Joseph Moore Dissertation

A COMPARATIVE ANALYSIS BETWEEN THE BUILD-UP PLAY IN
GOALS SCORED BETWEEN SUCCESSFUL AND UNSUCCESSFUL
TEAMS IN THE ENGLISH PREMIER LEAGUE
Joseph Moore
March 2014
Presented as part of the requirement for an award within the
Undergraduate Modular Scheme at
The University of the West of England
Hartpury College

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Declaration
This dissertation is a product of my own work and is not the work of any
collaboration.
I agree that this dissertation may be available for reference and photocopying
at the discretion of the college.
Joseph Moore

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Acknowledgments
I would like to express my thanks to Dr Laurence Protheroe for his guidance
in helping me complete this project. I would also like to thank my family for
not only supporting me whilst completing this project but for supporting me
throughout the duration of my study at undergraduate level.

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Contents
Section Page
List of Figures vi
Abstract vii
CHAPTER 1.0 – Introduction 1
CHAPTER 2.0 – Literature Review 4
2.1 Goal Scoring Notation 4
2.2 Build-up Play Notation 5
2.3 Tactical Development 7
2.4 Aims and Objectives 10
CHAPTER 3.0 – Methodology 11
3.1 Sample 11
3.2 Procedures 11
3.3 Data Analysis 13
CHAPTER 4.0 – Results 14
4.1 Overview of Build-up Passing Sequences 14
4.2 A Comparison of Build-up Passing Sequences 15
4.3 Possession Play vs Direct Play 16
4.4 A Comparison of Build-up Play Playing Style 17
4.5 Assist Results 18
4.6 Comparative Results for Type of Goals Scored 20
CHAPTER 5.0 – Discussion 22

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5.1 Results 22
5.2 Build-up Play Passing Sequences 22
5.3 Assist and Type of Goal 23
5.4 Tactical Implementation 24
5.5 Implications 25
5.6 Future Research 26
CHAPTER 6.0 – Conclusion 28
CHAPTER 7.0 – List of References 29
CHAPTER 8.0 – Appendices 35
Appendix A: Raw Goal Scoring Data 35
Appendix B: SPSS Output for Kolmogorov – Smirnov test for Normality 39
Appendix C: SPSS Output for Mann – Whitney U test 40
Appendix D: SPSS Output for Pearson’s Chi-Squared test (Build up
Playing style) 41
Appendix E: SPSS Output for Pearson’s Chi-Squared test (Assist Area) 42
Appendix F: SPSS Output for Pearson’s Chi-Squared test (Type of Goal) 43

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List of Figures
Figure Page
Figure 1.0: Pitch grid template used for the analysis of assist
information in the study. 12
Figure 2.0: Overview of build-up possession sequences and their
frequency of occurrence. 14
Figure 3.0: A comparison of build-up possession lengths
between top 6 and bottom 6 sides. 15
Figure 4.0: Direct vs Possession build up play statistics. 16
Figure 5.0: A comparison of Build-up possession categories
between top 6 and bottom 6 sides. 18
Figure 6.0: Assist area percentages for top 6 sides. 19
Figure 7.0: Assist area percentages for bottom 6 sides. 19
Figure 8.0: A comparison of types of goals scored between top 6
and bottom 6 sides. 21

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ABSTRACT
The aim of this project was to provide a quantitative analysis of the
differences in the build-up play in goals scored between successful and
unsuccessful teams in the English Premier League.
With the world of professional soccer being dominated by a focus upon
match outcomes it is understandable to see why there is such an emphasis
with regards to the goal (Scoulding, James & Taylor, 2004). Previous
research has allowed for an understanding into build up passing sequences
in relation to goals. However a further need for an analysis, particularly when
looking to compare successful and unsuccessful teams is still needed in
order to gain a greater understanding of how to implement tactics that look to
improve goal scoring.
The 6 top teams and 6 bottom teams in the English Premier League both had
10 games worth of goals analysed to determine the differences in scoring
patterns. Goals were analysed for build-up passing sequence lengths, as well
as area of assist before being categorised on resulting information.
Using a non-parametric test, in specific, the Mann – Whitney U test to
analyse build up passing sequences, results showed that there was a
significant level of difference (p.<0.5) between both groups. However, results
from Pearson’s Chi Squared test on the type of goals scored showed there
was not a significant difference (p.>0.5) in the relationship between both
groups.
Results from this study further support findings from previous studies that
successful sides enjoy lengthier passing sequences in the build up to goals
scored in comparison with unsuccessful ones (e.g. Hughes & Churchill, 2004;
Hughes & Franks, 2005; Hughes & Snook, 2006; Tenga et al., 2010).
However more research is needed to greater understand the differences in
build-up play between both groups of sides, with there being a lack of clarity
into where teams utilise possession. Therefore, a focus for future work
should be in the analysis of tactics that teams use to increase scoring
frequency.

