CARDIFF SCHOOL OF SPORT
DEGREE OF BACHELOR OF SCIENCE
(HONOURS)
SPORT AND EXERCISE SCIENCE
DIFFERENCES IN ATTACKING PLAY BETWEEN
SUCCESSFUL AND UNSUCCESSFUL TEAMS IN THE
UEFA CHAMPIONS LEAGUE 2010-11 COMPETITION
CHRISTOPHER FRY
ST09001842
Cardiff Metropolitan University
Prifysgol Fetropolitan Caerdydd
Certificate of student
I certify that the whole of this work is the result of my individual effort, that all
quotations from books and journals have been acknowledged, and that the
word count given below is a true and accurate record of the words contained
(omitting contents pages, acknowledgements, indexes, figures, reference list
and appendices).
Word count:
10900
Signed:
Date:
Certificate of Dissertation Tutor responsible
I am satisfied that this work is the result of the student’s own effort.
I have received a dissertation verification file from this student
Signed:
Date:
Notes:
The University owns the right to reprint all or part of this document.
Table of contents
Page
LIST OF TABLES
LIST OF FIGURES
ACKNOWLEDGEMENTS
ABSTRACT
CHAPTER ONE: INTRODUCTION
1.1 History of Performance Analysis………………………………………………1
1.2 History of the UEFA Champions League………………………………….....2
1.3 Rationale…………………………………………………………………………2
1.4 Hypotheses………………………..……………………….……………………3
1.5 Limitations……………………….……………………….……………………...4
CHAPTER TWO: LITERATURE REVIEW
2.1 Overview……………………….……………………….………………………..5
2.2 Principles of Play……………………….……………………….………………5
2.3 Direct v Possession Football……………………….………………………….6
2.4 Possession for Successful and Unsuccessful Teams………………………8
2.5 Frequency and Location of Shots and Goals…………………………...…...9
CHAPTER THREE: METHODS
3.1 Research Design……………………….……………………….……………12
3.2 System Development……………………….………………………..………12
3.3 Reliability Evaluation……………………….……………………….………..16
3.4 Sample……………………….……………………….………………………..19
3.5 Data Gathering……………………….……………………….……………….21
3.6 Data Analysis……………………….……………………….…………………22
CHAPTER FOUR: RESULTS
4.1 Overview……………………….……………………….…………………..….24
4.2 Shooting variables……………………….……………………….……………24
4.3 Possession Variables……………………….……………………….……..…33
CHAPTER FIVE: DISCUSSION
5.1 Overview……………………….……………………….………………………37
5.2 Differences between successful and unsuccessful teams………………..37
5.3 Similarities between successful and unsuccessful teams………………...43
CHAPTER SIX: CONCLUSION
6.1 Main Findings……………………….……………………….…………………46
6.2 Applied Implications……………………….……………………….………….47
REFERENCES
APPENDICES
Appendix A. Blank copy of final Notation System.
Appendix B. Scanned, used copy of notation system.
List of Tables
Page
Table 1. Operational definitions for the pass that preceded a shot or goal….13
Table 2. Operational definitions for shot outcomes. ………………………...…14
Table 3. Percentage error for shot and goal location. ………………………...16
Table 4. Percentage error for assist type.……………………….………………17
Table 5. Percentage error for Auxerre possession won.………………………17
Table 6. Percentage error for Ajax possession won. ……………………….…18
Table 7. Percentage error for attacking 3rd entries. ……………………….…..18
Table 8. Percentage error for penalty area entries. ……………………….…..18
Table 9. Overall percentage error for all variables.…………………………….18
Table 10. Successful and unsuccessful teams……………………….….……..20
Table 11. Match sample for successful teams…………………….……………20
Table 12. Match sample for unsuccessful teams.…………………….………..21
Table 13. Assists used by successful and unsuccessful teams.…….……….28
List of Figures
Page
Figure 1. Division of pitch to notate location of shots and goals. ……………12
Figure 2. Division of pitch to notate possession won. …………………….…..14
Figure 3. Mean frequencies for shot outcomes.…………………….………….24
Figure 4. Number of shots per match by goals to shots ratio for successful
and unsuccessful teams. …………………….…………………………………...25
Figure 5. Heat map to show the percentage of shots and goals with respect to
location for successful and unsuccessful teams…………………….…………26
Figure 6. Heat map to show the percentage of shots and goals taken by
successful and unsuccessful teams inside and outside the penalty area…..27
Figure 7. Percentage and frequency of each assist type…..…………………29
Figure 8. Frequency and percentage of goals with respect to length of
possession for successful and unsuccessful teams ………………….………29
Figure 9. Frequency and percentage of shots on target with respect to length
of possession for successful and unsuccessful teams…………………….….30
Figure 10. Frequency and percentage of shots off target with respect to length
of possession for successful and unsuccessful teams…………………….….30
Figure 11. Frequency and percentage of all goals and shots with respect to
length of possession for successful and unsuccessful teams. ………………31
Figure 12. % and frequency of shots and goals with respect to possession
length for successful and unsuccessful teams…………………….…………...32
Figure 13. Average number of won possessions per match with respect to
location and outcome for successful and unsuccessful teams. ……………33
Figure 14. Average number of won possessions per match with respect to
match location and outcome for the opposition of successful and unsuccessful
teams.
…………………….………………………………….……………………………34
Figure 15. Final 3rd and penalty area entries for successful and unsuccessful
teams.
…………………….………………………………….…………………………….35
Figure 16. Total final 3rd and penalty area entries for successful and
unsuccessful
teams…………………….………………………………….………………………36
ACKNOWLEDGEMENTS
I would like to thank the following people;
- Dr. Peter O’Donoghue.
- The 22 Kimberley Road boys.
- My parents.
i
ABSTRACT
Although football has been the focus of considerable performance analysis
research, little attention has been paid to the UEFA Champions League. On
the basis of this, and the fact that the differences between successful and
unsuccessful teams ultimately account for winning or losing (Frencken and
Lemmink, 2009), this study aimed to identify the differences in attacking play
between successful and unsuccessful teams in the UEFA Champions League
2010-11 competition.
Successful teams were defined as the 4 teams who reached the semi-finals.
Unsuccessful teams were defined as the 6 teams that finished on the fewest
points after the group stage of the tournament.
A hand notation system was designed to record the following variables; the
frequency and location of shots and goals, the number of passes preceding
shots and goals, the type of assist, frequency and location of possession won,
and the frequency of final 3rd and penalty area entries.
It was found that successful teams produced significantly higher values for the
following variables; shots and goals (p < 0.05), shots and goals from inside
the penalty area (p < 0.01), won possessions that ended in no shot (p < 0.05)
and shot in the midfield 3rd (p < 0.05), won possession ending in no shot in
the attacking 3rd (p < 0.05), entries into the final 3rd (p < 0.01) and penalty
area (p < 0.01).
In applied situations the results of this study could be used to develop
strategies that will enhance the frequency with which a team can gain entry
into their opponents defensive 3rd, and when in there creating shooting
opportunities - preferably from within the penalty area. Future studies should
take into account extra situational factors relevant to attacking play, or
examine the differences in defensive variables between successful and
unsuccessful
teams
in
the
ii
UEFA
Champions
League.
CHAPTER 1: INTRODUCTION
CHAPTER 1: INTRODUCTION
1.1 History of Performance Analysis
Performance analysis aims to analyse and improve performance and has 5
predominant areas in which it can be applied; tactical evaluation, technical
evaluation, analysis of movement, database development and modeling and
finally in educating coaches and players (Hughes and Franks, 2008). The
application of performance analysis and forms of notation to sporting events
can be traced back to Fullerton’s (1912) attempts to assess the various facets
of baseball, although Lyons (1997) highlights the work of Lloyd Messersmith,
noting that Messersmith was the first doctoral student to submit a notational
analysis dissertation and later performed innovative research.
Although these early efforts can be considered to be the foundations of what
we now know as performance analysis, it was not until the advent of
computers in the 1960’s that performance analysis as a discipline really
began to blossom (Croucher, 1997). Despite this, neither Reep and
Benjamin’s (1968) or Reilly and Thomas’s (1976) studies, generally regarded
as the pioneering research studies of football, used computerised systems.
Computerised systems are now widely used, with 8 of the 10 premiership
football clubs that replied to Blaze et al.’s (2004) questionnaire confirming
they used computerised systems, and all 10 declaring they used some form of
obtaining information regarding performance.
It is now accepted that performance analysis is an essential part of the postgame feedback to players and coaching staff (Reilly and Gilbourne, 2003) but,
due to the need to gain an advantage over their opposition, the full extent and
results of analysis that football clubs conduct remains secret to all but
authorized personnel within the club (James, 2006).
Consequently, the
majority of performance analysis information stems from academic sources,
the studies of which generally fall into either match analysis or time-motion
analysis
studies.
1
1.2 History of the UEFA Champions League
Football is undoubtedly the world’s most popular sport, played in every nation
without exception (Reilly, 1996). In Europe, the UEFA Champions League is
the premier club competition, and has existed in its current format for just 19
years. Each year, 32 teams compete to progress through qualifying, group,
and knockout stages to reach the final. The tournaments current structure is a
far cry from its humble beginnings when, in 1952, half the number of teams
who compete in todays tournament took part. The tournament was originally
named The European Cup, with the acronym UEFA not appearing in the
tournaments title as the decision to create the competition was not a UEFA
initiative (UEFA, 2011). In fact, the tournament was proposed by French
magazine L’Equipe to settle disputes over who could lay claim to be Europe’s
superior team after Wolverhampton Wanderers were lauded by British
newspapers following successes over Russian and Hungarian teams (Cox et
al., 2002).
1.3 Rationale
The decision to research differences between successful and unsuccessful
teams has been criticized (Jones et al., 2004; Tucker et al., 2005). The key
criticisms expressed by Jones et al. (2004) are failing to report reliability and
the use of imbalanced samples. Consequently, this study has reported
reliability, and balanced the sample in terms of matches played at home and
away, and strength of opposition. This research is important to undertake
though as the differences ultimately account for winning or losing (Frencken
and Lemmink, 2009). The results of this and similar studies, has implications
in terms of the planning of training and implementation of match strategies
and tactics.
