Jeremy Williams ST02002650
Technical analysis of Euro 2004
NAME: JEREMY WILLIAMS
UNIVERSITY NUMBER: ST02002650
SCHOOL OF SPORT, P.E. & RECREATION
Jeremy Williams ST02002650
Technical analysis of Euro 2004
ANALYSIS OF TECHNICAL ABILITITIES OF SUCCESSFUL AND
UNSUCCESSFUL SOCCER TEAMS COMPETING IN THE EUROPEAN
CHAMPIONSHIPS PORTUGAL 2004
Jeremy Williams ST02002650
Technical analysis of Euro 2004
Acknowledgements
I’m going to dedicate this work to my father who sadly passed away on the 21/01/04.
Thank you to Mike Hughes for his time and guidance with the dissertation.
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Technical analysis of Euro 2004
Table of Contents
Contents
Page
Acknowledgements
I
Abstracts
II
Chapter I
1.0 Introduction
1
1.1 Summary of the study
7
1.2 History of Soccer
7
1.3 Hypotheses 1
8
1.4 Null Hypotheses
8
1.5 Hypotheses 2
8
1.6 Null Hypotheses
9
1.7 Limitations
9
1.8 Delimitations
10
1.9 Aim of the Study
10
1.10 Definition of Terms
10
Chapter II
2.0 Lit Review
11
2.1 General Aim of the Research Study
18
Chapter III
3.0 Methodology
19
3.1 Introduction
19
3.2 Subjects
19
3.3 Equipment
20
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Technical analysis of Euro 2004
Contents continued
Page
3.3.1 Pilot Study
20
3.2 Operational Definitions
21
3.3 Operational Procedure
23
3.4 Validity and Reliability
27
3.5 Data Processing
28
3.6 Statistical Analysis
28
3.7 Chi-square
28
3.8 Limits of Agreement
29
Chapter IV
4.0 Results
30
4.1 Intra-operator Results
30
4.2 Inter-operator Results
34
4.3 Technical Scores for Outfield Teams Rating Results
40
4.4 Technical Analysis of Passing
41
4.5 Technical Analysis of Receiving
42
4.6 Technical Analysis of Shots
43
4.7 Technical Analysis of Running with a ball
44
4.8 Technical Analysis of Dribbling
45
4.9 Technical Analysis of Heading
46
4.10 Technical Analysis of Crossing
47
4.11 Technical Analysis of Tackling
48
4.12 Technical Scores for Goalkeepers Rating Results
49
4.13 Technical Analysis of Goalkeepers Saving
50
4.14 Technical Analysis of Goalkeepers Catching
51
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Contents Continued
Page
4.15 Technical Analysis of Goalkeepers Punching
52
4.16 Technical Analysis of Goalkeepers Throwing
53
4.17 Technical Analysis of Goalkeepers Kicking
54
4.18 Technical Analysis of Goalkeepers Passing
55
Chapter V
5.0 Discussion
56
5.1 Reliability Discussion
56
5.2 Intra-operator Results
57
5.3 Inter-operator Results
58
5.4 Performance Profiling
59
5.5 Discussion of Teams Technical Results
60
5.6 Discussion of Goalkeepers Technical Results
66
5.7 Summarising the Results
69
5.8 Limitations of Results
70
5.9 Delimitations of Results
71
5.10 General applications to sport
71
Chapter VI
6.0 Conclusion
72
6.1 Recommendations
73
References
74
Appendices
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Illustrations of Tables
Page
Table 1. Operational Definitions for outfield players
21
Table 2. Operational Definitions for goalkeepers
22
Table 3. Operational Definitions for player ratings
22
Table 4. Time Sequence
23
Table 5. Team and Player Number
23
Table 6. Technique Performed
24
Table 7. Rating Performed
24
Table 8. Pitch Position
25
Table 9. Outcome of Technique
25
Table 10. Hand Notation System example
25
Table 11. Technical frequencies recorded for Portugal v Greece
concerning the intra-operator test for T1 and T2
30
Table 12. Technical frequencies recorded for Portugal v Greece
concerning the intra-operator test for T2 and T3
31
Table 13. Rating frequencies recorded for Portugal v Greece
concerning the intra-operator test between T1 and T2
32
Table 14. Rating frequencies recorded for Portugal v Greece
concerning the intra-operator test between T2 and T3
33
Table 15. An inter-operator test for player technique showing
the difference in percentage error using the Chi-squared
system between operator 1 and 2 for T1
34
Table 16. An inter-operator test for player technique showing
the difference in percentage error using the Chi-squared
system between operator 1 and 2 for T2
35
Table 17. An inter-operator test for player technique showing
the difference in percentage error using the Chi-squared
system between operator 1 and 2 for T3
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Illustrations of Tables Continued
Page
Table 18. An inter-operator test for technical rating showing the
difference in percentage error using the Chi-squared
system between operator 1 and 2 forT1
37
Table 19. An inter-operator test for technical rating showing the
difference in percentage error using the Chi-squared
system between operator 1 and 2 for T2
38
Table 20. An inter-operator test for technical rating showing the
difference in percentage error using the Chi-squared
system between operator 1 and 2 for T3
39
Table 21. Technical Score summations for each team’s skill rating
40
Table 22. Technical Score summations for each team’s
goalkeeper skill rating
49
Table 23. Frequencies of total technical actions for outfield N=3 Matches 85
Table 24. Frequencies of total technical actions for goalkeepers N=3 Matches 86
Table 25. Chi-Square displaying the significant difference between outfield 87
teams of a successful team Portugal and unsuccessful team Croatia
in the competition
Table 26. Chi-Square displaying the significant difference between
successful team Greece and unsuccessful team Switzerland
in the competition
88
Table 27. Chi-Square displaying the significant difference between
Goalkeepers of a successful team Portugal and unsuccessful
Croatia team in the competition
89
Table 28. Chi-Square displaying the significant difference between
Goalkeepers of a successful team Greece and unsuccessful
team Switzerland
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Illustrations of Figures
Page
Figure 1.0 Displaying the Coaching Process
6
Figure 3.0 showing the team formations to help the
researcher place them in the hand notation
24
Figure 3.1 Showing structure of the grid used to identify
players pitch position on the field
26
Figure 4.0 Technical Rates of Successful Teams Passing
41
Figure 4.1 Technical Rates of Unsuccessful Teams Passing
41
Figure 4.2 Technical Rates of Successful Teams Receiving
42
Figure 4.3 Technical Rates of Unsuccessful Teams Receiving
42
Figure 4.4 Technical Rates of Successful Teams Shots
43
Figure 4.5 Technical Rates of Unsuccessful Teams Shots
43
Figure 4.6 Technical Rates of Successful Teams Running with a ball (RB) 44
Figure 4.7 Technical Rates of Unsuccessful Teams Running with a ball (RB) 44
Figure 4.8 Technical Rates of Successful Teams Dribbling
45
Figure 4.9 Technical Rates of Unsuccessful Teams Dribbling
45
Figure 4.10 Technical Rates of Successful Teams Heading
46
Figure 4.11 Technical Rates of Unsuccessful Teams Heading
46
Figure 4.12 Technical Rates of Successful Teams Crossing
47
Figure 4.13 Technical Rates of Unsuccessful Teams Crossing
47
Figure 4.14 Technical Rates of Successful Teams Tackling
48
Figure 4.15 Technical Rates of Unsuccessful Teams Tackling
48
Figure 4.16 Technical Ratings of Successful Goalkeepers Saves
50
Figure 4.17 Technical Ratings of Unsuccessful Goalkeepers Saves
50
Figure 4.18 Technical Ratings of Successful Goalkeepers Catching
51
Figure 4.19 Technical Ratings of Unsuccessful Goalkeepers Catching
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Illustrations of Figures Continued
Page
Figure 4.20 Technical Ratings of Successful Goalkeepers Punching
52
Figure 4.21 Technical Ratings of Unsuccessful Goalkeepers Punching
52
Figure 4.22 Technical Ratings of Successful Goalkeepers Throwing
53
Figure 4.23 Technical Ratings of Unsuccessful Goalkeepers Throwing
53
Figure 4.24 Technical Ratings of Successful Goalkeepers Kicking
54
Figure 4.25 Technical Ratings of Unsuccessful Goalkeepers Kicking
54
Figure 4.26 Technical Ratings of Successful Goalkeepers Passing
55
Figure 4.27 Technical Ratings of Unsuccessful Goalkeepers Passing
55
Figure 5.0 Displaying prime target area for shots on goal
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Technical analysis of Euro 2004
Abstract
Although soccer has received a major share of the research by notational analysts over
the last five decades (Pearce and Hughes, 2001) not much analysis has been
completed on technical abilities in this area of soccer. The aim of this study is to
investigate the technical differences between teams in Groups A and B of the
European Championships Portugal 2004 and whether there are any technical
differences between successful and unsuccessful teams and their goalkeepers. Is the
reason Greece were the Champions because they have a technical advantage over the
rest of the teams or was it just down to other contributing factors?