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CHAPTER 1.0
Introduction
Over the last 10 years, there has been a growing interest in match-analysis
of soccer (Gréhaigne, Mahut & Fernandez, 2001). Coaches and analysts
now understand the benefits of notational analysis and are using it as an
objective way of recording performance, which they then use quantify critical
events that take place in performance in a consistent and reliable manner
(Franks & Hughes, 2007). Notational analysis allows for a greater clarity of
performance by removing the error of human perception. Franks and Miller
(1986) first demonstrated this when looking at recall and recognition of
international football coaches. Results found that coaches can only recall
around 30 percent of incidents that took place throughout performance. This
pivotal research likened coaches’ observations to eyewitness testimony of
criminal events and suggested that the reliance on such observations is not
only unreliable but also inaccurate. Evidence from Franks and Miller (1986)
research has being widely used to promote the need for an objective and
reliable recording and notation system with regards to sport performance
(James, 2006).
Coaches look to use analysis as a way of providing objective feedback to
athletes, with it often being a major factor in improvement of sport skill
performance (Liebermann et al., 2002). From a coaching perspective then
there is visible evidence of the benefits that using a notation system can
bring. The area however has developed, Grehaigne, Mahut and Fernandez
(2001) cautioned that notational analysis should also predict performance
rather than just describe behaviours. Analysts now use data as a way of
predicting strengths and weaknesses of opposition team’s, and look to use
this information to exploit them on the field of play (Carling et al., 2009). The
development of notational analysis ensures it now serves two purposes; it
continues to provide a conceptual basis for the coaching process whilst also
providing a useful practical tool for the analyst, coach and performer (James,
Mellalieu & Hollely, 2002). Given this, it is not surprising see soccer is a sport

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that has received considerable focus and attention from researchers in the
notational analysis literature (James, 2006).
One particular area of soccer that receives extensive attention within notation
and soccer literature is goal scoring, this is not surprising with it often being
the ultimate determining factor between successful and unsuccessful teams
(James, Jones, & Mellalieu, 2004). This attention has been placed upon goal
scoring as it is the most significant Key Performance Indicator (KPI) with
regards to match outcome, despite the fact it only contributes to a miniscule
percentage of playing time. Like all invasion sports goal or point scoring is
the factor that dictates the final result, however unlike many other sports, one
of soccer’s main characteristics is its low frequency of scoring (Yiannakos &
Armatas, 2006). Often a single goal is the deciding factor between victory
and defeat, because of its low frequency there is an added importance in
understanding goal scoring in greater detail.
Previous literature has helped develop a greater understanding in the area;
this has been achieved through the exploration of varying aspects with
regards to goal scoring, with some published work being used to theoretically
develop tactics in order to increase frequency of goal scoring. The publication
of results and findings give an indication into goal scoring patterns and in
many cases highlight good and bad practices which can then be used to
influence performance. This was the case with Reep & Benjamin’s (1968)
study on goal creation through build up play. Results published from the
study had an effect on some coaches in British football in the 1970’s and 80’s
by influencing them to adopt a more ‘direct style’ when deciding on tactics.
Because of this the area of research remained popular, with many groups of
practitioner’s using similar themes to conduct further research through
statistical analysis (e.g. Franks et al., 1983, 1990; Hughes et al., 1988;
Partridge & Franks, 1989; Grehaigne, 1999;) Findings from these studies
support those of Reep & Benjamin (1968) and have also been used to
support the development of tactics as a result.