The choice to focus solely upon differences in attacking play was to limit the
scope of the research. The decision to examine the UEFA Champions
Leagueis that despite match analysis studies being the most frequent topic of
the World Congresses of Science and Football (Reilly and Gilbourne, 2003),
2
the UEFA Champions League has received little research in comparison to
domesic leagues and the FIFA World Cup. As it has been shown that
individual and team tactics differ depending upon whether playing in
European or domestic competiton (James et al., 2002), but not whether
playing in European club compeitions or the FIFA World Cup (Rowlinson and
O’Donoghue, 2007), more research into the UEFA Champions League is both
required and warranted.
The necessity for notational analysis to help discover any differences is
highlighted by prior research which reveals that coaches can recollect only
30-60% of the key factors that determine successful performance in football
(Franks and Miller, 1986; Laird and Waters, 2008). Without the use of
notational analysis the differences that distinguish between successful and
unsuccessful teams is therefore based solely upon subjective opinions.
1.4 Hypotheses
The following hypotheses will be tested:
1: Successful teams will shoot and score significantly more than unsuccessful
teams.
2: Successful teams will win possession of the ball significantly more than
unsuccessful teams across all areas of the pitch.
3: Successful teams will gain significantly more entries into their opponents
defensive 3rd than unsuccessful teams.
4: Successful teams will gain significantly more entries into their opponents
penalty area than unsuccessful teams.
5. Unsuccessful teams opponents will win possession significantly more times
than the opponents of successful teams across all areas of the pitch.
3
1.5 Limitations
All match videos used in this study were obtained from the UEFA official
website, with the videos themselves having been uploaded onto this site from
terrestrial broadcasts from across Europe. The aim of the broadcast is to
make the match enjoyable for the viewer, not to facilitate notation, and
therefore camera angles and replays occasionally hindered match analysis.
The choice to disregard differences in defensive play was to refine the scope
of analysis to fit time and word constraints, however in doing so, it may hinder
or distort this studies results for applied application as attacking play alone
cannot account for all differences between successful and unsuccessful
teams.
Finally, the samples were not balanced in the number of teams and therefore
the number of matches notated. The successful sample constituted four
teams, and sixteen matches, and the unsuccessful sample was composed of
6 teams, and twenty-four matches. Similarly, other researchers may not agree
on this studies classifications of successful and unsuccessful teams.
4
CHAPTER 2: LITERATURE REVIEW
CHAPTER 2: LITERATURE REVIEW
2.1 Overview
This study is concerned with discovering the differences between successful
and unsuccessful football teams in last seasons UEFA Champions League
tournament. Although a winning formula for football is unlikely to ever be
revealed (Olsen and Larsen, 1997), analysis of the differences between
successful and unsuccessful teams is important to undertake as the
differences ultimately account for winning or losing (Frencken and Lemmink,
2009).
To refine the scope of this study only the differences in how teams attack is to
be analysed, and this distinction is reflected in the following literature review.
2.2 Principles of Play
A football teams efficiency is largely dependent upon its tactical performance
(Garganta et al., 1997) and its ability to manipulate the underlying principles of
play, which essentially serve as conceptual guidelines to be adhered to in
order to facilitate success. These principles are themselves dependent upon
the 3 principle phases of football; attack, defense, and preparation or midfield
play (Wade, 1996).
With regards to attack, Wade (1996) regards width, improvisation, mobility,
penetration, support and depth as the core principles but highlights
possession as the first and most important principle as it determines
everything. He further expands that although possession is of the utmost
importance, it can be sacrificed if there is the opportunity to shoot, or create, a
shot at goal.
5
2.3 Direct v Possession Football
It is not uncommon for a match to exhibit up to 800 passes in a match, making
it the most common action in a football match (Bangsbro and Peitersen, 2004)
and consequently, there is much deliberation as to what is the best method for
maintaining and utilizing possession. This is particularly true for whether the
long ball direct style of play or the possession retention method are more
conducive to goal scoring and therefore winning, an academic debate that
stems from the conclusions of Reep and Benjamin’s (1968) study.
As aforementioned, Reep and Benjamin (1968) ignited the debate after their
analysis of 3213 matches over a 15 year period concluded that 80% of goals
were scored after possessions of 3 passes or fewer. Similarly, later work by
Bate (1988) supported Reep and Benjamin’s (1968) assertions, stating that
94% of international goals are scored after possessions of 4 passes or fewer.
These studies essentially founded the basis of the argument for the long ball
or direct method of play, a strategy that has been shown to be particularly
pertinent in British football (Pollard et al., 1988; Yamanaka et al., 1993).
The direct method, dismissingly termed as kick and rush, allowed teams such
as Watford and Wimbledon to over achieve (Wilson, 2008). The direct method
has also permeated international football, the national teams of Eire and
Norway being two notable examples (Hughes and Franks, 2005). In fact
Olsen and Larsen (1997), Larsen writing whilst consultant in match analysis to
Norwegian football, openly admitted that the work of Reep and Benjamin
(1968) and Bate (1988) shaped the penetrative way of play Norway utilised.
Although it allowed Norway to achieve success, their success is relative to
their own past performances, and at present they have still failed to progress
past the second round of a major international tournament (FIFA, 2011).
Analysis of major football tournaments has found some support for the direct
method, and stood the test of time. Olsen’s (1988) analysis of the 1986 World
Cup stated that most goals are scored following possessions of 2 or fewer
passes, and that just 20% of goals occur following possessions of 5 or more
6
passes. Comparably, at Euro 2004 the long pass was the principal action prior
to goals scored (34%) with combination play at 29% the next highest action
(Yiannakos and Armatas, 2006).
Despite the groundwork by Reep and Benjamin (1968) and Bate (1988), and
their recommendation that to increase scoring opportunities teams need to
play long balls into forward areas more frequently, actually adopting this
strategy may be naïve. Although both studies found more goals were scored
from passing sequences of fewer passes this is in part due to the fact there
are many more 3 pass phases of play than 8 pass phases of play (Hughes
and Franks, 2005). Hughes and Franks (2005) conducted analysis similar to
that of Reep and Benjamin (1968), achieving similar results but when the data
was normalized, it was shown that there were significantly more shots per
possession at longer passing sequences than there were shorter passing
sequences.
Analysis of more recent football than that conducted by Reep and Benjamin
(1968) and Bate (1988) seems to support the implementation of possession
football. Tenga et al. (2010a) revealed that over the course of one season of
Norwegian professional football possessions of five passes or more were
more effective for goal scoring than possessions of just two passes or less.
This is reflected at international level, with the percentage of goals scored
from passing sequences of more than five increasing from 26% for the 1998
World Cup to 34% for the 2002 World Cup (Carling et al., 2005) and that for
the 2006 World Cup just 54% of goals were scored after a sequence of 4 or
less passes (Acar et al., 2009). A similar statistic can be seen in European
International level football, with 44% of all goals at Euro 2004 came following
an organized offensive move (Yiannakos and Armatas, 2006).
In summary, whilst Bate (1988) acknowledges possession football as ‘skillful’
and ‘artistic’ but not necessarily as a precursor to success, recent research
seems to surmise the opposite may be true. Regardless, none of the studies
references were conducted on the UEFA Champions League, and thus
analysis
of
passing
sequences
7
prior
to
goals
is
necessary.
2.4 Possession for successful and unsuccessful teams
Possession of the ball is the most popular performance indicator in football
analysis research (Lago and Martín, 2007) and numerous studies have shown
the association between possession and success. Jones et al. (2004)
analysed possession with respect to evolving match status, finding that
successful and unsuccessful teams had longer possession durations when
they were losing compared to when they were winning, and that that
regardless of the evolving score line successful teams had significantly longer
possessions than unsuccessful teams. They defined possession in terms of
time, into durations of 3-10s, 10-20s and <20s and although this is important
to know, it fails to reveal the number of passes that occurred within each time
frame or the pitch locations of possessions. This is important as prior research
has shown that the number of times a team loses possession short of the final
third is a significant factor in determining success (Lanham, 2005).
Redwood-Brown (2008) expanded upon the analysis of possession by
investigating possession patterns before and after scoring. She established
that in the 5 minutes prior to scoring a goal the scoring team played a
significantly higher percentage of accurate passes than the average for the
half, and the conceding team play significantly fewer passes. However, the
study failed to address the differences between successful and unsuccessful
teams, something that Lago (2009) did. Similarly to the results of Jones et al.
(2004), Lago found that playing against stronger opposition results in a
decrease in time spent in possession.
Analysis of international football has found comparable results to those
conducted on club football. For the 1986 World Cup, successful teams played
significantly more touches of the ball per possession than unsuccessful teams
(Hughes et al., 1988) and at the 1994 World Cup the champions Brazil had a
greater time in possession than their opponents (Luhtanen et al., 1997).
Conversely, it has also been reported that there are no differences in passing
ability successful and unsuccessful teams (Scoulding et al., 2004).
8
Nonetheless, despite the seemingly clear connection between possession
and success across domestic, European club, European international and
international football, it would be callous to infer from this that the same
results will be found for the UEFA Champions League. Moreover, FC
Internazionale Milano won the 2009-10 UEFA Champions League tournament
despite averaging just 45% possession throughout the tournament, and just
32% for the final (UEFA, 2010).
It is unclear whether FC Internazionale Milano’s possession strategy is an
anomaly, or on the contrary an effective strategy for success. Whilst it has
been shown that in comparison to domestic matches, European matches
have a greater frequency of play occurring in the pre-defensive areas of the
pitch (James et al., 2002) it is equally feasible that it merely reflects Italian
football, where league rankings are more highly correlated with measures of
efficiency in defence than attack (Dobson and Goddard, 2011). This is further
exemplified in that the symbolic Italian system of play, catenaccio, translates
as ‘door-bolt’ (Goldblatt, 2007).
Consequently, further analysis of possession in the UEFA Champions
League, especially with reference to the differences between successful and
unsuccessful teams, is necessitated.