The data was collected from 12 matches carried out in the 2004 European
Championships in Portugal. Each game involved two European national teams, highly
trained elite soccer teams, with each team comprising of at least 11 players of mixed
age and relative technical ability.
The reliability of the data produced results using a chi-square system and the
percentage error to test for reliability. The intra-operator tests for technical
frequencies actions between T1 and T2 displayed no significant difference (P>0.95).
T2 and T3 also displayed no significant difference (P>0.95). The intra-operator tests
for technical frequencies ratings between T1 and T2 displayed a significant difference
(P<0.95). There was no significant difference between T2 and T3 (P>0.95).
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The inter-operator data for technical frequency actions for T3’s displayed a no
significant difference of (P>0.95) and the technical frequency rating for T3’s
displayed no significant difference (P>0.95).
In conclusion to the findings the results suggest that there is a significant difference in
technical abilities between successful and unsuccessful outfield players (P<0.05).
Also, there is a significant difference in technical abilities between successful and
unsuccessful team’s goalkeepers within the study (P<0.05). From a coaching concept
the data produced from the results section can hopefully show that technical ability is
an important contributor to individual’s performances from a coaching aspect.
Further recommendations would suggest to analysis all the teams in the competition
to provide a true accurate analysis of technical abilities of teams in the tournament.
The Soccer World Cup 2006 would be a good analysis for future research in soccer
tournaments. The best teams from around the world will be competing in the
competition such as the likes of South American, African and Asian teams, which can
provide a more accurate account of technical abilities. The teams could be ranked
from the best to the worst technical teams in the world.
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Technical analysis of Euro 2004
CHAPTER I
INTRODUCTION
Jeremy Williams ST02002650
Technical analysis of Euro 2004
1.0 Introduction
Notational analysis is a method used in modern sport to record positions and actions
of players during competitive situations. It is also a systematic gathering, analysing
and communication of accurate information relating to competitive sport, which is
used within a variety of sports coaching, academic and broadcasting purposes.
Notational analysis was designed for precise and objective analysis within sport. The
quantified information provided by notational analysis helps avoid coach
misperceptions and identifies facets of the game that require attention (O’Donoghue
et al., 1996). Notational analysis is also a scientific based method of observation,
which has been successfully used within previous sports research for the quantitative
measurements of movement variables and appraisal of skill performance (Reilly and
Thomas, 1976; Ali and Farrally, 1991; Robinson et al., 1994; Carter, 1996). Without
such methods players and coaches would have to rely on their own impressions and
prejudices in developing their game plan (Croucher, 1994).
The earliest publication in notation of sport is that by Fullerton (1912), which
explored the combinations of baseball players batting, pitching and fielding with the
probabilities of success. The first attempt to devise a hand notation system
specifically for sport analysis was that by Messersmith and Bucher (1939), who
attempted to notate distance covered by specific basketball players during a match
(Hughes and Franks, 2004).
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Notation systems were commercially available for analysis of American football as
early as 1966 and the Washington Redskins were using one of the first in 1968
(Witzel; cited by Hughes and Franks, 2004). Computerised notational analysis were
banned from American football stadiums so teams had to use hand notations and
transfer data onto the computer after the game (Hughes, 1996; Hughes and Franks,
2004). The first publication of a comprehensive sport notation system in Britain was
that by Dowey (1973), who developed a detailed system which allowed the
comprehensive notation of lawn tennis matches. As early as the late 70’s, the majority
of what little research was published in game analysis was concerned with basketball
and soccer, which was at a fairly global and unsophisticated level (Hughes, 1996).
The first systems tended to produce tables and data, often incorporated with statistical
tests for significance. Coaches or athletes attempting to adopt these systems were
frequently confused by this ‘scientific’ form of communication of match data
(Hughes, 1996). Franks et al. (1986); (cited in Hughes and Franks, 2004) made an
attempt to present the coach with the possibility of immediate analysis combined with
the visual presentation of the feedback of the action. Analysis of a game can be
performed either qualitatively or quantitatively where it is often imprecise, subjective
and ambiguous (Erdmann, 1993). Analysis of soccer matches for many years have
been based on ‘observation sheets or hand notations’ filled in during matches with
modern ways of match analysis being developed in the early 1980’s (Erdmann, 1993;
Hughes and Franks, 2004).
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The introduction of computerised notation systems has enabled immediate easy data
access and the presentation of data in graphical and table format so that information
could be easily understood by coaches and athletes (O’Donoghue et al., 1996;
Hughes, 2001). Computer and video based analysis can support and enhance the
coach’s impressions and evaluations, if the complex material is prepared in a clear
and coherent way (Hughes, 1990; Tiryaki et al., 1997). The increasing sophistication
and reducing cost of video systems has greatly enhanced post-event feedback, from
playback with subjective observations by a coach to detailed objective analysis by
means of notation systems (Brown and Hughes, 1995).
Since the beginning of both hand and computerised systems (Reilly and Thomas,
1976; Franks et al., 1983) through to the development of contemporary software
packages, soccer received considerable attention from researchers in the notational
analysis literature (Reilly and Thomas, 1976; Withers et al., 1982; Mayhew and
Wenger, 1985; Yamanaka et al., 1993; and James et al., 2002). However, as
Grehaigne et al., (2001) suggested, for researchers and practitioners to benefit from
analysis of soccer where there is a need to move beyond the mere description of
behaviours and progress towards prediction of individual technical performances.
Besides notational work in soccer, squash has had major influences in notation
(Brown and Hughes, 1995; Hughes and Knight, 1995). This has changed considerably
now and work and publications can be found on most sports such as tennis
(O’Donoghue and Liddle, 1998), netball (Palmer et al., 1994), Volleyball (Handford
and Smith, 1996) and rugby union (Docherty et al., 1988).
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There are four major purposes of notation analysis with studies to relate to them:
Analysis of movement: A study by Starosta and Berger, (1993) based their study on
patterns of a sport technique in football on the symmetry of movements, which is seen
in football as the identical effectiveness of using both feet. Grehaigne et al., (1997)
based a study on attacking moves in soccer. These and other studies in analysis of
movements in soccer can generate information to a coach, which can be applied in
training and matches to improve performances and also to understand the opponent’s
movement patterns during a game.
