal regulations require that the original copy of the participant’s consent be maintained in the
principal investigator’s files and that a copy is given to the subject at the time of consent.
147
Accredited Since December 2005
Projects involving Mountain States Health Alliance must also be approved by MSHA
following IRB approval prior to initiating the study.
Unanticipated Problems Involving Risks to Subjects or Others must be reported to the IRB (and VA
R&D if applicable) within 10 working days.
Proposed changes in approved research cannot be initiated without IRB review and approval. The
only exception to this rule is that a change can be made prior to IRB approval when necessary to
eliminate apparent immediate hazards to the research subjects [21 CFR 56.108 (a)(4)]. In such a
case, the IRB must be promptly informed of the change following its implementation (within 10
working days) on Form 109 (www.etsu.edu/irb). The IRB will review the change to determine that it
is consistent with ensuring the subject’s continued welfare.
Sincerely,
Stacey Williams, Chair
ETSU Campus IRB
cc: Kimitake Sato
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VITA
HEATHER ABBOTT
Education:
Doctor of Philosophy in Sports Physiology
East Tennessee State University (ETSU), TN
Graduation: August 2016
Master of Education
Frostburg State University (FSU), MD
Graduation: May 2013
Bachelor of Health Science
Drexel University (DU), PA
Graduation: June 2011
Experience:
Olympic Training Site at East Tennessee State University
Head Sport Scientist Ph.D., Weightlifting 8/2013 to Current
USA Field Hockey
Assistant Sport Scientist Ph.D., 12/2013 to Current
East Tennessee State University
Teaching Associate (Teacher of Record), 8/2013 to Current
Head Coach and Strength & Conditioning Coach for the
ETSU Men's Rugby Club, 4/2014 to Current
Women’s Golf Strength and Condition Coach, 8/2013 to
4/2014
Frostburg State University
Graduate Assistant Coach Field Hockey DIII, 8/2011 to
5/2013
Publications:
Refereed Publications
Bazyler, C. D., Abbott, H. A,Bellon, C. R., Taber, C. B., & Stone,
M. H. (2015). Strength Training for Endurance Athletes: Theory to
Practice. Strength & Conditioning Journal, 37(2), 1-12.
Taber, C. B., Bellon, C. R., Abbott, H.A., Bingham, G. (2016). The
Roles of Maximal Strength and Rate of Force Development in
Maximizing Muscular Power. Strength &Conditioning Journal
38(1), 71-78.
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Refereed Proceedings
Abbott, H.A., Taber, C.B., Griggs, C., Rhudy, J., Keck, S. Power
Output at Varying Loads during Squat Jumps. East Tennessee
State University 10th Annual Coaches and Sports Science College,
December 2015.
Abbott, H.A., Taber, C.B., Ricker. D., Weiss M. Monitoring the
Effects of Volume Load on Static Jump Height in Weightlifters.
East Tennessee State University 10th Annual Coaches and Sports
Science College, December 2015.
Swisher, A.M., Abbott, H.A. A Call to Reform Coach Education in
the United States. East Tennessee State University 9th Annual
Coaches and Sports Science College, December 2014.
Swisher, A.M., Dotterweich, A.R., Clendenin, S., Palmero, M.,
Greene, A.E, Walker, J.T., Abbott, H. Using Benefits-Based
Models To Manage Sport Performance Enhancement Groups. East
Tennessee State University’s 8th Annual Coaches and Sports
Science College, December 2013.
Smith, S.A., Berry, H., Lenzi, R., Abbott, H. Effect of Altering
Stride Frequency on Constant Speed Running Economy and
Perceived Exertion. 34th Annual Meeting, Mid-Atlantic Regional
Chapter of the American College of Sports Medicine, November
2011.
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