2.5 Frequency and location of shots and goals
Shooting at goal is regarded as the most important feature of football (Jinshan
et al., 1993) and also provides objective data about the culmination of attacks
(Garganta et al., 1997). With regards to shot frequency, Reep and Benjamin
(1968) first noted that for every 10 shots taken 1 goal will be scored. Although
noteworthy, the study analysed a range of samples, which varied in size from
8 matches for the 1954 World Cup to 51 miscellaneous matches from 195354. Additionally, O’Donoghue (2010) cautions that the results of performance
analysis research should not be taken as representative of a sport beyond the
era of that data used and so it is more appropriate to examine the results of
newer research. Consequently, the results of more contemporary research,
9
particularly that by Hughes and Franks (2005), is striking in that a similar
shots to goal ratio was found despite being conducted approximately forty
years later on the 1990 and 1994 World Cups.
Hughes and Franks (2005), unlike Reep and Benjamin (1968), accounted for
the differences between successful and unsuccessful teams and showed that
successful teams shoot more frequently than unsuccessful teams, an
assertion that has been consistently supported by additional studies. Analysis
of Greek football has demonstrated that the top teams have more shots, and
require fewer shots to score a goal, than the bottom teams (Armatas et al.,
2009). The same can be said of winning teams in Spanish football (LagoPeñas et al., 2010) although dividing a sample into winning, drawing or losing
teams might not be the best means by which to divide a sample in that it is
possible to play well but still lose a one-off match. Consequently the research
of Lago-Ballesteros and Lago-Peñas (2010) is notable in that, although they
too analysed Spanish football and found that successful teams yielded higher
total shots, and shots on target, than the middle or bottom teams, they divided
their sample into the top 4, middle 12 and bottom 4 teams based on final
league standings.
Research on the UEFA Champions league has supported the statement that
successful teams shoot more frequently (Szwarc, 2007) but is limited by
choice and size of sample. Despite finding a significant difference, only the
finals were analysed, which is contentious as although one team will be
unsuccessful in the final, to reach the final they had to adopt a strategy that
was successful. Accordingly, this study aims to account for some of the
shortcomings of prior research through analysis of group games and by taking
drawing and losing matches into account for both the successful and
unsuccessful groups.
Analysis of shot location has consistently shown the penalty area to be the
most frequent location for goals scored, with percentages ranging from 45%
(Yiannakos and Armatas, 2006), 80% (Dufour, 1993), 82% (Sotiropoulous et
al., 2005; cited in Yiannakos and Armatas, 2006) and even 90% (Olsen,
10
1998). Analysis of the UEFA Champions League reveals a slightly lower
percentage, 64.4%, but it is still the most common area for goals to be scored
from (Michailidis et al., 2004).
With respect to the differences between successful and unsuccessful teams, it
has been shown that successful teams have more shots on target within the
penalty area and score a greater number of goals outside and inside the
penalty area (Kapidžić et al., 2010). However, much like Szwarc (2007) and
Lago-Peñas et al. (2010), the teams in Kapidžić et al.’s (2010) study were
classified as successful or unsuccessful depending upon whether the team
won or lost the match which is questionable as performance and score are not
necessarily the same (Lago, 2007). Consequently, teams who achieved a
high final league standing would be classed as unsuccessful if they lost the
game that was analysed, despite utilising a strategy that ultimately brought
success.
On the basis of the limitations of prior research, and the applied implications
inherent in match analysis research, this study seeks to discover the
differences in attacking play between successful and unsuccessful teams.
11
CHAPTER 3: METHODS
CHAPTER 3: METHODS
3.1 Research Design
The form of descriptive research this study would be classified as is
observational research, as behaviours, such as the on-pitch actions of players
and teams, are observed and coded in their natural environment, during a
match, after which these frequencies were analysed (Thomas et al., 2011). To
record the frequencies of events a hand notation system was developed, with
events coded post-match so that the analyst could have greater control over
the video playback.
3.2 System Development
Variables
A hand notation system was designed to record the following variables; the
frequency, type and location of shots and goals, the number of passes
preceding shots and goals, the type of assist, frequency and location of
possession won and lost, the frequency of final 3rd entries and penalty area
entries.
Operational Definitions
In order to record the variables, the following operational definitions were
used. Unless otherwise stated, they were defined by the author.
Figure 1. Division of pitch to notate location of shots and goals.
12
In figure 1, zones A-E are within the attacking 3rd and F-H are within the
midfield 3rd. Zone B is the goal area, zone C is the penalty area and zone E
lies occupies the space between the edge of the penalty area and the midfield
3rd.
This study has assigned the types of pass that occur prior to a shot or goal
into 1 of the 6 categories in table 1. A pass is defined as ‘when a player
attempts to play the ball with any part of the body to one of his team-mates to
allow that player possession of the ball’ (James et al., 2002). Shots that had
no preceding pass were notated as ‘Direct Shot’.
Table 1. Operational definitions for the pass that preceded a shot or goal.
Pass Type
Definition
A pass played through or over the
Forward Pass
defence. Typically include long balls or
through balls.
A pass that is made from the byline
Byline Pass
back towards the penalty area. Typically
occur after a player has got behind the
last line of defence.
A pass made that travels less than
Short Pass
approx.10m in any direction.
A pass made from a wide position
Cross
(typically zones A and D).
Includes free kicks, corners
Set Play
and throw-ins.
A pass of any description that does not
Other
fall into any of the categories.
13
Shots are any attempt with any part of the body to score a goal. The
classifications assigned to the outcomes of all shots are defined in table x.
Table 2. Operational definitions for shot outcomes.
Shot Outcome
Definition
When a shot completely crosses the
Goal
goal line and a goal is awarded.
A shot that if it was not blocked or
On Target
saved, would have resulted in a goal
being awarded.
A shot that strikes or passes either
Off Target
wide of the goal posts or high of the
goal crossbar.
For the team being notated, and their opposition, instances of when they won
possession were notated. Similarly to Pollard and Reep (1997) momentary
touches of the ball that do not significantly change the direction of the ball, do
not count as winning possession. The location (defensive, midfield or
attacking third see figure x) of won possession was also recorded.
A
B
C
Figure 2. Division of pitch to notate possession won.
14
When attacking from left to right, zone A is the defensive 3rd, B the midfield 3rd
and C the attacking (final) 3rd.
Penetrations into the attacking third were notated. For an entry to be notated
the ball had to be dribbled across the attacking third line under control, or a
successful pass had to be made to a teammate within the final 3rd. These
entires were notated as occurring at the left, middle or right side of the pitch.
The left was zone A, middle was zone E and the right was zone D (see figure
1).
Pilot
One half of a match from the 2010-11 UEFA Champions League, but not a
match that constitutes part of the sample, was notated. The system was
deemed to have collected sufficient data, although one change was made including ‘other’ as a classification of pass that precedes a shot or goal as it
was found that during the pilot an assist was made that did not fall into one of
the previous classifications.
The pilot study was conducted under timed conditions. This was done as the
analyst was unsure how long it would take to notate one half, as the system
cannot be used in real-time. Using the system, the analyst took 55m to notate
one match, which helped inform how many matches to use in this study.
Final System
The final notation system that was used for this study can be viewed in
appendix A. A used notation system is in appendix B. When viewing the used
notation system, for assist type whenever a shot or goal was taken the
number of passes that preceded the shot or goal was inserted into the
relevant box.
15
3.3 Reliability Evaluation
Reliability refers to the extent to which the event codes, notated by an analyst,
reflect what happened in the match (James et al., 2007). This study is using a
novel notation system, in which case making sure the data it produces is
reliable is the key factor (Cooper et al., 2007) and until this any data produced
is unusable. In comparison with other sport science disciplines, the issue of
reliability assessment is particularly pertinent in performance analysis as 70%
of notational analysis studies do not report reliability and those that do often
apply inappropriate or incomplete processes (Hughes, Cooper & Nevill, 2002),
owing possibly to the lack of a definitive research paper to make
recommendations in performance analysis, as Atkinson and Nevill (1998) did
for sports medicine.
Reliability was assessed by conducting an intra-operator test. The match used
was Auxerre v Ajax from the 2010-11 UEFA Champions League season, with
a gap of 1 week between analyses to reduce memory factors. Hughes et al.’s
(2004) % error for the frequency of events was used.
Table 3. Percentage error for shot and goal location.
16
Table 4. Percentage error for assist type.
Table 5. Percentage error for Auxerre possession won.
Table 6. Percentage error for Ajax possession won.
17
Table 7. Percentage error for attacking 3rd entries.
Table 8. Percentage error for penalty area entries.
Table 9. Overall percentage error for all variables.
Although the % error for individual variables reached values of 40%, the total
% error for any variable did not exceed 10%, and the combined % error for all
variables was 10.2%, and so deemed acceptable, and therefore the analyst
and the system reliable. These values are possibly due to the system being
lapsed-time, which have been shown to be more reliable than real-time
18
systems due to the control the analyst has over the match footage (Williams,
et al., 2007).
Despite reaching acceptable reliability levels, only an intra-operator reliability
test has been performed. Intra-operator tests are limited in their ability to
evaluate reliability as they show merely the ability of the analyst to use the
notation system (O’Donoghue, 2007). Consequently, it is possible to
consistently notate events inaccurately but still achieve high reliability levels.
Inter-operator tests however would reveal whether the system is objective
regardless of the perceptions of different analysts (O’Donoghue, 2010).
3.4 Sample
Hughes et al. (2001) suggest that the number of matches required to
constitute a sufficient sample is dependent upon the level of analysis.
Although variables such as turnovers occur many times over the course of a
match, others such as shots and goals do not, and so 40 matches was
deemed appropriate. All matches are from the 2010-11 UEFA Champions
League Competition.
Successful teams were defined as the 4 teams who reached the semi-finals.
The losing semi-finalists were Real Madrd CF and FC Schalke 04, the losing
finalist was Manchester United FC and FC Barcelona was the winning finalist.
Unsuccessful teams were defined as the 6 teams that finished on the fewest
points after the group stage of the tournament. All of the unsuccessful teams
used in this study had at least 2 points separating them from 3 rd place.