Tactical evaluation: Ali. (1988) based a study on tactical movement patterns in soccer
and Bate. (1988) based a study on tactics and strategy. Pearce and Hughes. (2001)
based a study on analysis of substitutions during the European championships 2000.
This study was useful as it analysed whether it had a positive or negative impact on a
team’s performance when a sub came on. Hook and Hughes. (2001) examined the
attacking styles of play culminating in a shot or a goal. In soccer to organize team
formation is an important factor for a coach to consider to overcome the opposition
and to make substitutions to change the outcome of a game. These studies can supply
a coach with information on possible tactical strategies and ways of using a sub in an
effective way during a game of soccer.
Technical evaluation: A study by Partridge et al. (1988) examined computer assisted
analysis of technical performances and Bishovet’s et al. (1993) used computer
analysis of the effectiveness of collective technical and tactical moves in matches of
1988 Olympics and 1990 World Cup. Another study by Hill and Hughes. (2001)
based a study on the effectiveness of corner kicks in the European Championships for
association football 2000.
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These studies can supply a coach with effective uses of coaching technical work for
player development and the effectiveness of set-pieces in games that can have
influential factors in winning and losing a match.
Statistical compilation: Sforza et al. (1997) analysed penalties in soccer by statistical
evaluation and James et al. (2002) analysed strategies in soccer as a function of
European and domestic competition. The study addressed British teams preferred
playing long balls, while European teams played short passes, runs, and dribbles
reducing the risk of losing possession. Statistical analysis can produce information for
a coach on different team styles of play and identify the team’s strengths and
weaknesses. Many of the traditional systems are concerned with the statistical
analysis of events, which previously had to be recorded by hand (Hughes and Franks,
2004), which is a simple form of collecting data (Treadwell, 1988).
All these different aspects and resources used in sport notation can have beneficial
factors for educating players and coaches in improving performance levels by
planning practices based on these analyses. Information of players variables about
their performance is one of the most important factors affecting the learning and
subsequent execution of a motor skill. Providing feedback to teams about tactical
performance and to individuals about technical performance within the game
significantly modifies playing behaviour toward a predefined model of performance
(Franks and McGarry, 1996).
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Team sports can benefit immensely from the development of computerised notation;
the information derived can be used for a number purposes i) immediate feedback, ii)
development of database, iii) indication of areas requiring improvements, iv)
evaluation, v) a mechanism for selective searching through a video recording of the
game (Franks et al., 1983; cited in Hughes and Franks, 2004), which are important to
the coaching process, the initial reason for performance analysis. The coaching
process is to improve performance in competitive sport by observation, identification
of technical or tactical faults and correcting those faults through training and
presentation. The coach requires information to provide feedback to the players so as
they can reflect on their performances (figure 1.0) (O’Donoghue et al., 1996 and
Garganta et al., 1997).
Figure 1.0 displaying the coaching process (Hughes et al., 2004)
Feedback can be provided before, during or after competition (O’Donoghue et al.,
1996; Hughes, 1995). Coaches must be able to pinpoint weaknesses of attacking skill
and team work such as few attacks and few scoring opportunities created (Garganta
and Goncalves, 1994), as it is the responsibility of the coach to teach the athlete what
to do, how to do it and hopefully how to do it well (Hughes and Franks 2004).
Coaching is a deliberate act of intervention in sport with the intention of improving
performance using effective ways such as match analysis. The purpose of match
analysis is to evaluate performance in order to inform coaching process (Tiryaki, et
al., 1994; O’Donoghue et al., 1996), which concentrates on technical events and
effectiveness (O’Donoghue et al., 1996).
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Coaches are under constant pressure to optimise sporting performances (Treadwell,
1988) and they must prepare themselves in order to be successful in an ever more
complex and constantly changing world. In order to keep up with the changes, it is
necessary for coaches to review and update their knowledge and skills more
frequently than in the past. Technology can play a role in providing coaches with
quality information in a timely fashion, which should be easily assessable and provide
the user with the opportunity to store, retrieve and utilize the data when required in an
effective and easy fashion (Kilb et al., 2001).
1.1 Summary of the study
Little research has gone into the technical aspects of soccer in notational analysis so
this study will analysis the technical aspects of successful and unsuccessful teams and
goalkeepers in Groups A and B of the European Championships in Portugal 2004.
1.2 History of Soccer
Britain is the birthplace of modern soccer/association football especially with
Scotland and England being the co-founders of the organised game. Football in
Britain was a popular sport of the masses from the 8th century onwards in England,
King Edward III passed laws in 1331 to try and suppress football. In Scotland, King
James 1, in 1424, proclaimed in Parliament, "That no man shall play football”. This
was the result of all men had to spend more time practicing their archery skills
(worldsoccer.about.com 2004).
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In 1815, the famous English School, Eton College, established a set of rules which
other schools, colleges and Universities began to use (worldsoccer.about.com 2004).
1863 the Football Association was formed with revised rules, which was firstly
played by upper-class people and then the picture changed as the game developed into
industrial English midlands. In 1904 FIFA was formed and seven nations were
formed, but the British teams refused to join (worldsoccer.about.com 2004).
1.3 Hypotheses 1
There is a significant difference in technical abilities of successful teams than
unsuccessful teams in the European Championships Portugal 2004.
1.4 Null Hypotheses
There is no significant difference in technical abilities of successful teams than
unsuccessful teams in the European Championships Portugal 2004.
1.5 Hypotheses 2
There is a significant difference in technical abilities of successful team’s goalkeepers
than unsuccessful team’s goalkeepers in the European Championships Portugal 2004.
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1.6 Null Hypotheses
There is no significant difference in technical abilities of successful teams
goalkeeper’s than unsuccessful team’s goalkeepers in the European Championships
Portugal 2004.
1.7 Limitations
There are number limitations to the study that need to be taken into consideration:
1). Time: the time it will take to watch one 90-mintue game can take up a lot of time
and patience for accurate recordings.
3). Quality of video: when recording each game to analyses the data to produce results
it is important that a new video is used to gain accurate observations. If an old video is
to be used then it will be hard to identify the performers in the game (player’s
numbers) as the quality of the picture will be harder to observe.
4). Injuries: injuries to key players for a team can cause problems to the way the
results are presented. If players pick up an injury and pull out of the competition then
it may make the results less reliable to the study.
5). Replays: When a goal is scored the TV production will tend to edit the repeat of a
shot on goal, an attack or a foul, which in turn will disrupt the action of the game and
can miss technical aspects of play.
6). Biased: In the tournament itself the analyser may take a liking to certain players or
a certain national team and may be biased in producing higher ratings for that
individual or team.
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1.8 Delimitations
1). All players are of elite standard and that all the European teams are in it to win the
competition.
2). the study cannot be compared to non-elite performers because of the professional
standard set by the elite athletes and the difference in performance levels.
3). Due to the number of technical aspects of soccer the study will not include unusual
techniques that may be attempted by certain performers.
1. 9 Aim of the study
The aim of this study is to investigate the technical differences between teams in
Groups A and B of the European Championships Portugal 2004 and whether there are
any technical differences between successful and unsuccessful teams and team’s
goalkeepers. Is the reason Greece were the Champions because they have a technical
advantage over the rest of the teams or was it just down to other contributing factors?
1.10 Definition of Terms
Successful Teams = Teams who qualified from the group stages
Unsuccessful Teams = Teams who didn’t qualify from the group stages
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CHAPTER III
METHODOLOGY
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Technical analysis of Euro 2004
3.0 Methodology
3.1 Introduction
The study used a notation system designed by the students from UWIC who will be
assessing the technical abilities of performers in the 2004 European finals in Portugal.
In order to make inferences regarding technical abilities of subjects under analysis in
the competition all matches were recorded from ITV 1 and 2, BBC 1 and Eurosport
television.