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Although findings from these studies allow for a degree of appreciation with
regards to how goals are scored, they lead to only a partial understanding of
the science of the goal in soccer. An argument can be raised to the
relevance of this analysis, particularly when using results to implement or
alter tactics in the professional game. Previous research has been used as
the basis for recommendation with regards to playing style. However, a level
of prudence should be exercised when utilising it to adapt style of play.
These publicised studies (e.g. Franks et al., 1983, 1990; Hughes et al., 1988;
Partridge & Franks, 1989; Grehaigne, 1999) have looked into build up play in
order to try to understand the best way to utilise possession to increase
scoring frequency. It was not until Franks & Hughes (2005) that a paper was
published that looked into the area in more depth. Unlike research before
them, Hughes & Franks looked to normalise data to determine what build up
style would have a greater effect on scoring frequency. The study also looked
to compare unsuccessful teams shooting data to determine if there were
differences between the two.
The publication of previous literature on build up possession in relation to
goal scoring has allowed for a greater understanding into the frequency of
goals in relation to the length of their build up. Hughes & Franks (2005) have
also allowed for a development of knowledge in the area through normalising
data allowing for a greater clarity when analysing. However, where all
previous literature has looked to clarify how goals are scored in relation to
build up play they have failed to address where goals are scored from.
Development of tactics as a result of previous research would have benefited
greatly had there been an indication into where the ball should be played
rather than just how often. An understanding of how and where goals are
scored from could provide greater justification for a development of tactics,
which would be aided further by a comparison between successful and
unsuccessful teams.

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CHAPTER 2.0
Literature Review
2.1 Goal Scoring Notation
Since the beginnings of both hand and computerised systems (e.g., Reilly
and Thomas, 1976; Franks et al., 1983), through to the development of
contemporary software packages soccer is a sport that has received
considerable attention with regards to notation literature (James et al., 2002).
This popularity has spanned from a need for a greater understanding of the
sport and has seen papers published on almost all aspects of performance.
From studies on possession (e.g., Scoulding et al., 2004; Jones et al., 2004)
to shots and goals (e.g. Reep & Benjamin, 1968; Hughes & Franks, 2005)
researchers have analysed performance in order to understand good and
bad practises in greater detail. With the world of professional soccer being
dominated by a focus upon match outcomes (Scoulding, James & Taylor,
2004) it is understandable to see why there is such an emphasis with regards
to the goal. After all the goal is the ultimate determinant of match outcome
and in many ways defines the success of a team. Varying aspects of
analysing goals have received attention in literature, from frequency and time
(e.g., Yiannakos & Armatas, 2006; Armatas, Yiannakos & Sileloglou, 2007),
to an analysis advantageous elements (e.g. Pollard, 1986; Jacklin, 2005),
different components that make up a goal have been analysed to explore
whether trends or patterns emerge that produce information on why and
when goals are scored.
Armatas, Yiannakos & Sileloglou’s (2007) study on relationship of time and
goal scoring in soccer games is representative of a study that looks to
provide a greater understanding into when and why goals are scored.
Results concluded that teams scored significantly more goals in the second
half of matches throughout the 1998 and 2002 FIFA World Cups. The
increased frequency in goal scoring was put down to deterioration in the
physical condition of athletes as time progressed in performance. The results
of this study then allow for a detailed understanding as to when goals are

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scored and provide justification as to why. What this study fails to address
however is how those goals are being scored. Findings from this study
therefore provide little use or assistance to coaches and analysts in the
professional world. After all a coach cannot expect to gain success by setting
a team up to only score in the second half as it represents an increase in the
likelihood of a goal being scored. Research therefore should look to address
areas of goal scoring that have greater relevance in the professional game,
with an emphasis being placed on the development of a playing style or
tactics to enable increased scoring frequency.
2.2 Build-up play Notation
It is not only in the scoring of the goal itself but an analysis of the effort prior
to the goal that can be extremely valuable to coaches and researchers
(Garganta, Maia & Basto, 1997). An analysis of build-up play prior to a goal
allows for an understanding of how goals are scored, this has been used to
create or alter tactics by trying to implement areas of good practice from
published work and transferring it across to the professional environment
(e.g. Reep & Benjamin, 1968). Numerous studies have analysed the build-up
play in goals (e.g. Reep & Benjamin, 1968; Franks et al., 1983, 1990;
Hughes et al., 1988; Partridge & Franks, 1989; Grehaigne, 1999; Hughes &
Franks, 2005) and although there has been a level of continuity with regards
to findings, there remains a differing opinions when looking to implement
playing style as a result of published work.
Reep & Benjamin (1968) first looked into build up play with relation to effect
on goal scoring by analysing 3213 matches between 1953 and 1968
throughout British football. The study analysed data on goal scoring and
length of passing sequences in the build up to goals and presented passing
sequences as having a negative binomial distribution. What this meant was
that more goals were scored with 1 pass in their build-up than 2 and more
with 2 than 3 and so on. The pair went on to publicise two main findings from
the analysis: (1) Approximately 80% of goals resulted from a sequence of
three passes or less with 50% of goals scored generated from one pass or