19
Table 10. Successful and unsuccessful teams.
Successful Teams
Unsuccessful Teams
FC Barcelona
Bursaspor
Manchester United FC
Panathinaikos FC
Real Madrid CF
CFR 1907 Cluj
FC Schalke 04
MŠK Žilina
AJ Auxerre
FK Partizan
In the UEFA Champions league there are 8 groups, but to refine the scope,
only the 6 lowest teams were chosen. Consequently SV Werder Bremen from
Group A and Hapoel Tel Aviv F.C. from Group B were excluded as, despite
finishing bottom of their respective groups, only 1 point separated them from
finish 3rd in their groups.
Table 11. Match sample for successful teams.
Manchester United FC
FC Barcelona
Valencia
D 1-1
Copenhagen
W 2-0
Valencia
W 0-1
Copenhagen
D 1-1
Rangers
D 0-0
Rubin Kazan
W 2-0
Rangers
W 0-1
Rubin Kazan
D 1-1
Real Madrid CF
FC Schalke 04
AC Milan
W 2-0
Lyon
W 3-0
AC Milan
D 2-2
Lyon
L 1-0
Ajax
W 2-0
Benfica
W 2-0
Ajax
W 0-4
Benfica
W 1-2
20
Table 12. Match sample for unsuccessful teams.
Bursaspor
Zilina
Valencia
L 0-4
Marseille
L 0-7
Valencia
L 6-1
Marseille
L 1-0
Rangers
D 1-1
S. Moscow
L 1-2
Rangers
L 1-0
S. Moscow
L 3-0
Panathinaikos
Auxerre
Copenhagen
L 0-2
AC Milan
L 0-2
Copenhagen
L 3-1
AC Milan
L 2-0
Rubin Kazan
D 0-0
Ajax
W 2-1
Rubin Kazan
D 0-0
Ajax
L 2-1
Cluj
FK Partizan
Roma
D 1-1
Arsenal
L 1-3
Roma
L 2-1
Arsenal
L 3-1
Basel
W 2-1
Braga
L 0-1
Basel
L 1-0
Braga
L 2-0
For both the successful and unsuccessful teams, all matches were balanced
in terms of location and strength of opposition; that is each team was
analysed whilst playing the teams who finished 2 nd and 3rd in their group at
both home and away. This was done as it is proposed that the outcomes and
processes of a performance are influenced by the quality and type of
opposition (O’Donoghue, 2009).
3.5 Data Gathering
To film and analyse the matches, the following equipment was used;
Hardware

SONY DCR-HC51E Standard Definition DV Tape Camcorder

Libec TH-650 DV Tripod

LINDY FireWire 800 Cable

Apple MacBook Pro 15.4 inch
21
Software

iMovie 2011

Microsoft Word for Mac 2011

Microsoft Excel for Mac 2011

Google Chrome

SPSS
Matches were purchased and streamed online from the official UEFA Video
Replay Centre website, at the analysts home using the analysts Apple
MacBook Pro. Whilst the matches were being streamed, they were
simultaneously being imported into iMovie so that a permanent copy of the
match could be made. This was done as the notation system does not permit
all variables to be recorded in real-time.
They were imported into iMovie by filming the Apple MacBook Pros’ screens
whilst the match was being streamed. It was filmed using the SONY
Camcorder, which was affixed to the Libec tripod, and connected to the Apple
MacBook Pro using the Lindy FireWire 800 cable.
Once a match had been analysed, it was processed into an Excel
spreadsheet. This was done so that graphs and tables could be created,
measures of central tendency calculated, and so that relevant data could be
copied and pasted from the spreadsheet into SPSS for statistical analysis.
3.6 Data Analysis
The frequency of discrete events, as in this study, do not typically follow
normal distributions (Cooper et al., 2007) and so fail to meet the assumptions
for the use of parametric tests. Although expected, it was still necessary to
confirm non-normality. To do this, the Shapiro-Wilkes test was used, as there
are fewer than 50 values in the overall sample (40 matches) (O’Donoghue,
2010). From the test, a p value of under 0.05 level resulted in the rejection of
the null hypothesis that the data follows a normal distribution. When this
22
happened, the Mann-Whitney U Test was used to test for differences between
samples. From the Mann Whitney test, a p value of under 0.05 indicated a
statistically significant difference. In reporting the results of the Mann Whitney
U Test in the results section the p value is given with the medians of the
particular variable, as it is a better measure of central tendency when working
with skewed distributions (Vincent, 2005).
When the Shapiro-Wilkes test returned a p value of above 0.05, and so the
data was normally distributed, then the independent samples t-test was
performed.
From this, a p value of under 0.05 indicated a statistically
significant difference. In reporting the results of the independent samples ttest in the results section the p value is given with the means of the particular
variable, as it is a preferable measure of central tendency with normal
distributions (Vincent, 2005).
IBM SPSS statistics version 20 (IBM, New York, NY) was used to conduct the
statistical analyses.
23
CHAPTER 4: RESULTS
CHAPTER 4: RESULTS
4.1 Overview
The following tables and graphs present comparisons between successful
and unsuccessful teams. Firstly, analyses of variables pertaining to shooting
are examined, followed secondly by variables related to possession. As
samples were unbalanced in terms of the number of matches played,
percentages, means and medians are used to present the data.
4.2 Shooting Variables
18.0
16.0
Frequency
14.0
12.0
10.0
8.0
Successful
6.0
Unsuccessful
4.0
2.0
0.0
Goal
On Target
Off Target
Shot Outcome
Total
Figure 3. Mean frequencies for shot outcomes.
Expectedly, successful teams had higher mean frequencies for all shot
outcomes than unsuccessful teams (figure 1). The mean frequency of
successful teams shots and goals per game of 15.2±6.2 was significantly
higher than the 10.4±3.5 for unsuccessful teams (t38 = 3.1, p < 0.05).
To explore the differences in the attacking production on a team-by-team
basis, a graph of the number of shots per match by the goals to shots ratio
was created (figure 2). From the graph, teams in the top-right managed to
create and convert many chances and teams in the bottom-left neither
created nor converted many. Interestingly, despite the presumed superior
24
technical ability of successful teams players, the difference between
successful and unsuccessful teams seems to be - with the exception of
Schalke – in the frequency of shots taken, and not the ratio of goals to shot.
Figure 4. Number of shots per match by goals to shots ratio for successful
and unsuccessful teams.
25
Additionally, unsuccessful teams tended to remain within the low shots and
low conversion zone. Interestingly, Cluj managed to convert as many shots as
Barcelona, but took far fewer per match.
Figure 5. Heat map to show the percentage of shots and goals with respect to
location for successful and unsuccessful teams.
One clear difference between the percentage of shots and goals that
successful and unsuccessful teams with respect to location is that
unsuccessful teams take a larger percentage of shots from outside the final
3rd and from wide positions. Conversely, successful teams shoot from central,
closer
positions.
26
Figure 6. Heat map to show the percentage of shots and goals taken by
successful and unsuccessful teams inside and outside the penalty area.
The second heat map (figure 5) shows a more obvious difference in the
location that successful and unsuccessful teams shoot from; successful teams
shoot more frequently from inside the penalty area, and for unsuccessful
teams the opposite is true. It was later found that the frequency of shots and
goals taken per match from inside the penalty area by successful teams
(median = 8) was significantly more than unsuccessful teams (median = 4) (U
= 77, z = -3.193, p < 0.01). Unsuccessful teams didn’t however shoot
significantly more from outside the penalty area (median = 6.5) than
successful teams (median = 6) (U = 178.5, z = -.375, p = .708).
Table 1 presents the total frequencies of each assist type, and whether the
assist preceded a goal, shot on target or off target. From this, it is evident that
the short pass is the favoured assist type of both successful and unsuccessful
teams and, excluding ‘other’, the byline is the least frequent for both groups.
27
Table 13. Assists used by successful and unsuccessful teams.
Successful teams used the byline twice as many times as unsuccessful
teams, despite the successful teams sample constituting 8 matches fewer.
Despite suggesting superior ability to gain entry behind the opposition
defence, this difference was not found to be statistically significant though (U
= 148, z = -1.608, p = .233). A difference was found for crosses, as successful
teams used the cross (median = 2) significantly more times per game than
unsuccessful teams (median = 1) (U = 114.5, z = -2.207, p < 0.05),
suggesting greater ability to attack down the wings and get into a position to
take a cross, and also an increased technical ability in taking a cross that will
reach a teammate in a position to shoot.
Although table 1 presents the frequencies of all assists, as the samples were
unbalanced in terms on number of matches played, it is difficult to infer any
trends. Consequently, figure 6 presents pie charts to show the percentage –
and frequency (data labels) - that each assist type contributed to the overall
assist totals.
28
Figure 7. Percentage and frequency of each assist type.
Although successful teams scored more goals, the percentage of goals with
respect to possession length does not seem to differ between successful and
unsuccessful teams (figure 7).
Figure 8. Frequency and percentage of goals with respect to length of
possession for successful and unsuccessful teams.
29
Figure 9. Frequency and percentage of shots on target with respect to length
of possession for successful and unsuccessful teams.
The majority of unsuccessful teams shots on target come after possession
lengths of 0 or 1 passes (figure 8). Conversely, successful teams appear to be
more able to produce shots on target after longer possession lengths, noticed
by the less prominent decline in the height of the green bars.
Figure 10. Frequency and percentage of shots off target with respect to length
of possession for successful and unsuccessful teams.
30
Successful teams produced a higher percentage of shots off target from
possession lengths of 6, 7, 8, 9 and 10 or more (figure 9).
Figure 11. Frequency and percentage of all goals and shots with respect to
length of possession for successful and unsuccessful teams.
For the percentage of shots and goals with respect to possession length, a
similar downward trend can be seen for both the successful and unsuccessful
groups (figure 10). A higher percentage of successful teams shots and goals
came after possession lengths of 4, 6, 7, 8, 9, and 10 or more for successful
teams in comparison to unsuccessful teams, possibly indicating increased
ability to retain possession. Interestingly, although successful teams produced
more shots and goals at longer possession lengths than unsuccessful teams,
successful teams scored over 90% of their goals after possession of 3 passes
or fewer (figure 11).