3.2 Subjects
The data was going to be collected from 31 matches carried out in the 2004 European
Championships in Portugal, but due to other circumstances this could not be carried
out so it was decided to analysis 12 matches between two researchers. Each game
involved two European national teams, highly trained elite soccer teams, with each
team comprising of at least 11 players of mixed age and relative technical ability. The
number of participants was dependent upon substitutions throughout the matches
(Dudley, 2003). A system was set up at UWIC between four year 3 sport coaching
students to ensure the notation of the matches. Each of the researchers would notate a
group each and follow their teams through the stages of the competition to allow data
from all 31 games to be recorded. Therefore, the author was required to notate the
following matches:
Group A
Portugal v Greece
Spain v Russia
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Greece v Spain
Russia v Portugal
Spain v Portugal
Russia v Greece
Quarter-Final
Semi-Final 1
Final
Portugal v England
Portugal v Holland
Portugal v Greece
3.3 Equipment
The data was collected from video recordings from television coverage and was
viewed post-event using a Toshiba MV13P2 13-Inch/VCR colour television (Toshiba
America, Inc. 1251 Avenue of the Americas, Suite 4110, New York, NY 10020). By
viewing the video footage of the subjects under analysis the data was then recorded
using a pencil onto a good hand notation system that can accurately identify technical
analysis over 90-minutes of play. The ‘stop-pause’ capabilities of the Toshiba VC602
remote control (Toshiba America, Inc. 1251 Avenue of the Americas, Suite 4110,
New York, NY 10020) allowed each frame to be viewed accurately and lead to
optimum accuracy when notating.
3.3.1 Pilot Study
As with most studies concerned with data collection within notation, a pilot study was
required to test the accuracy and suitability of the system to be used (Dudley, 2003).
A pilot study must be done, which helps with the training and designing of the handnotation system that enables the researcher to calculate the time it will take to analysis
all the matches.
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The pilot study also helps to identify the limitations while operating the notation
system and deciding on a final system ready for re-training making the study as
reliable as possible. The operators analysed the first ten minutes of the European
Championship final alone after having talked through the system beforehand. The
results were then compared and discrepancies were discussed. This process was
repeated three times until the results across the researchers became universal.
3.2 Operational Definitions
It was important to distinguish operational definitions to prevent any confusion during
analysis due to differing perceptions regarding the player’s actions (Dudley, 2003).
The operational definitions of the technical aspects for the outfield players and
goalkeepers under analysis were classified into a number of categories (See table 1.
and 2.). When a subject under analysis completed a technical movement a rating was
also given (see table 3.).
Table 1. Operational definitions for outfield players
Technique
P
R
S
RB
=
=
=
=
Pass:
Receiving the ball:
Shot:
Running with ball:
D
=
Dribbling:
H
=
Header:
C
=
Cross:
T
=
Tackle:
Operational Definitions
How the ball is given from one player to another
A players attempt to gain control of the ball
A players attempt to score
When a player moves in any direction with the
ball
When a player keeps possession of the ball
against opponents attempts to retrieve it
Transferring the ball in any direction with the
use of the head
When a player plays a horizontal or diagonal
delivery into the box of the opposition
Challenging for the ball from an opponent
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Table 2. Operational Definitions for goalkeepers
Technique
Operational Definitions
KS
=
Save:
When the ball is prevented from going into the
goal
KC
=
Catch:
When the ball is clutched from the air and held
KP
=
Punch:
When the ball is cleared by using the fist
KT
=
Throw:
When the ball is transferred using a throw
KK
=
Kick:
A clearing kick, either from the floor or from the
hands
KPA
=
Pass:
Transferring the ball to another player using the
feet
Table 3. Operational Definitions for Player ratings
Ratings
Operational Definitions
+3 =
Excellent technique performed under pressure
+2 =
Very good technique under slight pressure
+1 =
Good technique under no pressure
0 =
Average, standard technique
-1 =
Poor technique performed under pressure
-2 =
Very poor technique performed under slight pressure
-3 =
Unacceptable technique under no pressure
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Technical analysis of Euro 2004
3.3 Operational Procedure
Prior to observing the match’s information regarding the players names, numbers and
teams were noted onto a plane piece of paper in team formation to familiarise the
researcher with the teams. The whole study of data analysis will be very subjective as
coaching intervention has been based upon subjective observations of performers
(Hughes and Franks, 1997). The formatting of a system to be used during the analysis
of the European Championships in Portugal 2004 will be broken down to enable the
researcher to familiarise the process of analysis. The researcher will record the
technical capabilities of subject’s under analysis.
Table 4. Time Sequence
Time/1st half
5 minutes
8 minutes
9 minutes
15 minutes
In the first box it will indicate the time of each technical attempt by each performer
that can help identify what parts of the game were mainly active.
Table 5. Team and Player Number
Team/Player No
F2
E4
F6
E10
Each time a performer attempts a technique the researcher will indicate this by
placing the team’s letter by (France -F) in the box with the player’s position or team
number for the squad (2). The teams will have a formation system so the researcher
understands who is in the starting line-up and for (See figure 3.0)
23
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Technical analysis of Euro 2004
France 4-4-2
g
F2
F5
F6
F3
F7
F4
F8
F11
F9
F10
E9
E10
E11
E4
E8
E7
E3
E5
E4
E2
England 4-4-2
Figure 3.0 showing the team formations to help the researcher place them in the hand notation
Table 6. Technique Performed
Indicating technique performed by the researcher will place a key into the technique
performed by each individual. Each technique will have a key (e.g. P = Pass, S =
Shot, etc).
Technique
Performed
P
S
R
H
Table 7. Rating Performed
When a technique is performed the researcher will indicate this by placing a technical
rating in the column next to the technique performed (e.g. 3+ = excellent and 3- =
unacceptable).
Technical rating
1+
3+
13+
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Table 8. Pitch Position
When the subject under analysis attempts a technique the researcher will identify this
on the pitch by using a grid system from 1 to 12.
Grid Position
6
7
2A
11
To enable the analyst to determine the positioning of attacks, the area of the pitch was
divided into 12 small grids with the goalkeepers area divided into even smaller grids
from which action frequencies were recorded (see figure 3.1).
Table 9. Outcome of Technique
When a technique is attempted the researcher can place an outcome in the box such as
a shot on goal and the performer scored or a tackle and the performer was shown a
yellow card.
Outcome
of
technique
Attack in final
third
Goal
Lost possession
Goal
Table 10. Hand Notation System example
This is an example of the overall hand notation analysis design that will record all the
data from each in the European Championships in Portugal 2004.
Time/1st half
Player
Number/
Team
F2
Skill
Performed
Skill Rating
Grid Position
Outcome
technique
P
1+
6
8 minutes
E4
F6
S
R
3+
1-
7
2A
Attack in final
third
Goal
Lost possession
15 minutes
E10
H
3+
11
Goal
5 minutes
25
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Jeremy Williams ST02002650
Technical analysis of Euro 2004
11
10
Direction of play
12
C
B
A
D
E
F
80 yards
7
8
9
120 yards
4
5
20 yards
6
40 yards
1
2
3
30
yards
F
E
D
C
B
A
2
Figure 3.1 Showing structure of the grid used to identify players pitch position on the field (James et
al., 2002)
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3.4 Validity and Reliability
The reliability of the system is essential in collecting consistent and accurate data and
to do this both intra-reliability and inter-reliability tests will be carried out (Dudley,
2003). The tests will show the consistency of each operator in collecting data from the
same sample of data on three separate occasions (Dudley, 2003 and Hughes and
Franks, 1997). It is important that the reliability of the data gathering system is
established and in a way that is compatible with the intended analyses of the data
(Hughes and Franks, 2004). The data must also be tested in the same way and to the
same depth in which it will be processed in the analysis (Hughes et al., 2004). The
reliability tests will be carried out on the first 10-minutes in the first half of the
European final 2004, between Portugal and Greece, hence allowing a test-retest-retest
method to identify a degree of similarity between the operators. Attention was taken
to all variables from the operational definitions and the operational procedure.