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less (zero pass possessions include penalties and free kicks). (2) A goal is
scored every 10 shots. Findings from Reep & Benjamin (1968) have since
been reconfirmed in the sport through different publicised studies that have
analysed games at World Cup finals (Franks et al., 1983, 1990; Hughes et
al., 1988; Partridge & Franks, 1989; Grehaigne, 1999; Hughes & Franks,
2005). The fact that these findings have remained relatively stable throughout
a 50 year period in football gives a clear indication to the relevance of study
in the area. However, a further analysis is needed that can provide greater
detail into the level of success established with varying build up play
possession lengths. With Hughes & Franks (2005) concluding that although
the original work of Reep and Benjamin (1968) proved to be a key landmark
in football analysis, it led only to a partial understanding of the phenomenon
that was investigated.
Follow up studies in the area proved popular with numerous using a similar
model for analysis to determine results (e.g. Franks et al., 1983, 1990;
Hughes et al., 1988; Partridge & Franks, 1989; Grehaigne, 1999;) It was not
until Hughes & Franks (2005) however that a study looked to develop the
analysis used by Reep & Benjamin (1968). This was done by normalising
possession data which enabled an examination of different interpretations of
results. Data was normalised by removing the inequality of the data by
comparing the number of goals scored for each possession length and
rounding it to a common 1000 possessions. By doing this Hughes & Franks
(2005) looked to gain a greater understanding of possession sequences in
the build up to goals by determining the success rates based on even
frequencies. Hughes & Franks (2005) analysed goal scoring and build up
possession data throughout the 1990 & 1994 FIFA World Cup’s. In total 116
matches involving 56 teams were used throughout both tournaments. It was
concluded that the results from the study replicated that of Reep and
Benjamin’s (1968). Approximately 80% of goals occurred from possessions
containing 4 passes or less, with Hughes & Franks recognising that there
were more 0 pass possessions than 1 pass possessions and more 1 pass
possessions than 2 pass possessions and so on.

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Unlike in previous research Frank & Hughes (2005) not only normalised data
but compared successful and unsuccessful teams throughout both
tournaments. Teams were grouped by being deemed successful upon
making the quarterfinals of the tournament, being knocked out in any round
prior to this saw a team being placed in the unsuccessful group. When data
was normalised Hughes & Franks (2005) went on to find: (1) there were
significantly more shots per possession at longer passing sequences than
there were at shorter passing sequences for successful teams. (2)
Successful teams had a better ratio when converting shots to goals. By
employing a more patient style of play successful teams were providing
themselves with a greater chance of creating a shot on goal. What this
enable Hughes & Franks (2005) to conclude was that a team containing
athletes with a higher skill level would be more successful when creating
goals through controlling possession in the build up to attacks. This of
course, becomes dependant on the level of skill that any given team
possesses.
2.3 Tactical Development
Amongst the notational analysis literature examining soccer performance
there has been particular interest in the strategies and tactics adopted by
teams (Hughes & Franks, 2004). This interest spans from the need to
highlight areas of good practice in performance, with the common objective
of most studies being to determine the most effective way of playing the
game (Tenga, et al, 2009). Reep & Benjamin’s (1968) results were thought to
have influenced many researchers and football coaches throughout British
football (Hughes, 1987). Findings were used to evolve a simple tactical
approach to soccer, which was to maximise the ‘chance’ elements in favour
of their teams (Hughes & Franks, 2005). This was done by reverting to a
‘long ball’ or ‘direct’ style of football, which saw teams in possession looking
to move the ball into a goal scoring area as quickly as possible in a minimal
number as passes (Franks, 1996). The logic behind these tactics is that the
more times the ball enters goal scoring areas of the pitch the more chance