There was however no statistically significant difference between the 9.5±4.3
shots and goals per game following possession lengths of 3 passes or fewer
for successful teams and the 8.3±2.7 by unsuccessful teams (t38 = 1.0, p =
.302).
31
Unfortunately, due to time and other constraints, the data in this study has not
been normalized with respect to the frequency of the respective lengths of
possession. As there are many more possessions of 3 passes or fewer than
there are of, for instance, 8 passes, it is unknown from the results of this study
whether the downward trend in shot and goal production as possession length
increased that was observed would have held true if normalized.
100%
2
90%
24
80%
46
53
5
49
70%
60%
50%
24
40%
64
30%
60
68
8
99
20%
10%
0%
Goals
Shots On Shots Off
Goals
Successful
Shots On Shots Off
Unsuccessful
Figure 12. % and frequency of shots and goals with respect to possession
length for successful and unsuccessful teams.
32
4.3 Possession Variables
60
Frequency
50
40
30
20
10
0
Def 3rd
Mid 3rd
Att 3rd
Def 3rd
Mid 3rd
No Shot
Att 3rd
Shot
Successful
Unsuccessful
Figure 13. Average number of won possessions per match with respect to
location and outcome for successful and unsuccessful teams.
The only location and outcome that unsuccessful teams won possession more
times per match (mean = 43.5±8.2) than successful teams (mean =
38.8±10.7) was the defensive 3rd no shot, however this was not statistically
significant (t38 = -1.6, p = .117). With regards to the possessions won in the
defensive 3rd that ended in a shot, although successful teams managed to do
this more times per match (median = 1.5) than unsuccessful teams (median =
1) this was not statistically significant (U = 146.5, z = -1.295, p = .195).
For possessions won in the midfield 3rd that did not end in a shot, successful
teams did this significantly more times per match (mean = 50.1±10.9) than
unsuccessful teams (mean = 42.9±9.4) (t38 = 2.2, p = < 0.05). For
possessions won in the midfield 3rd that did end in a shot, successful teams
accomplished this significantly more times per match (mean = 5±2.7) than
unsuccessful
teams
(mean
=
3±1.8)
33
(t38
=
2.9,
p
<
0.01).
For possessions won in the attacking 3rd that did not end in a shot, successful
teams achieved this significantly more times per match (median = 7.5) than
unsuccessful teams (median = 5) (U = 115.5, z = -2.138, p < 0.05). For
possessions won in the attacking 3rd that did end in a shot, although
successful teams managed this more frequently per match (median = 3) than
unsuccessful teams (median = 2), this was not statistically significant (U =
134, z = -1.634, p = .102).
50
45
40
Frequency
35
30
25
20
15
10
5
0
Def 3rd
Mid 3rd
Att 3rd
No Shot
Def 3rd
Mid 3rd
Att 3rd
Shot
Successful Opposition
Unsuccessful Opposition
Figure 14. Average number of won possessions per match with respect to
match location and outcome for the opposition of successful and unsuccessful
teams.
The opposition of successful teams won possession more times per match
(mean = 45.9±10.0) than the opponents of unsuccessful teams (mean =
40.3±11.0) in the defensive 3rd with the outcome of no shot, however this was
not statistically significant (t38 = 1.6, p = .107). With regards to the
possessions won in the defensive 3rd that ended in a shot, the opponents of
unsuccessful teams managed this significantly more times per match (median
34
= 1) than the opponents of successful teams (median = 0) (U = 118, z = 2.130, p < 0.05).
For possessions won in the midfield 3rd that did not end in a shot, the
opponents of successful teams accomplished this less per match (mean =
43.2±14.9) than the opponents of unsuccessful teams (mean = 46.6±10.7),
however this was not statistically significant (t38 = -.848, p = .402). For
possessions won in the midfield 3rd that did end in a shot, the opponents of
unsuccessful teams managed this significantly more (median = 5.5) per match
than the opponents of successful teams (median = 3) (U = 91.5, z = -2.807, p
< 0.01).
For possessions won in the attacking 3rd that did not end in a shot, the
opponents of unsuccessful teams achieved this more times per match
(median = 5) than the opponents of successful teams (median = 3), but this
was not found to be statistically significant (U = 122, z = -1.947, p = 0.051).
For possessions won in the attacking 3rd that did end in a shot, although the
opponents of unsuccessful teams accomplished this more frequently per
match (median = 3) than the opponents of successful teams (median = 2), this
Unsuccessful Successful
was not statistically significant (U = 154, z = -1.066, p = .287).
Left
Middle
Right
Left
Middle
Right
-
100
200
300
400
500
600
Figure 15. Final 3rd and penalty area entries for successful and unsuccessful
teams.
35
Both successful and unsuccessful teams made most entries through the
middle, and fewest down the left (figure 14).
Successful
Unsuccessful
-
200
400
600
800
1,000
1,200
Figure 16. Total final 3rd and penalty area entries for successful and
unsuccessful teams.
In total, successful teams made more final 3rd and penalty area entries (figure
15). Per match, successful teams made significantly more final 3 rd entries
(mean = 79.7±25.3) than unsuccessful teams (mean = 51.1±15.9) (t38 = 4.4, p
< 0.01). They also made significantly more penalty area entries per match
(median = 16.5) than unsuccessful teams (median = 7.5) (U = 65.5, z = 3.502, p < 0.01).
36
CHAPTER 5: DISCUSSION
CHAPTER 5: DISCUSSION
5.1 Overview
The aim of this study was to discover the differences in attacking play
between successful and unsuccessful football teams that competed in the
UEFA Champions League 2010-11 tournament.
This discussion has been divided into the differences – variables that were
significantly different - between successful and unsuccessful teams and the
similarities - variables where no significant difference was found - between
successful and unsuccessful teams.
5.2 Differences between successful and unsuccessful teams
Shooting Variables
Predictably, the frequency of successful teams shots and goals per game was
significantly higher than unsuccessful teams (p < 0.05). This conforms with
prior research into the UEFA Champions League football (Szwarc, 2007),
studies undertaken on top teams in Greece (Armatas et al., 2009), Spain
(Lago-Ballesteros and Lago-Peñas, 2010; Lago-Peñas et al., 2010) and on
World Cup quarter-finalists (Hughes and Franks, 2005). Obviously, this result
is part explained by the fact that to be classed as a successful team for this
study teams had to reach the quarter-final, which is of course dependent upon
goals scored.
Nevertheless, it could be possible that successful teams shoot less frequently,
but convert a higher percentage, and so it was still necessary to investigate.
Therefore, the number of shots taken per match by a team was plotted
against their goals to shot ratio which allowed trends and outliers to become
more apparent. For example Schalke’s shot frequency of 8.75 per match was
the lowest of all analysed teams, but their success was explained by the fact
that they had, by far, the best goals to shot ratio. Excluding Schalke, the three
37
remaining successful teams were the three teams who shot most frequently
per match. This is interesting in that successful teams have generally been
shown to have better goals to shot ratios (Lago-Ballesteros and Lago-Peñas,
2010) but in this study, the difference seems to be only in the frequency.
Based on these results, those in an applied situation could simply advise their
teams to shoot more frequently, on the basis that regardless of ability, the
more frequently you shoot the more likely you will score.
Further examination showed clear differences not only in the frequency per
game, but in the percentages of each shot type. Goals and shots on target
accounted for 10% and 42% of successful teams overall shots and goals,
compared to just 5% and 35% for unsuccessful teams. The largest split
though was for shots off target, which accounted for 60% of all unsuccessful
teams shots and goals, but just 48% for successful teams. This seems to
suggest that the difference between successful and unsuccessful teams is not
just in the quantity of shots but the quality. This notion echoes prior research,
as it has been shown that the effectiveness of shots is 3 times greater for
winning UEFA Champions League finalists than losers (Szwarc, 2007),
greater for winning teams in Spain than drawing or losing teams (Lago-Peñas
et al., 2010) and that, based on league standing, top teams require
significantly fewer shots to score a goal than bottom teams (Lago-Ballesteros
and Lago-Peñas, 2010). This finding implies not only superior tactical ability of
players, in creating and taking shooting opportunities, but also technical ability
in producing more on target. Of course poor conversion rates is not only
dependent upon the aforementioned factors, but also luck and quality of the
opposition defence and goalkeeper (Papahristodoulou, 2008), and so the
difference in frequency of shots and goals may be explained in that successful
teams are likely to be shooting against goalkeepers of lower ability and had
they been playing against goalkeepers of a similar standard to themselves,
would not have scored as many. Similarly, this study did not examine further
contextual factors upon shots and goals such as the number of players
between the shot location and the goal, or the position of the goalkeeper, both
of which have been shown to significantly affect the number of goals scored
(Wright
et
al.,
38
2011)
Due to the location of the penalty area, it is not surprising that it has
repeatedly been identified as the most frequent location for goals (Dufour,
1993; Olsen, 1998; Michailidis et al., 2004; Sotiropoulous et al., 2005: cited in
Yiannakos and Armatas, 2006; Yiannakos and Armatas, 2006; Armatas and
Yiannakos, 2010; Wright et al., 2011) and the results of this study are no
different (inside = 32; outside = 7). In fact, the overall 82% value of goals
scored inside the penalty area almost replicates the 80% (Dufour, 1993) and
82% (Sotiropoulous et al., 2005; cited in Yiannakos and Armatas, 2006)
previously found. With regards to the difference between sucessful and
unsuccessful teams, successful teams took significantly more shots and goals
from inside the penalty area (p < 0.01), a result that has been previously
found by Kapidžić et al. (2010), and Luhtanen et al. (1997) who observed that
the FIFA 1994 World Cup Winners Brazil had the highest number of scoring
chances in the vital area.