The percentage error will be found using the following equation below:
% Error =∑ (mod) [T1-T2] /Sum (S1) x 100%
The formula above will give the researcher an insight and an accurate account of the
consistency, validity and reliability of the data obtained through the system. Also a
chi-square system was used to test the intra-inter reliability tests. The P value will be
used to indicate any significance of intra and inter-operator reliability between
technical actions and technical ratings at the 95% level of significance.
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3.5 Data Processing
Data was placed into Microsoft Excel and a chi-square system adapted by
O’Donoghue, 2001; to test the reliability as the presented data was arranged into
categories by rating frequency counts (Vincent, 1999). When data was placed into
Excel the graphs in the results section showed different sizes and scales, which could
not be rectified.
3.6 Statistical Analysis
The appropriateness of the statistical test used in the study was dependent upon the
type of data available. There are two categories of statistical tests: parametric and
non-parametric (Dudley, 2003). As the data were nominal and did not apply with the
assumptions of normality in parametric testing, a non-parametric test (chi-square) was
chosen (Dudley, 2003 and Vincent, 1999).
3.7 Chi Square
Chi-square equation is a nonparametric statistical technique for determining the
significance of the difference between frequency counts on nominal data (Vincent,
1999). Comparisons are made between the observed frequency in each group and the
expected findings (Thomas and Nelson, 1996).
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Thomas and Nelson, (1996) outline the restrictions in using Chi-square, which include
(Dudley, 2003):
1. The observations must be independent and the categories mutually exculsive.
2. Observed frequencies or number of occurrences should be tested-ratios and
percentages are not appropriate.
3. The total of the expected frequencies and the total of the observed frequencies
of any classifications should be equal.
4. Chi-square is not applicable to small samples an adequate sample size is
required.
3.8 Limits of Agreement
The gathering of the data was tested at a 5% level of significance. The 5% level
shows an appropriate level when testing notation systems. If the value recognized
through the formula is below 5% the data is not due to chance (Vincent, 1996).
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CHAPTER IV
RESULTS
Jeremy Williams ST02002650
4.0
Results
4.1
Intra-operator Results
Technical analysis of Euro 2004
The reliability test was performed by the same operator three times on the first 10minutes of the first half of the European Championships final in Portugal 2004
Intra-operator test for the Sum of T1 and T2 recording the technical aspects:
% Error = Sum (Modulus / sum (T1 & T2)
= 5 / 125 x 100
= 4 % Error
Table 11. Technical frequencies recorded for Portugal v Greece concerning the intraoperator test for T1 and T2
Technique
Pass
Receive
Shot
RB
Dribble
Header
Cross
Tackle
Keeper Pass
Total
DF
Chi square
P value
T1
63
33
1
1
3
10
1
10
2
124
T2 Mod T1 &T2
61
-2
34
-1
1
0
2
-1
4
-1
10
0
1
0
11
-1
2
0
126
5
Abs
Diff Mean
%Error
2
62 3.225806
1
33.5 2.985075
0
1
0
1
1.5 66.66667
1
3.5 28.57143
0
10
0
0
1
0
1
10.5 9.52381
0
2
0
6
125
8
0.999802
The Chi square P value of 0.99 indicates that reliability does occur between T1 and
T2 for intra-observer action observation at the 95% level of significance.
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Technical analysis of Euro 2004
Intra-Operator test for the Sum of T2 and T3 recording the technical aspects:
% Error = Sum (Modulus / sum (T2 & T3)
= 4 / 138.5 x 100
= 3.1 % Error
Table 12. Technical frequencies recorded for Portugal v Greece concerning the intraoperator test for T2 and T3
Technique
Pass
Receive
Shot
RB
Dribble
Header
Cross
Tackle
Keeper Pass
Total
DF
Chi square
P value
T2
61
34
1
2
4
10
1
11
2
126
T3 Mod T2 & T3
63
1
35
1
1
0
2
0
6
2
10
0
1
0
11
0
2
0
131
4
Abs
Diff
2
1
0
0
2
0
0
0
0
5
Mean
62
34.5
1
2
5
10
1
11
2
128.5
%Error
3.225806
2.898551
0
0
40.0
0
0
0
0
8
0.999966
The Chi square P value of 0.99 indicates that reliability does occur between T2 and
T3 for intra-observer action observation at the 95% level of significance.
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Intra-Operator test for the Sum of T1 and T2 recording the technical aspects:
% Error = Sum (Modulus / sum (T1 & T2)
= 8 / 121 x 100
= 6.6 % Error
Table 13. Rating frequencies recorded for Portugal v Greece concerning the intraoperator test between T1 and T2
Rating
+3
+2
+1
0
-1
-2
-3
Total
DF
Chi square
P value
T1
0
11
65
37
4
0
1
118
T2 Mod T1 & T2
1
-1
12
-1
68
-3
38
-1
3
1
1
-1
1
0
124
8
Abs
Diff
1
1
3
1
1
1
0
8
Mean
0.5
11.5
66.5
37.5
3.5
0.5
1
121
%Error
200
8.695652
4.511278
2.666667
28.57143
200
0
6
0.908349
The Chi square P value of 0.90 indicates that reliability does not occur between T1
and T2 for intra-observer action observation at the 95% level of significance.
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Technical analysis of Euro 2004
Intra-operator test for the Sum of T2 and T3 recording the ratings of each technique:
% Error = Sum (Modulus / sum (T2 & T3)
= 7 / 127.5 x 100
= 5.4 % Error
Table 14. Rating frequencies recorded for Portugal v Greece concerning the intraoperator test between T2 and T3
Rating
+3
+2
+1
0
-1
-2
-3
Total
DF
Chi square
P value
T2
1
12
68
38
3
1
1
124
T3
2
13
69
39
5
2
1
131
Mod T2 & T3
1
1
1
1
2
1
0
7
Abs
Diff
1
1
1
1
2
1
0
7
Mean
1.5
12.5
68.5
38.5
4
1.5
1
127.5
%Error
66.66667
8
1.459854
2.597403
5
66.66667
0
6
0.984228
The Chi square P value of 0.98 indicates that reliability does occur between T2 and
T3 for intra-observer action observation at the 95% level of significance.
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4.2 Inter-operator Results
Table 15. An inter-operator test for player technique showing the difference in
percentage error using the chi-squared system between operator 1 and 2 for T1
= 15 /129.5 x 100
= 11.5% Error
Rating
Pass
Receive
Shot
RB
Dribble
Header
Cross
Tackle
Keeper Pass
Operator 1
T1
61
38
1
9
3
10
1
10
2
Operator 2
T1
63
33
1
1
3
10
1
10
2
Description of variable
SUM
T1
135
T2
124
DF
Chi square
P value
Abs
Diff
2
5
0
8
0
0
0
0
0
Abs
Diff
15
Mean
62
35.5
1
5
3
10
1
10
2
%Error
3.225806
14.08451
0
160
0
0
0
0
0
Mean
129.5
%Error
8
0.610475
A P value of 0.61 indicates not a strong inter-operator reliability between action
observations at the 95% level of significance.