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there is of scoring which tends to encapsulate the finding of Reep (James,
2006).
However, whether an advocate of the direct game or possession game
numerous variables should be taken into consideration when analysing
findings from the study. Reep and Benjamin’s (1968) sample consisted of
3213 matches over a 15 year period, whilst creating a quantitative overview
of goal scoring in British football throughout the period; the study does not
give an indication as to the varying levels of success between specific teams.
Results then give us a clear indication into the style of play throughout
football at the time. There is however, no conclusive evidence from the study
that a more ‘direct style’ of play guaranteed success with regards to goal
scoring. The broad categorisation of direct and possession build up by Reep
& Benjamin (1968) only touches upon how teams utilise possession in the
build up to goal scoring. With 80% percentage of goals coming from 3 passes
or less results can be easily interpreted to assume a direct style of play will
increase the frequency of goals.
Nevertheless some teams were recorded as having some measure of
success by employing these strategies, but they were predominantly from the
lower divisions of English football (e.g. Wimbledon and Watford). Whilst at
international level Eire and Norway were regarded as over achievers through
adopting low passing sequences per possession. Very few teams however
have succeeded at the highest level by winning the World Cup or other
European championships using the tactic ‘direct play’ (Hughes & Franks,
2005). This has been supported by numerous studies that have analysed
possession as a way to determine level of performance. These studies
correlated possession percentages with match outcome in order to better
understand performance (e.g. Dawson, Dobson & Gerrard, 2000; Hughes,
2003; McGarry & Franks, 2003). With the reoccurring finding being the
correlation between the ability to retain possession of the ball from prolonged
periods of time and success (Bate, 1988; Gomez & Alvaro, 2002; James et
al. 2004).

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Hughes & Franks (2005) themselves went on to produce findings that
supported the argument that teams are likely to be more successful if they
implement a style of play that builds up an attack slowly. This was however
deemed to be dependent on the level of skill possessed by a team which
meant only a team possessing highly skilled players could successfully adopt
this tactic. This too can be used support the argument that teams who have
gone on to achieve the greatest level of success (e.g. winning the world cup)
have done so by being more patient in the build up to scoring a goal. By
comparing both successful and unsuccessful teams throughout the study
Hughes & Franks (2005) were able produce results that highlighted the
difference in goal scoring patterns between the two. Areas of good practice
can therefore be highlighted that determined the levels of success between
the two groups. By using the comparative results from this study a more
reliable foundation for the creation of tactical playing style would have been
able to take place in comparison with findings from Reep & Benjamin (1968).
Previous research in this area (e.g. Reep & Benjamin, 1968; Hughes &
Franks, 2005) has helped for a developed understanding of build-up play in
relation to goal scoring. This has been done by producing results that show
the level of success of possession lengths and the consequent number of
goals scored. However, it is important to realise that goal scoring is often a
result of a combination of factors, including technical (e.g. passing precision),
psychological (e.g. coping with stress), physical (e.g. endurance), social
(e.g.cooperation), and tactical (e.g. exploitation of imbalances in the
opponent’s defence) (Burwitz, 1997). As a result of this Tenga et al, (2010)
concluded that work from previous studies (Reep & Benjamin, 1968; Hughes
& Franks, 2005) may not be appropriate. This is because simply counting the
number of passes excludes other essential features in the analysis of these
styles of attack. What previous work fails to address is a greater detail of the
tactics implemented by teams in the build up to their attacks. An analysis of
not only how often teams pass the ball, but also where they pass it could
provide even greater depth into the analysis of build-up play in goal scoring.
This could be done through an analysis of assist information in the build up to
the scoring of a goal. Subsequent findings from a study of this nature could

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have the potential to provide a foundation for a more in depth generation of
tactics.
2.4 Aims and Objectives
With this considered this study looks to address the following aim and
objectives.
Aim:
 To provide a comparative analysis of the differences in the build-up
play of goals scored between successful and unsuccessful teams in
the English Premier League.
Objectives:
 To compare the number of build passes in the creation of a goal
between successful and unsuccessful teams.
 To analyse and compare where assist’s for goals come from between
successful and unsuccessful teams.
 To compare results from this study with those from previous
publications to determine whether they still remain relevant.