Evidently, it is easier to shoot on target and score the closer to goal a shot is
taken, or precisely each extra yard from goal decreases the odds of scoring
by 15% (Pollard et al., 2004), and so this could explain why successful teams
shot and scored more frequently. Obviously, shooting from within the penalty
area is first dependent upon gaining entry into the penalty area, which seems
to suggest why a significant difference exists between successful and
unsuccessful teams.
Possession Variables
Successful and unsuccessful teams did not differ greatly in the type of assist,
with just one notable exception. This was that successful teams used the
cross significantly more times per game than unsuccessful teams (p < 0.05),
suggesting greater ability to attack down the wings, and increased technical
ability in taking a cross that will reach a teammate in a position to shoot. This
finding is notable in that prior research pertaining to the cross is mixed. Whilst
the French national team - at a time when they were the worlds best team created significantly more crosses than their opponents (Griffiths, 1999), it has
also been shown that losing teams make, on average, significantly more
39
crosses per game than drawing or winning teams (Lago-Peñas et al., 2010).
Of course match result and performance are not necessarily equivalent
(Lago, 2007), and so more research is needed to fully investigate the, to date,
inconsistent influence of crosses on team performance. Especially as it has
recently been shown that most (31%) of all goals are scored when the assist
is from an airborne delivery (Wright et al., 2011). Naturally, not all crosses are
played in the air, and not all assists that are played into the air are crosses,
but this still goes to show the obvious power of using the cross. Despite this,
the study did not account for the differences between successful and
unsuccessful teams, and was not conducted upon the UEFA Champions
League.
For possessions won in the midfield 3rd that did, and did not, end in a shot
successful teams did this significantly more times per match than
unsuccessful teams (no shot: p < 0.05; shot: p < 0.01). It is difficult to draw
exact comparisons between this specific result and prior studies as
possession has typically been examined in terms of time. For example
although the finding of this study would seem to suggest superior technical
ability in making tackles, or superior tactical ability in playing a strategy that
forced turnovers, studies have demonstrated that teams have significantly
more possession when they are losing than drawing or winning (Jones et al.,
2004; Lago and Martín, 2010). Therefore it is possible that the number of
teams successful teams won possession is perhaps more indicative of the
duration unsuccessful teams had possession – as to win it the other team
must have had possession. Despite this, the studies of Bloomfield et al.
(2005) and Jones et al. (2004) demonstrated that regardless of score-line
successful teams have significantly more possession. Again though,
comparisons between successful teams from this study and those of
Bloomfield et al. (2005) and Jones et al. (2004) are restricted as possession
was defined in terms of time.
Pressurising opponents into making mistakes, and thus winning possession in
the attacking 3rd, is a tactical recommendation made by Olsen (1988), the
40
basis of which is to further reduce the time in possession of the player on the
ball - the increased pace of play has already led to a partial decrease in time
in possession. This is perhaps why top European club teams win between
32.1-60% of their possessions here (Garganta et al., 1997). It is no surprise
then that for possessions won in the attacking 3 rd that did not end in a shot,
successful teams achieved this significantly more times per match than
unsuccessful teams (p < 0.05). Despite this significant difference, the figures
found in this study do not replicate those of Garganta et al. (1997). For
possessions won in the attacking 3rd, successful teams managed this 9.8
times per match, and unsuccessful teams just 7.4 times per match. This
equates to only 8.8% of all successful teams won possession and just 7.5%
for unsuccessful teams. Without further research it will remain unclear
whether this result is representative of UEFA Champions League football,
although it does seem tentative comparisons can be drawn from this result
and those of James et al. (2002). They documented that in comparison to
domestic matches, European matches have a greater frequency of play
occurring in the pre-defensive areas of the pitch – therefore the presumption
being that if possession occurs more frequently in defensive and predefensive areas then it is more likely that possession will be won and lost
here. It does however seem more logical to draw comparisons with James et
al.’s (2002) paper than Garganta et al.’s (1997) on the basis of methodological
limitations - the latter paper looked at just 5 teams, with one team being
notated for just 5 matches – and the passing of time since its publication; it is
recommended that the results of performance analysis research should not be
taken as representative beyond the era of the matches analysed
(O’Donoghue, 2010).
For possessions won in the defensive 3rd and midfield 3rd that ended in a shot,
the opponents of unsuccessful teams managed this significantly more than
the opponents of successful teams (defensive 3rd: p < 0.05; midfield 3rd: p <
0.01). They also managed to win possession on average more frequently in
the midfield and attacking 3rd when playing against unsuccessful teams.
Lanham (2005) revealed that the difference between winning and losing was
achieved simply by losing possession fewer times than the opposition short of
41
the final (attacking) 3rd and, as the inability of unsuccessful teams to retain
possession is highlighted here it could, in part, explain their lack of success.
Making cautious inferences, it could be that not only do unsuccessful teams
lose possession more frequently but also once they have lost possession they
fail to effectively defend the subsequent attack. For instance, counter attacks
have been shown to be more effective than elaborate attacks when playing
against an imbalanced defence (Tenga et al., 2010). Consequently, further
examination - taking in a wider range of contextual factors - into the reasons
that the opposition of unsuccessful teams create significantly more shots from
regained possession is needed.
Bate (1988) and Lanham (2005) both regarded the frequency of these entries
as vital to success, and along these lines it was found that successful teams
made significantly more final 3rd entries (p < 0.01) than unsuccessful teams
per match. Correspondingly, Tenga et al. (2010b) found this area to have
considerably higher goal scoring effectiveness than other pitch areas and thus
goes some way to explaining the difference found.
The other area where entries into were notated was the penalty area.
Similarly to final 3rd entries, it was found that successful teams penalty area
entries per match than unsuccessful teams (p < 0.01). Research into entries
into this area is sparse, although the well-documented effectiveness of
shooting from this area was described earlier. Ruiz-ruiz et al. (2011) did
however directly notate penalty area entries, and examined the effects of
match status, team strategy, player dismissals and quality of opposition.
Pertinent to the results of this study is their finding that regardless of the
strength of the team being notated, the opposition strength did not
significantly affect the number of penalty areas they received. Succinctly,
weak teams did not concede significantly more penalty area entries when
playing against strong opposition as they did when playing against medium or
weak. It is interesting then that this study did find a difference, as Ruiz-Ruiz et
al. (2011) postulated that it could be the quality and not the quantity of entries
that is the key differences between the strengths of different teams.
42
5.3 Similarities between successful and unsuccessful teams.
Shooting Variables
With regards to the frequency of shots and goals produced per match from
outside the penalty area, a statistically significant difference between
successful and unsuccessful teams was not found (p = .699). Nevertheless,
just because the samples were not significantly different it does not instantly
prove equivalence (Tenga et al., 2010b). For instance, 62.4% of all
unsuccessful teams attempts at goal came from outside the penalty area, in
contrast to just 48.6% for successful teams. Furthermore, 10.8% of
unsuccessful teams attempts at goal were taken from outside the final 3 rd but
just 2.1% of all successful teams attempts were taken here. These indicate
unsuccessful teams shoot from areas that have been shown to be less
conducive to scoring, although further examination of why this occurs is
needed.
Successful team used the ‘byline’ as an assist 8 times, twice as many as
unsuccessful teams, suggesting better ability to gain entry behind the
opposition defence into dangerous areas. This difference was not found to be
statistically significant though (p = .233). From figure 7, it is evident that
although the difference in the frequency for each assist type between
successful and unsuccessful teams can vary by over 20, successful and
unsuccessful teams are very similar in the proportion of each assist type
used. This would seem to indicate that the type of assist has no influence on
shot or goal production, and therefore success (match outcome). Of course as
a difference was found for crosses, evidently there is a requirement for future
research to continue to examine the impact of assist type upon shot and goal
production and the differences between the varying strength of teams.
The final area of interest pertaining to shots and goals was the length of the
preceding possession. This study determined that 92% of successful and 62%
of unsuccessful teams goals are scored following a possession length of 3
passes or fewer. Combining all shot outcomes shows that 60% of successful
43
teams, and 69% of unsuccessful teams shots and goals came following a
possession length of 3 passes or fewer. There was however no statistically
significant difference between the frequency of shots and goals per game
following possession lengths of 3 passes or fewer for successful teams and
unsuccessful teams (p = .302). The percentages found for goals seem to
indicate that regardless of the strength of team, possessions of fewer passes
are favorable. The 92% - and to a lesser degree the 62% - finding found in
this study correspond to the 80% of goals from 3 passes or fewer found by
Reep and Benjamin (1968), 94% from 4 passes or fewer by Bate (1988) and
the 84% and 80% from 4 passes or fewer from consecutive FIFA World Cups
by Hughes and Franks (2005). Future research should examine the effect of
possession length on goal and shot production upon teams competing in the
UEFA Champions League but normalize the data for possession length.
Normalising data leads to a more thorough understanding of the data (Hughes
and Franks, 2005) and it would be interesting whether, when normalized, the
data would indicate whether possession play or direct play is more effective.
Possession Variables
Unsuccessful teams won possession (no shot) in the defensive 3rd more than
unsuccessful teams, but it was not significant (p = .117). With regards to the
possessions won in the defensive 3rd that ended in a shot, although
successful teams managed to do this more times per match than
unsuccessful teams, this was not statistically significant (p = .195). Despite
the lack of significance difference, this hints at the higher technical and
tactical ability of successful teams in that they are able to advance up the
pitch and create shooting opportunities more frequently.
For possessions won in the attacking 3rd that did end in a shot, although
successful teams managed this more frequently per match than unsuccessful
teams this was not statistically significant (p = .102). In fact, successful teams
averaged 3.4 shots from possession gained in the attacking 3 rd per match, or
32.7% of all their possessions won that ended in shots. Unsuccessful teams
44
averaged just 2.4 shots from possession won in the attacking 3 rd per match,
or 35.8% of all their possessions won that ended in shots. These figures are
about half of the 50-60% of shots and goals from possession gained in the
attacking 3rd as reported by Bate (1988).