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Technical analysis of Euro 2004
Table 16. An inter-operator test for player technique showing the difference in
percentage error using the chi-squared system between operator 1 and 2 for T2
= 5/125.5 x 100
= 3.9% Error
Rating
Pass
Receive
Shot
RB
Dribble
Header
Cross
Tackle
Keeper Pass
Operator 1
T2
62
32
1
3
3
10
1
11
2
Operator 2
T2
61
34
1
2
4
10
1
11
2
Description of variable
SUM
T1
125
T2
126
DF
Chi square
P value
Abs
Diff
1
2
0
1
1
0
0
0
0
Abs
Diff
5
Mean
61.5
33
1
2.5
3.5
10
1
11
2
%Error
1.626016
6.060606
0
40
28.57143
0
0
0
0
Mean
125.5
%Error
8
0.999939
A P value of 0.99 indicates a strong inter-operator reliability between action
observations at the 95% level of significance.
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Jeremy Williams ST02002650
Technical analysis of Euro 2004
Table 17. An inter-operator test for player technique showing the difference in
percentage error using the chi-squared system between operator 1 and 2 for T3
= 7/128.5 X 100
=5.4% Error
Operator 1
T3
62
33
1
3
3
10
1
11
2
Description of variable
Pass
Receive
Shot
Running with Ball
Dribble
Header
Cross
Tackle
Keeper Pass
Description of variable
SUM
DF
Chi square
P value
T1
Operator 2 Abs
T3
Diff Mean
%Error
63
1
62.5
1.6
35
2
34 5.882353
1
0
1
0
2
1
2.5
40
6
3
4.5 66.66667
10
0
10
0
1
0
1
0
11
0
11
0
2
0
2
0
Abs
T2
Diff Mean
%Error
126
131
7
128.5
8
0.99693
A P value of 0.99 indicates an inter-operator reliability between action observations at
the 95% level of significance.
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Technical analysis of Euro 2004
Table 18. An inter-operator test for technical rating showing the difference in
percentage error using the chi-squared system between operator 1 and 2 for T1’s
=24/127 x 100
=18.8% Error
Rating
+3
+2
+1
0
-1
-2
-3
Operator 1
T1
1
8
66
48
8
4
1
Operator 2
T1
0
11
65
37
4
0
1
Description of variable
SUM
T1
136
T2
118
DF
Chi square
P value
Abs
Diff
1
3
1
11
4
4
0
Abs
Diff
24
Mean
0.5
9.5
65.5
42.5
6
2
1
%Error
200
31.57895
1.526718
25.88235
66.66667
200
0
Mean
127
%Error
6
0.321057
A P value of 0.32 indicates not a strong inter-operator reliability between action
observations at the 95% level of significance.
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Jeremy Williams ST02002650
Technical analysis of Euro 2004
Table 19. An inter-operator test for technical rating showing the difference in
percentage error using the chi-squared system between operator between operator 1
and 2 for T2’s
=7/124.5 x 100
=5.6% Error
Rating
+3
+2
+1
0
-1
-2
-3
Operator 1
T2
1
10
67
40
4
2
1
Operator 2
T2
1
12
68
38
3
1
1
Description of variable
SUM
T1
125
T2
124
DF
Chi square
P value
Abs
Diff
0
2
1
2
1
1
0
Abs
Diff
7
Mean
1
11
67.5
39
3.5
1.5
1
%Error
0
18.18182
1.481481
5.128205
28.57143
66.66667
0
Mean
124.5
%Error
6
0.994213
A P value of 0.99 indicates an inter-operator reliability between action observations at
the 95% level of significance.
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Jeremy Williams ST02002650
Technical analysis of Euro 2004
Table 20. An inter-operator test for technical rating showing the difference in
percentage error using the chi-squared system between operator 1 and 2 for T3’s
=12/128 x 100
=9.3% Error
Rating
3+
2+
1+
0
123-
Operator 1
T3
1
11
64
42
4
2
1
Operator 2
T3
2
13
69
39
5
2
1
Description of variable
SUM
T1
125
T2
131
DF
P
Abs
Diff
1
2
5
3
1
0
0
Abs
Diff
12
Mean
1.5
12
66.5
40.5
4.5
2
1
%Error
66.66667
16.66667
7.518797
7.407407
22.22222
0
0
Mean
128
%Error
6
0.992854
A P value of 0.99 indicates an inter-operator reliability between action observations at
the 95% level of significance.
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Jeremy Williams ST02002650
Technical analysis of Euro 2004
4.3 Technical Scores for Outfield Teams Rating Results
Table 21. Technical Score summations for each team’s skill rating
Portugal Greece Spain Russia France England Croatia Switzerland
Pass
1035
418
881
551
594
562
449
336
Receive
792
356
667
491
119
143
81
66
Shot
33
14
8
10
-1
21
14
12
RB
86
52
39
53
148
119
120
94
Dribble
251
71
139
95
162
60
136
53
Header
152
140
72
99
122
181
145
96
Cross
83
35
80
33
47
58
41
37
Tackle
14
94
31
57
113
115
160
138
Total
Rating
Frequency
2446
1180
1917
1389
1304
1259
1146
832
Mean
305.7
147.5
239.6
173.6
163
157.3
1432.2
104
Table 21 displays outfield team’s technical rating frequencies over a total of three
games during the group stages of the tournament.
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4.4 Technical Analysis of Passing
Passing Rates of Successful Teams Total N=3 Matches
700
Cumulative Frequency
600
Portugal
500
Greece
400
France
300
England
200
100
0
3+
2+
1+
0
1-
2-
3-
Technical Ratings
Figure 4.0 Technical Rates of Successful Teams Passing
Cumulative Frequency
Passing Rates of Unsuccessful Teams Total N=3 Matches
900
800
700
600
500
400
300
200
100
0
Spain
Russia
Croatia
Switzerland
3+
2+
1+
0
1-
Technical Rating
Figure 4.1 Technical Rates of Unsuccessful Teams Passing
41
2-
3-
Jeremy Williams ST02002650
Technical analysis of Euro 2004
4.5 Technical Analysis of Receiving
Receiving Rates of Successful Teams Total N=3 Matches
Cumulative Frequency
600
500
Portugal
400
Greece
300
France
200
England
100
0
3+
2+
1+
0
1-
2-
3-
Technical Ratings
Figure 4.2 Technical Rates of Successful Teams Receiving
Receiving Rates of Unsuccessful Teams Total N=3 Matches
Cumulative Frequency
700
600
500
Spain
400
Russia
300
Croatia
200
Switzerland
100
0
3+
2+
1+
0
1-
Technical Ratings
Figure 4.3 Technical Rates of Unsuccessful Teams Receiving
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Jeremy Williams ST02002650
Technical analysis of Euro 2004
4.6 Technical Analysis of Shots
Cumulative Frequency
Shot Rates of Successful Teams Total N=3 Matches
20
18
16
14
12
10
8
6
4
2
0
Portugal
Greece
France
England
3+
2+
1+
0
1-
2-
3-
Technical Ratings
Figure 4.4 Technical Rates of Successful Teams Shots
Shot Rates of Unsuccessful Teams Total N=3 Matches
Cumulative Frequency
14
12
10
Spain
8
Russia
6
Croatia
4
Switzerland
2
0
3+
2+
1+
0
1-
Technical Ratings
Figure 4.5 Technical Rates of Unsuccessful Teams Shots
43
2-
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Jeremy Williams ST02002650
Technical analysis of Euro 2004
4.7 Technical Analysis of Running with the ball
Cumulative Frequency
RB Rates of Successful Teams Total N=3 Matches
100
90
80
70
60
50
40
30
20
10
0
Portugal
Greece
France
England
3+
2+
1+
0
1-
2-
3-
Technical Ratings
Figure 4.6 Technical Rates of Successful Teams Running with a ball (RB)
Cumulative Frequency
RB Rates of Unsuccessful Teams Total N=3 Matches
90
80
70
60
50
40
30
20
10
0
Spain
Russia
Croatia
Switzerland
3+
2+
1+
0
1-
2-
3-
Technical Ratings
Figure 4.7 Technical Rates of Unsuccessful Teams Running with a ball (RB)
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Technical analysis of Euro 2004