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CHAPTER 3.0
Methodology
3.1 Sample
This study analysed goal scoring data from matches between 10th December
2013 to the 9th February 2014 in the English Premier League (EPL), 85
games were analysed generating a sample of 176 goals. In total 116 goals
were scored for top 6 sides whilst bottom six sides scored 70 goals. These
dates were chosen as it represented a time frame that would allow for an
analysis of 10 games for every team involved in the study. This equated to
over a quarter (26.3%) of a season’s worth of games being analysed for
every team. This study looked to compare scoring data between successful
and unsuccessful teams, in order to do this 12 teams from the 20 in the EPL
were categorised into two groups: top 6 and bottom 6. Throughout the
season there is a constant shift in where teams lie in the table with in many
instances there being a rotation of positions on a weekly basis. As a result of
this teams were placed into categories based on their final season finishing
for the English Premier League 2012/2013 season.
Top 6 sides therefore consisted of: Manchester United, Manchester City,
Chelsea, Arsenal, Tottenham Hotspur and Everton. Bottom 6 sides were then
made up of the lowest three finishing sides that were not relegated from the
EPL (15th, 16, and 17th) and the three newly promoted sides from the
Football League Championship. These were: Newcastle United, Aston Villa,
Sunderland, Cardiff City, Hull City and Crystal Palace. Throughout the
duration of the study teams were not analysed replaying an already played
opposition. This was implemented following suggestions from Franks (1994)
that a player or team exhibits greater consistency in play when matched
against the same opponent rather than against different opposition.
3.2 Procedures
Data was collected from matches via a hand notation system post match
performance. Highlight footage from the BBC’s Match of the Day and Match

12
of the Day 2 programme was used to view scored goals. All goals were hand
notated within 24 hours of being broadcast this time frame was chosen as it
allowed easier access to extended footage on television or via online
streaming if it was required. All 176 goals were hand notated in a Microsoft
Excel document (appendix A) with information being collected on a group of
key performance indicators (KPI’s) that were taken for every goal. These
KPI’s were: the category of the team, this was determined on a team either
being in the top 6 or bottom 6. The number of passes in the build up to the
goal, this KPI had been used in previous research (e.g. Franks et al., 1983,
1990; Hughes et al., 1988; Partridge & Franks, 1989; Grehaigne, 1999;
Hughes & Franks, 2005). The zone from where the assist came from, this
was determined using a pitch grid template that can be seen in figure 1.0.
Figure 1.0 Pitch grid template used for the analysis of assist information in
the study.
Goals were then categorised into 5 groups depending on the information of
their build up passing sequences and assist information: Direct Central goals
were scored from passing sequences of 3 or less and were assisted from
central areas of the pitch. Direct Wide goals were scored from passing

13
sequences of 3 or less and were assisted from wide areas on the pitch.
Possession Central goals were scored from passing sequences of 4 or more
and were assisted from central areas of the pitch. Possession wide goals
were scored from passing sequences of 4 or more and were assisted from
wide areas of the pitch.
3.3 Data Analysis
With the use of the Statistical Package for the Social Sciences (SPSS), data
recorded on goal scoring information was processed. Build up play passing
sequences were analysed for normality of distribution using the Kolmogorov
– Smirnov test, with a non-significant result (Sig. value of more than 0.5)
indicating normality and a significant result (Sig value of less than 0.5)
suggests violation of the assumption of normality (Pallant, 2013).
The Mann – Whitney U test was chosen as the non-parametric to analyse the
differences in build-up play passing sequences between top 6 and bottom 6
sides following the results of the Kolmogorov – Smirnov test. The test also
allowed for the comparison of two groups that have different populations
which is necessary given the differing sample sizes between the groups. A
significant difference between the groups will be represented by p < 0.05
whilst a non-significant result will be p > 0.05 (Field, 2013).
Pearson’s Chi Squared test was chosen to determine whether there was a
relationship between the styles of build-up play, assist information and types
of goals scored between top 6 and bottom 6 sides. A significant difference in
the relationship between the two groups will be shown by p < 0.05 whilst a
non-significant relationship will be displayed by p > 0.05 (Field, 2013).
Descriptive statistics were also conducted across all areas on build-up play to
goals to provide frequencies and averages to compare information between
top 6 and bottom 6 sides.

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CHAPTER 4.0
Results
4.1 Overview of Build-up Passing Sequences
Build up play passing sequences D (176) = 0.127, p =.00 were significantly
non normal and as a consequence, a non-parametric test in the form of
Mann-Whitney U was used to analyse data from the top 6 and bottom 6
passing sequences.
Descriptive statistics report the mean for possession lengths at (n=176)
3.08±2.134, with a median of 3.0 and a range for data at 11.0. Figure 2.0
highlights the negative binomial distribution shown by passing sequences;
this reflects a gradual decline in goals as frequency of build-up possession
lengths increase.
Figure 2.0 Overview of build-up possession sequences and their frequency of
occurrence.