Bate’s (1988) analysis was of
international soccer, and is now dated as more contemporary research has
shown that regained possession in the attacking 3 rd accounted for 16% of
goals for the 1998 FIFA World Cup and 20% for the 2002 FIFA World Cup
(Carling et al., 2005). They inferred from this, and analysis conducted upon
the English Premier League, that international teams do not pressurize
opponents to the extent that domestic teams do. From the teams analysed in
this study of UEFA Champions League football, it would appear that their
tactics are more aligned with international than domestic football. This could
also explain why significant differences were found between successful and
unsuccessful teams for winning possession in the midfield 3 rd, and why this
was the area where possession was won most frequently.
For possession won in the defensive 3rd that did not result in a shot, the
opponents of successful teams managed this more frequently per match,
however this was not statistically significant (p = .107). For possession won in
the midfield and attacking 3rd that did not result in a shot, the opponents of
unsuccessful teams managed this more frequently per match than the
opponents of successful teams, however a statistically significant difference
was found for neither (midfield: p = .402; attacking 3rd: p = .051). For
possessions won in the attacking 3rd that did end in a shot, although the
opponents of unsuccessful teams accomplished this more frequently per
match than the opponents of successful teams, this was not statistically
significant (p = .287). The failure to find significant differences is as much to
do with the ability of successful and unsuccessful teams to retain possession
as it is the opposition to win it. It would appear though that when playing
against weaker opposition (unsuccessful teams) the opposition are able to win
possession more frequently, although once possession has been lost
successful and unsuccessful teams are similarly matched in their ability to
thwart
attacks.
45
CHAPTER 6: CONCLUSION
CHAPTER 6: CONCLUSION.
6.1 Main Findings.
This study attempted to find the differences in attacking play between
successful and unsuccessful teams in the 2010-11 UEFA Champions League
competition.
The following hypotheses were accepted;
1: Successful teams will shoot and score significantly more than unsuccessful
teams.
2: Successful teams will gain significantly more entries into their opponents
defensive 3rd than unsuccessful teams.
3: Successful teams will gain significantly more entries into their opponents
penalty area than unsuccessful teams.
The finding that successful teams shoot significantly more than unsuccessful
teams supports the existing body of work (Hughes and Franks, 2005; Szwarc,
2007; Armatas et al., 2009; Kapidžić et al., 2010; Lago-Ballesteros and LagoPeñas, 2010; Lago-Peñas et al., 2010;).
Similarly, entries into the final 3rd and penalty area have been identified as key
to success (Bate, 1988; Lanham, 2005; Tenga et al., 2010) and so it was
expected, and found, that successful teams would advance into these areas
significantly more frequently than unsuccessful teams.
Moreover, the penalty area has consistently been shown to be more
conducive for goal scoring (Olsen, 1998; Dufour, 1993; Michailidis et al.,
2004; Sotiropoulous et al., 2005: cited in Yiannakos and Armatas, 2006;
Yiannakos and Armatas, 2006; Armatas and Yiannakos, 2010; Wright et al.,
2011) and that successful teams take significantly more shots and goals here
46
(Luhtanen et al., 1997; Kapidžić et al., 2010). Accordingly, successful teams
in this study took significantly more shots and goals from inside the penalty
area than unsuccessful teams (p < 0.01). This is also probably due to the
earlier described difference in penalty area entries, reinforcing the importance
of gaining entry into this area.
The following hypotheses were not accepted;
1: Successful teams will win possession of the ball significantly more than
unsuccessful teams across all areas of the pitch.
2: Unsuccessful teams opponents will win possession significantly more times
than the opponents of successful teams across all areas of the pitch.
Although successful teams did not win possession significantly more than
unsuccessful teams across all areas, they did win possession significantly
more frequently in the midfield 3rd (shot and no shot) and in the attacking 3rd
(no shot). This would seem to imply that successful teams press their
opponents higher up the pitch and with more intent.
Although the opponents of unsuccessful teams did not win possession
significantly more than unsuccessful teams across all areas, they did win
possession significantly more frequently in the defensive 3rd and midfield 3rd
that ended in a shot. This indicates the inferior ability of unsuccessful to retain
possession, and once possession has been lost, to prevent shots.
6.2 Applied implications.
Based on the limitations and findings of this studies, there are numerous
avenues that future research could investigate, namely;
-
The differences in defensive variables between successful and
unsuccessful teams to in the UEFA Champions League.
-
As ethnic differences have been identified for international football
(Brown and Hughes, 2004) exploration into whether ethnic differences
47
-
In attacking play of clubs competing in the UEFA Champions league
exist.
-
Only matches at the group stage were notated, and so it is possible
that successful teams knew they were already through to the last 16, or
that unsuccessful teams knew they would not go through. Therefore it
is
possible
that
the
managers
attempted
new
strategies
or
inexperienced players because they knew the outcome of the match
would not affect their tournament progression. Consequently, the affect
of tournament progression, as some matches are evidently more
important than others, upon attacking play is an area that deserves
attention.
48
CHAPTER 7: REFERENCES
CHAPTER 7: REFERENCES
Acar, M. F., Yapicioglu, B., Arikan, N., Yalcin, S., Ates, N. and Ergun, M.
(2009). Analysis of goals scored in the 2006 World Cup. In Science and
Football VI (edited by T. Reilly and F. Korkusuz), pp. 235-242. London:
Routledge.
Armatas, V. and Yiannakos, A. (2010). Analysis and evaluation of goals
scored in the 2006 World Cup. Journal of Sport and Health Research,
2(2), 119-128.
Armatas V, Yiannakos A, Zaggelidis G, Skoufas D, Papadopoulou S, Fragkos
N. (2009). Goal scoring patterns in Greek top leveled soccer matches.
Journal of Physical Education in Sport, 23(2), 1‐ 5.
Atkinson, G. and Nevill, A.M. (1998). Statistical methods for assessing
measurement error (reliability) in variables relevant to sports medicine.
Sports Medicine, 26, 217-238.
Bangsbro, J. and Pietersen, B. (2004). Offensive Soccer Tactics: How to
control possession and score more goals. Leeds: Human Kinetics.
Bate, R. (1988). Football chance: tactics and strategy. In Science and Football
(edited by T. Reilly, A. Lees, K. Davids and W. Murphy), pp. 293–301.
London: E. & F. N. Spon.
Blaze, A., Atkinson, G., Harwood, C. and Cale, A. (2004). Prevalence and
perceptions of performance analysis in the English Premier Association
Football League. In Performance Analysis of Sport 6 (edited by P.
O’Donoghue and M. Hughes), pp. 79-83. Cardiff: CPA Press, UWIC.
Bloomfield, J. R., Polman, R. C. J., & O’Donoghue, P. G. (2005). Effects of
score-line on team strategies in FA Premier League Soccer. Journal of
Sports Sciences, 23, 192 – 193.
49
Brown, S. and Hughes, M. (2004). The attacking patterns in offensive areas of
European, South American, African and Asian teams in the 2002 World
Cup for association football. In Performance Analysis of Sport 6 (edited
by P. O’Donoghue and M. Hughes), pp. 92-102. Cardiff: CPA Press,
UWIC.
Carling, C., Williams, A. M. and Reilly, T. (2005). Handbook of soccer match
analysis: a systematic approach to improving performance. London:
Routledge.
Cooper, S-M., Hughes, M., O’Donoghue, P. and Nevill, A.M. (2007). A simple
statistical method for assessing the reliability of data entered into sport
performance analysis systems. International Journal of Performance
Analysis of Sport, 7, 87-109.
Cox, R., Russell, D. and Vamplew, W. (2002). Encyclopedia of British
Football. London: Routledge.
Croucher, J. S. (1997). The use of notational analysis in determining optimal
strategies in sports. In Notational Analysis of Sport 1 and 2 (edited by
M. Hughes), pp. 3-21. Cardiff: UWIC.
Dobson, S. and Goddard, J. (2011). The Economics of Football. Cambridge:
Cambridge University Press.
Dufour, W. (1993). Computer-assisted scouting in soccer. In Science and
Football (edited by T. Reilly, A. Lees, K. Davids and W.J. Murphy),
pp.160-166. London: E. & F. Spon.
FIFA. (2012). FIFA/Coca-Cola World Ranking [on-line].
http://www.fifa.com/worldranking/rankingtable/index.html [accessed 20
February 2012].
50
Franks, I.M. and Miller, G., (1986). Eyewitness testimony in sport. Journal of
Sport Behaviour, 9, 39−45.
Frencken, W. G. P. and Lemmink, K. A. P. M. (2007). Team kinematics of
small-sided soccer games. In Science and Football VI (edited by T.
Reilly and F. Korkusuz), pp. 161-166. London: Routledge.
Fullerton, H. S. (1912). The Inside Game: The Science of Baseball. The
American Magazine, LXX, 2-13.
Garganta, J., Maia, J. and Basto, F. (1997). Analysis of goal-scoring patterns
in European top level soccer teams. In Science and Football III (edited
by T. Reilly, J. Bangsbro and M. Hughes) pp. 246-250. London: E. & F.
Spon.
Goldblatt, D. (2007). The Ball is Round: A Global History of Football. London:
Penguin Books.
Griffiths, D.W. (1999) An analysis of France and their opponents at the 1998
soccer World Cup with specific reference to playing patterns. PhD
thesis. University of Wales Institute Cardiff.
Hughes, M., Cooper, S-M. and Nevill, A. (2002). Analysis procedures from
non-parametric data from performance analysis. International Journal of
Performance Analysis of Sport, 2, 6-20.
Hughes, M., Cooper, S. M., & Nevill, A. (2004). Analysis of notation data:
reliability. In Notational Analysis of Sport: Second Edition (Edited by M.
Hughes & I.M. Franks), pp. 189-204. London: Routledge.
Hughes, M., Evans, S. and Wells, J. (2001). Establishing normative profiles in
performance analysis. International Journal of Performance Analysis in
Sport, 1, 1-26.
51
Hughes, M.D., Robertson, K., & Nicholson, A. (1988). An analysis of the 1984
World Cup of Association Football. In Science and Football (edited by T.
Reilly, A. Lees, K. Davids and W. Murphy), pp. 363 – 367. London: E &
FN Spon.
Hughes, M. and Franks, I. M. (2005). Analysis of passing sequences, shots
and goals in soccer. Journal of Sports Sciences, 23(5), 509-514.