4.8 Technical Analysis of Dribbling
Cumulative Frequency
Dribbling Rates of Successful Teams Total N=3 Matches
90
80
70
60
50
40
30
20
10
0
Portugal
Greece
France
England
3+
2+
1+
0
1-
2-
3-
Technical Ratings
Figure 4.8 Technical Rates of Successful Teams Dribbling
Dribbling Rates of Unsuccessful Teams Total N=3 Matches
Cumulative Frequency
120
100
Spain
80
Russia
60
Croatia
40
Switzerland
20
0
3+
2+
1+
0
1-
Technical Ratings
Figure 4.9 Technical Rates of Unsuccessful Teams Dribbling
45
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Jeremy Williams ST02002650
Technical analysis of Euro 2004
4.9 Technical Analysis of Heading
Heading Rates of Successful Teams Total N=3 Matches
Cumulative Frequency
140
120
100
Portugal
80
Greece
60
France
40
England
20
0
3+
2+
1+
0
1-
2-
3-
Technical Ratings
Figure 4.10 Technical Rates of Successful Teams Heading
Cumulative Frequency
Heading Rates of Unsuccessful Teams Total N=3 Matches
100
90
80
70
60
50
40
30
20
10
0
Spain
Russia
Croatia
Switzerland
3+
2+
1+
0
1-
Technical Ratings
Figure 4.11 Technical Rates of Unsuccessful Teams Heading
46
2-
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Jeremy Williams ST02002650
Technical analysis of Euro 2004
4.10 Technical Analysis of Crossing
Crossing Rates of Successful Teams Total N=3 Matches
Cumulative Frequency
35
30
25
Portugal
20
Greece
15
France
10
England
5
0
3+
2+
1+
0
1-
2-
3-
Technical Ratings
Figure 4.12 Technical Rates of Successful Teams Crossing
Crossing Rates of Unsuccessful Teams Total N=3 Matches
Cumulative Frequency
30
25
Spain
20
Russia
15
Croatia
10
Switzerland
5
0
3+
2+
1+
0
1-
Technical Ratings
Figure 4.13 Technical Rates of Unsuccessful Teams Crossing
47
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Technical analysis of Euro 2004
4.11 Technical Analysis of Tackling
Cumulative Frequency
Tackling Rates of Successful Teams Total N=3 Matches
100
90
80
70
60
50
40
30
20
10
0
Portugal
Greece
France
England
3+
2+
1+
0
1-
2-
3-
Technical Rates
Figure 4.14 Technical Rates of Successful Teams Tackling
Tackling Rates of Unsuccessful Teams Total N=3 Matches
Cumulative Frequency
80
70
60
Spain
50
Russia
40
Croatia
30
Switzerland
20
10
0
3+
2+
1+
0
1-
Technical Ratings
Figure 4.15 Technical Rates of Unsuccessful Teams Tackling
48
2-
3-
Jeremy Williams ST02002650
Technical analysis of Euro 2004
4.12 Technical Scores for Goalkeepers Results
Table 22. Technical Score summations for each team’s goalkeeper skill rating
Portugal Greece
Spain
Russia
France England Croatia Switzerland
Save
6
26
22
17
22
42
56
39
Catch
13
34
23
33
11
3
13
9
Punch
2
8
4
6
2
2
2
0
Throw
7
9
12
15
19
14
16
13
Kick
13
35
14
28
23
46
31
29
Pass
15
3
1
7
8
5
2
10
Total
Rating
Frequency
56
115
76
106
102
112
120
100
Mean
7
14
9.5
13
12
14
15
12
Table 22 displays team’s goalkeeper’s technical rating frequencies over a total of
three games during the group stages of the tournament.
49
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Technical analysis of Euro 2004
4.13 Technical Analysis of Saving
Saving Rates of Successful Goalkeepers Total N=3 Matches
Technical Ratings
3+
2+
1+
England
0
France
1-
Greece
2-
Portugal
30
2
4
6
8
10
12
14
Cumulative Frequency
Figure 4.16 Technical Ratings of Successful Goalkeepers Saves
Technical Rates of Unsuccessful Goalkeepers Total N=3 Matches
Technical Ratings
3+
2+
1+
Switzerland
0
Croatia
1-
Russia
2-
Spain
30
5
10
15
20
Cumulative Frequency
Figure 4.17 Technical Ratings of Unsuccessful Goalkeepers Saves
50
25
Jeremy Williams ST02002650
Technical analysis of Euro 2004
4.14 Technical Analysis of Catching
Catching Rates of Successful Goalkeepers Total N=3 Matches
Technical Rates
3+
2+
1+
England
0
France
1-
Greece
2-
Portugal
30
2
4
6
8
10
12
Cumulative Frequency
Figure 4.18 Technical Ratings of Successful Goalkeepers Catching
Catching Rates of Successful Goalkeepers Total N=3 Matches
Technical Ratings
3+
2+
1+
Switzerland
0
Croatia
1-
Russia
2-
Spain
30
5
10
Cumulative Frequency
Figure 4.19 Technical Ratings of Unsuccessful Goalkeepers Catching
51
15
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Technical analysis of Euro 2004
4.15 Technical Analysis of Punching
Punch Rates of Successful Goalkeepers Total N=3 Matches
Technical Rates
3+
2+
1+
England
0
France
1-
Greece
2-
Portugal
30
0.5
1
1.5
2
Cumulative Frequency
Figure 4.20 Technical Ratings of Successful Goalkeepers Punching
Punch Rates of Unsuccessful Goalkeepers Total N=3 Matches
Technical Ratings
3+
2+
1+
Switzerland
0
Croatia
1-
Russia
2-
Spain
30
0.5
1
1.5
Cumulative Frequency
Figure 4.21 Technical Ratings of Unsuccessful Goalkeepers Punching
52
2
Jeremy Williams ST02002650
Technical analysis of Euro 2004
4.16 Technical Analysis of Goalkeepers Throwing
Throwing Rates of Successful Goalkeepers Total N=3 Matches
Technical Ratings
3+
2+
1+
England
0
France
1-
Greece
2-
Portugal
30
2
4
6
8
10
12
14
Cumulative Frequency
Figure 4.22 Technical Ratings of Successful Goalkeepers Throwing
Throwing Rates of Unsuccessful Goalkeepers Total N=3 Matches
Technical Ratings
3+
2+
1+
Switzerland
0
Croatia
1-
Russia
2-
Spain
30
5
10
Cumulative Frequency
Figure 4.23 Technical Ratings of Unsuccessful Goalkeepers Throwing
53
15
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Technical analysis of Euro 2004
4.17 Technical Analysis of Goalkeepers Kicking
Kicking Rates of Successful Goalkeepers Total N=3 Matches
Technical Ratings
3+
2+
1+
England
0
France
1-
Greece
2-
Portugal
30
10
20
30
40
50
Cumulative Frequency
Figure 4.24 Technical Ratings of Successful Goalkeepers Kicking
Kicking Rates of Unsuccessful Goalkeepers Total N=3 Matches
Technical Ratings
3+
2+
1+
Switzerland
0
Croatia
1-
Russia
2-
Spain
30
5
10
15
20
25
30
Cumulative Frequency
Figure 4.25 Technical Ratings of Unsuccessful Goalkeepers Kicking
54
35
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Technical analysis of Euro 2004
4.18 Technical Analysis of Goalkeepers Passing
Passing Rates of Successful Goalkeepers Total N=3 Matches
Technical Ratings
3+
2+
1+
England
0
France
1-
Greece
2-
Portugal
30
2
4
6
8
10
12
Cumulative Frequency
Figure 4.26 Technical Ratings of Successful Goalkeepers Passing
Passing Rates of Unsuccessful Goalkeepers Total N=3 Matches
Technical Ratings
3+
2+
1+
Switzerland
0
Croatia
1-
Russia
2-
Spain
30
2
4
6
8
10
12
Cumulative Frequency
Figure 4.27 Technical Ratings of Unsuccessful Goalkeepers Passing
55
14
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Technical analysis of Euro 2004
CHAPTER VI
CONCLUSION
Jeremy Williams ST02002650
Technical analysis of Euro 2004
6.0 Conclusion
The study investigated any technical differences between successful and unsuccessful
outfield player’s and teams goalkeepers competing in the European tournament in
Portugal 2004, which will hopefully influence how coaches consider the importance
of technical work when developing young performers and far more experienced
athletes in their sport.