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4.2 A Comparison of Build-up Passes Sequences
Results from the Mann – Whitney U test show that passing sequences in top
6 sides (Mn = 97.87) differed significantly from those of the bottom 6 sides
(Mn = 70.38) in the build up to goal scoring, U = 2393, p = 0.001 in the
English Premier League.
Descriptive statistics report mean data for both groups as (n=116)
3.53±2.507 for top 6 sides and (n=60) 2.20±1.560 for bottom 6 sides. The
difference in mean data between the two groups allows for some degree of
understanding as to the differences in possession lengths in the build up to a
goal between the two. Figure 3.0 shows results comparing the two groups
and their passing sequence lengths in the build up to goals scored. Top 6
sides scored more goals in all build up possession lengths apart from when
goals were scored from just a single pass. It also shows an increase in goals
scored as a result of 5 or more passes from top 6 teams when comparing
them against scoring data from the bottom 6. Bottom 6 sides failed to score
any goals from a result of 7 passes or more whereas top 6 sides scored 14
goals from passing sequences of 7 up to 11.
Figure 3.0 A comparison of build-up possession lengths between top 6 and
bottom 6 sides.

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4.3 Possession Play vs Direct Play
Figure 4.0 shows the results for the percentage of goals scored as a result of
direct or possession build up play. Each category was determined on the
length of passing sequences prior to goal scoring; the direct category was
made up of goals scored in 3 passes or less, whilst the possession category
consisted of goals that were scored as a result of 4 passes or more. Results
show that a larger percentage of goals were scored as a result of a direct
build up (61.36%). Although the possession style of build-up represents a
lower figure (38.64%), it should be noted that goals that were scored from a
result of 0 passes have been grouped into direct data as it allows for a
comparison with previous studies that have been published in the area (e.g.
Reep & Benjamin, 1968 & Hughes & Franks, 2005).
Figure 4.0 Direct vs Possession build up play statistics.

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4.4 A Comparison of Build-up Play Playing Style
Results from Pearson’s Chi-Squared test for association x²(2) = 8.33, p=
0.016 show a significant difference in the relationship in the playing style
between top 6 and bottom 6 sides in the English Premier League.
The test also allowed for expected counts to be drawn for both groups across
the 3 playing styles. Top 6 sides actual count = 48 was considerably lower
than the expected count = 56.7 for goals scored via a direct method in build-
up, in comparison bottom 6 sides actual count = 38 was considerably higher
than the expected count = 29.3. In a reversal top 6 sides actual count = 53
for possession play was higher than the expected count = 44.8, whilst bottom
6 sides actual count = 15 was lower than the expected count = 23.2. Figure
5.0 displays results on the percentage of goals scored as result of direct or
possession build up play between top 6 and bottom 6 teams. Unlike in figure
3 goals that have been scored as a result of 0 build-up passes in length have
been recorded in a separate N/A category. Results show that top 6 sides
score more goals from longer possession sequences in their build up play
with 45.69% of all goals scored resulting in build-up play of 4 passes or more.
The goals they scored in a more direct style were marginally lower and
consisted of 41.38% of their total goals. The final 12.93% of goals scored by
top 6 sides were placed into a N/A category as they resulted in goals from
direct free kicks or penalties. In contrast bottom 6 sides scored a higher
63.33% of their goals through a direct style of build-up play with 3 passes or
less. The percentage of goals they scored through longer possession
sequences in the build up to goals was greatly reduced in comparison at
25.0%. However goals scored as a result of 0 build-up passes was only
slightly down on top 6 sides at 11.67%.