Hughes, M. and Franks, I. M. (2008). The Essentials of Performance Analysis:
An Introduction. London: Routledge.
James, N. (2006). Notational analysis in soccer: past, present and future.
International Journal of Performance Analysis in Sport, 6(2), 67-81.
James, N., Mellalieu, S. D. and Hollely, C. (2002). Analysis of strategies in
soccer as a function of European and domestic competition.
International Journal of Performance Analysis in Sport, 2(1), 85-103.
James, N., Taylor, J. and Stanley, S. (2007). Reliability procedures for
categorical data in Performance Analysis. International Journal of
Performance Analysis of Sport, 7, 1-11.
Jinshan, X., Xiaoke, C., Yamanaka, K. and Matsumoto, M. (1993). Analysis of
goals scored in the 14th World Cup. In Science and Football (edited by T.
Reilly, A. Lees, K. Davids and W. Murphy), pp. 203-205. London: E&F
Spon.
Jones, P. D., James, N. and Mellalieu, S. D. (2004). Possession as a
performance indicator in soccer. International Journal of Performance
Analysis in Sport, 4(1), 98-102.
52
Kapidžić, A., Mejremić, E., Bilalić, J. and Bečirović, E. (2010). Differences in
some parameters of situation efficiency between winning and defeated
teams at two levels of competition. Sport Scientific and Practical
Aspects, 7(2), 21-28.
Lago, C. (2007). Are winners different from losers? Performance and chance
in the FIFA World Cup Germany 2006. International Journal of
Performance Analysis in Sport, 7(2), 36-47.
Lago, C. (2009). The influence of match location, quality of opposition, and
match status on possession strategies in professional association
football. Journal of Sports Sciences, 27(13), 1463-1469.
Lago, C. and Martín, R. (2007). Determinants of possession of the ball in
soccer. Journal of Sports Sciences, 25(9), 969-974.
Lago-Peñas, C., Lago-Ballesteros, J., Dellal, A. and Gómez, M. (2010).
Game-related statistics that discriminated winning, drawing and losing
teams from the Spanish soccer league. Journal of Sports Science and
Medicine, 9(2): 288-293.
Lago-Ballesteros, J. and Lago- Peñas, C. (2010). Performance in team sports:
Identifying the keys to success in soccer. Journal of Human Kinetics, 25,
85-91.
Laird, P. and Waters, L. (2008). Eyewitness recollection of sport coaches.
International Journal of Performance Analysis of Sport, 8(1), 76-84.
Lanham, N. (2005). The goal complete: The winning difference. In Science
and Football V (edited by T. Reilly, J. Cabri and D. Araújo), pp. 194–200.
London: Routledge.
53
Luhtanen, P. H., Korhonen, V. and Llka, A. (1997). A new notational analysis
system with special reference to the comparison of Brazil and its opponents in
the World Cup 1994. In Science and Football 3 (edited by T. Reilly, J.
Bangsbro and M. Hughes), pp 229-232. London: E & F Spon.
Lyons, K. (1997). Lloyd Lowell Messersmith: Pioneer of notational analysis of
performance in sport. In Notational Analysis of Sport 1 and 2. (edited
by M. Hughes), pp. 49-58. Cardiff: UWIC.
Michailidis, C., Michailidis, I., Papaiakovou, G. and Papaiakovou, I. (2004).
Analysis and evaluation of way and place that goals were achieved
during the European Champions League of Football 2002-2003. Sports
Organization, 2(1), 48-54.
O’Donoghue, P. (2007). Reliability Issues in Performance Analysis.
International Journal of Performance Analysis of Sport, 7, 35-48.
O’Donoghue, P. (2009). Interacting Performances Theory. International
Journal of Performance Analysis in Sport, 9(1), 26-46.
O’Donoghue, P. (2010). Research Methods for Sports Performance Analysis.
London: Routledge.
Olsen, E. (1988). An analysis of goal scoring strategies in the World
Championship in Mexico, 1986. In Science and Football (edited by T.
Reilly, J. Clarys and A. Stibbe), pp. 373-376. London: E & F Spon.
Olsen, E. and Larsen, O. (1997). Use of match analysis by coaches. In
Science and Football III (edited by T. Reilly, J. Bangsbro and M.
Hughes), pp 209-220. London: E & F Spon.
Papahristodoulou, C. (2008). An analysis of UEFA Champions league match
statistics. International Journal of Applied Sports Science, 20(1), 67-93.
54
Pollard, R., Ensum, J. and Taylor, S. (2004). Estimating the probability of a
shot resulting in a goal: the effects of distance, angle and space.
International Journal of Soccer and Science, 2(1), 50-55.
Pollard, R. and Gómez, M. A. (2009). Home advantage in football in SouthWest Europe: Long-term trends, regional variation, and team
differences. European Journal of Sport Science, 9(6), 341-352.
Pollard, R. and Reep, C. (1997). Measuring the effectiveness of playing
strategies at soccer. The Statistician, 46, 541-550.
Pollard, R., Reep, C. and Hartley, S. (1988). The quantitative comparison of
playing styles in soccer. In Science and Football (edited by T. Reilly, A.
Lees, K. Davids and J. W. Murphy), pp. 309-315. London: E and F
Spon.
Redwood-Brown, A. (2008). Passing patterns before and after goal scoring in
FA Premier League Soccer. International Journal of Performance
Analysis in Sport, 8(3), 172-182.
Reep, C. and Benjamin, B. (1968). Skill and chance in Association Football.
Journal of the Royal Statistical Society, Series A, 131, 581-585.
Reilly, T. (1996). (ed) Science and Soccer. London: E. & F.N. Spon.
Reilly, T. and Gilbourne, D. (2003). Science and football: a review of applied
research in the football codes. Journal of Sports Sciences, 21, 693–
705.
Reilly, T. and Thomas, V. (1976). A Motion Analysis of Work-Rate in Different
Positional Roles in Professional Football Match-Play. Journal of Human
Movement Studies, 2, 87-97.
55
Rowlinson, M. and O’Donoghue, P. (2007). Performance profiles of soccer
players in the 2006 UEFA Champions League and the 2006 FIFA
World Cup. In Science and Football VI (edited by T. Reilly and F.
Korkusuz), pp. 229-234. London: Routledge.
Ruiz-Ruiz, C., Fradua, L., Fernández-García, A. and Zubillaga, A. (2011).
Analysis of entries into the penalty area as a performance indicator in
soccer. European Journal of Sport Science,
DOI:10.1080/17461391.2011.606834.
Scoulding, A., James, N. and Taylor, J. (2004). Passing in the soccer World
Cup 2002. International Journal of Performance Analysis in Sport, 4(2),
36-41.
Sotiropoulos, A., Mitrotasios, M., Traulos, A. (2005). Comparison in goal
scoring patterns between Greek professional and amateur teams. 1st
International Scientific Congress in Soccer, cited in, Yiannakos, A. and
Armatas, V. (2006). Evaluation of the goal scoring patterns in
European Championship in Portugal 2004. International Journal of
Performance Analysis in Sport, 6(1), 178-188.
Szwarc, A. (2007). Efficacy of successful and unsuccessful soccer teams
taking part in finals of Champions League. Research Yearbook, 13(2),
221-225.
Tenga, A., Holme, I., Ronglan, L. T. and Bahr, R. (2010a). Effect of playing
tactics on goal scoring in Norwegian professional soccer. Journal of
Sports Science, 28 (3), 237-244.
Tenga, A., Ronglan, L. T. and Bahr, R. (2010b). Measuring the effectiveness
of offensive match-play in professional soccer. European Journal of
Sport Science, 10(4), 269-277.
56
Thomas, J.R., Nelson, J.K. and Silverman, S.J. (2011). Research Methods in
Physical Activity. Leeds: Human Kinetics.
Tucker, W., Mellalieu, S. D., James, N. and Taylor, J. B. (2005). Game
location effects in professional soccer: a case study. International
Journal of Performance Analysis in Sport, 5(2), 25-35.
UEFA. (2010). Technical Report 2009/10. [online].
http://www.uefa.com/MultimediaFiles/Download/EuroExperience/uefaor
g/Publications/01/54/80/64/1548064_DOWNLOAD.pdf [Accessed 14
April 2011].
UEFA. (2011). History: Football’s premier club competition [on-line].
http://www.uefa.com/uefachampionsleague/history/index.html
[accessed on 14 July 2011].
Vincent, W.J. (2005). Statistics in Kinesiology. Leeds: Human Kinetics.
Wilson, J. (2008). Inverting the Pyramid: The History of Football Tactics.
London: Orion.
Wade, A. (1996). Principles of Team Play. Spring City: Reedswain.
Williams, J.J., Hughes, M., O’Donoghue, P. and Davies, G. (2007). A
reliability study of a Real-Time and Lapsed-Time application for rugby
union. International Journal of Performance Analysis of Sport, 7, 80-86.
Wright, C., Atkins, S., Polman, R. and Sargeson, L. (2011). Factors
associated with goals and goal scoring opportunities in professional
soccer. International Journal of Performance Analysis in Sport, 11(3),
438-449.
57
Yamanaka, K., Hughes, M. and Lott, M. (1993) An analysis of playing patterns
in the 1990 World Cup for association football. In Science and Football
II (edited by T.Reilly, A.Stibbe and J.Clarys), pp. 206-214. London: E. &
F.N.Spon.
Yiannakos, A. and Armatas, V. (2006). Evaluation of the goal scoring patterns
in European Championship in Portugal 2004. International Journal of
Performance Analysis in Sport, 6(1), 178-188.
58
APPENDICES
Appendix A. Blank copy of final Notation System.
ASSIST TYPE
DIRECT
SHOT
FWD
PASS
BYLINE
SHORT
PASS
CROSS
SET
PLAY
OTHER
GOAL
ON T
OFF T
POSSESSION WON
DEF
1/3
ATT
1/3
MID 1/3
No Shot
Shot
No Shot
Shot
ENTRIES
L
ATT 1/3
PEN AREA
M
R
Appendix B. Scanned, used copy of notation system.