It was found that their were differences in technical abilities between successful and
unsuccessful teams in the European Championships in Portugal 2004 using a hand
notation designed to identify teams technical ratings.
The results indicated for outfield teams:
•
Portugal (successful team) and Croatia (unsuccessful team) there was a
significant difference (P<0.05) of technical ability.
•
Greece (successful team) and Switzerland (unsuccessful team) there was a
significant difference (P<0.05) of technical ability.
The results indicated for team’s goalkeepers:
•
Portugal (successful team) and Croatia (unsuccessful team) there was a
significant difference (P<0.05) of technical ability.
•
Greece (successful team) and Switzerland (unsuccessful team) there was a
significant difference (P<0.05) of technical ability.
72
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From a coaching concept the data produced from the results section can hopefully
show that technical ability is an important contributor to individual’s performances in
soccer and the coaching process in this country may have to be re-evaluated to teach
new emphasis on technique to new and present performers.
6.1 Recommendations
The main aim of the study was to analysis all 16 teams in the competition, but due to
other circumstances and time restraints the analysis of these teams could not be
gathered in groups C and D. For further research it would be suggested to analysis all
the teams in the competition to provide a true accurate analysis of technical abilities
of teams in the competition. The rating scale from 3+ to 3- maybe increased due to
most of the performer’s technical abilities when under analysis was greater than 3+ at
this elite level so the ratings could be increased to 4+ to 4- giving an even more
accurate analysis of the data gathering.
The Soccer World Cup 2006 would be a good analysis for future research in soccer
tournaments. The best teams from around the world will be competing in the
competition such as the likes of South American, African and Asian teams, which can
provide a more accurate account of technical abilities due to the World Cup being the
highest
standard
of
performers
in
73
at
the
elite
level
in
soccer.
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Jeremy Williams ST02002650
Technical analysis of Euro 2004
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www.worldsoccer.about.com 28th January 2005
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www.soccerphile.com 2005 3rd January 2005
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Appendices 1.0.
Table 23. Frequencies of total technical actions for outfield N=3 Matches
Portugal Greece Spain Russia France
England
Croatia Switzerland
Pass
1065
515
1066
813
1132
917
849
844
Receive
914
447
899
662
140
158
163
151
Shot
48
23
27
35
45
29
33
25
RB
76
51
45
47
143
105
134
113
Dribble
213
94
205
143
114
60
100
58
Header
218
181
169
141
158
176
149
120
Cross
72
43
68
48
123
58
78
65
Tackle
93
90
83
119
94
152
140
143
Table 23 displays team’s technical action frequencies over a total of three games
during the group stages of the tournament.
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Appendices 2.0
Table 24. Frequencies of total technical actions for goalkeepers N=3 Matches
Portugal Greece Spain Russia France
England
Croatia Switzerland
Save
8
19
16
13
15
30
32
34
Catch
12
22
16
25
11
5
5
8
Punch
4
4
2
3
1
2
0
1
Throw
6
6
12
14
19
20
19
22
Kick
20
32
18
36
47
67
56
43
Pass
13
3
4
7
17
15
20
15
Table 24 displays team’s goalkeeper’s technical action frequencies over a total of
three games during the group stages of the tournament.
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Appendices 3.0
Table 25. Chi-Square displaying the significant difference between outfield teams of
a successful team Portugal and unsuccessful team Croatia in the competition
Technique
Pass
Receive
Shot
Running with ball
Dribbling
Header
Crossing
Tackling
Portugal Croatia
1035
449
792
81
33
14
86
120
251
136
152
145
83
41
14
160
Description of variable
SUM
Chi square
DF
P
T1
2446
T2
1146
Abs
Diff
586
711
19
34
115
7
42
146
Abs
Diff
1660
Mean
742
436.5
23.5
103
193.5
148.5
62
87
%Error
78.97574
162.8866
80.85106
33.00971
59.43152
4.713805
67.74194
167.8161
Mean
1796
%Error
603.3631
7
4.6E-126
A P value of 4.6E indicates a significant difference between the technical frequency
ratings at the 5% level of significance (P>0.05)
88
Jeremy Williams ST02002650
Technical analysis of Euro 2004
Appendices 4.0
Table 26. Chi-Square displaying the significant difference between successful team
Greece and unsuccessful team Switzerland in the competition
Technique
Pass
Receive
Shot
Running with ball
Dribbling
Header
Crossing
Tackling
Greece
418
356
14
52
71
140
35
94
Switzerland
336
66
12
94
53
96
37
138
Description of variable
SUM
T1
1180
T2
832
Chi square
DF
P
Abs
Diff
82
275
0
68
65
5
2
44
Abs
Diff
541
Mean
377
218.5
14
86
103.5
142.5
36
116
%Error
21.75066
125.8581
0
79.06977
62.80193
3.508772
5.555556
37.93103
Mean
1093.5
%Error
225.4811
7
4.53E-45
A P value of 4.53E indicates a significant difference between the technical frequency
ratings at the 5% level of significance (P<0.05).
89
Jeremy Williams ST02002650
Technical analysis of Euro 2004
Appendices 5.0
Table 27. Chi-Square displaying the significant difference between Goalkeepers of a
successful team Portugal and unsuccessful Croatia team in the competition
Technique
Save
Catch
Punch
Throw
Kick
Pass
Portugal
6
13
2
7
13
15
Croatia
56
13
2
16
31
2
Description of variable
SUM
T1
56
T2
120
Chi square
DF
P
Abs
Diff
50
0
0
9
18
13
Abs
Diff
90
Mean
31
13
2
11.5
22
8.5
%Error
161.2903
0
0
78.26087
81.81818
152.9412
Mean
88
%Error
43.64805
5
2.73E-08
A P value of 2.73E indicates a significant difference between technical frequency
ratings at the 5% level of significance (P<0.05)
90
Jeremy Williams ST02002650
Technical analysis of Euro 2004
Appendices 6.0
Table 28. Chi-Square displaying the significant difference between Goalkeepers of a
successful team Greece and unsuccessful team Switzerland
Technique
Save
Catch
Punch
Throw
Kick
Pass
Greece
26
34
8
9
35
3
Switzerland
39
9
0
13
29
10
Description of variable
SUM
T1
115
T2
100
Chi square
DF
P
Abs
Diff
13
25
8
4
6
7
Abs
Diff
63
Mean
32.5
21.5
4
11
32
6.5
%Error
40
116.2791
200
36.36364
18.75
107.6923
Mean
107.5
%Error
29.28994
5
2.03E-05
A P value of 2.03E indicates a significant difference between technical frequency
ratings at the 5% level of significance (P<0.05)
91