18
Figure 5.0 A comparison of Build-up possession categories between top 6
and bottom 6 sides.
4.5 Assist Results
Results from Pearson’s Chi-Squared test for association x²(5)=0.995,
p=0.963 show that there was not a significant difference in the relationship
between assist information for top 6 and bottom 6 sides in the English
Premier League.
The test was also used to determine expected counts to compare with actual
frequency for assist area. Count and expected count had very little variance
with the largest being 1.8 which was recorded from assist from a central area
in the penalty box. Figure 6.0 and 7.0 shows assist area percentages for top
6 and bottom 6 sides respectively. The results in figure 6.0 show that over a
third of the top 6 goals come from an assist that is in line with the penalty
area and comes from a wide position. Almost all goals are assisted from

19
34%
15%14%
22%
3%
0% 12%
Penalty Area Wide
Penalty Area Central
Attacking Half Wide
Attacking Half Central
Defensive Half Wide
Defensive Half Central
N/A
35%
12%
13%
23%
5%
0% 12%
Penalty Area Wide
Penalty Area Central
Attacking Half Wide
Attacking Half Central
Defensive Half Wide
Defensive Half Central
N/A
areas in the attacking half of the pitch with only 3% of assists coming from
the defensive half. Assist from inside the defensive half in central areas
represents the only area on the pitch that no assists for goals were
registered. Similarly figure 7.0 shows over a third (35%) of goals for bottom 6
sides were scored from an assist from a wide area in line with the penalty
box. In fact both figure 6.0 and figure 7.0 show almost identical assist
patterns between both groups, with no assist again coming from central
areas in the defensive half. Both of these figures allow for a clearer insight in
to the areas of the pitch that both groups of teams are working the ball in to
prior to a goal being scored.
Figure 6.0 Assist area percentages for top 6 sides.
Figure 7.0 Assist area percentages for bottom 6 sides.

20
4.6 Comparative Results for Type of Goals Scored
Results from Pearson’s Chi-Squared test for association x²(2) = 8.446, p =
0.77 show there was not a significant difference in the relationship for the
type of goals scored between top 6 and bottom 6 sides in the English
Premier League.
Top 6 sides were however recorded as scoring more goals than expected
that were built up through longer sequences in possession. In contrast to this
bottom 6 sides scored more than expected through goals that came from a
direct nature. Figure 8.0 shows the frequency of the type of goals scored for
both top 6 and bottom 6 sides. Groups were categorised on passing
sequence length and assist area. Goals from wide proved to be most popular
amongst both groups of teams with possession wide goals (34) proving the
most regular type of goal for top 6 sides, whilst direct wide (25) were the
most frequent for bottom 6. Direct goals from a wide position proved to be the
most popular between both groups with a total of 54 goals being scored in
this manner. Aside from goals from direct free kicks and penalties (N/A)
goals from central position that were created through a possession based
build up represented the lowest proportion of goals scored at 25 in total. This
type of goal also registered the lowest count scored from either group with
bottom 6 sides only scoring 6 types of this goal which equated to 10% of all
their goals scored. Top 6 sides scored more types of every goal in
comparison to bottom 6 sides which is not overly surprising when
consideration is given to the fact that they scored almost double the amount
of goals as bottom 6 sides.

21
Figure 8.0 A comparison of types of goals scored between top 6 and bottom
6 sides.

22
CHAPTER 5.0
Discussion
5.1 Results
The aim of this study was to discover whether there was a difference in the
build-up in goals scored between top 6 and bottom 6 sides in the English
Premier League. Conclusively, results showed there was a significant
difference in the build-up play passing sequences between the two groups.
Whilst there was not a significant difference in the association in assist
information and the type of goals scored between the two.
5.2 Build-up Play Passing Sequences
An overview on both groups passing sequences in the build up to goals
supports findings from Reep & Benjamin (1968) that goals are scored
predominately from passing sequences of 3 passes or less (61.36%).
However, by removing goals which were subsequently scored from 0 passes
in there build up (penalties, direct free kicks); an analysis was able to take
place on solely goals scored as a result of team possession. Results indicate
that in fact less than half (48.9%) of goals are scored as a result of a direct
style of build-up. These contrasting findings further support the views of
Hughes & Franks (2005) that although the original work of Reep and
Benjamin (1968) proved to be a key landmark in football analysis, it led only
to a partial understanding of the phenomenon that was investigated.
Results show a significant difference p<0.5 between the two groups build up
play passing sequences. Top 6 sides had a higher percentage of goals
scored as a result of longer passing sequences in their build up. In
comparison bottom 6 sides favoured scoring goals through a more direct
method in build-up. This further supported evidence from Hughes & Franks
(2005) that successful teams were both more efficient in converting lengthier
possession sequences into shots, as well as being more efficient with
regards to scoring in comparison with unsuccessful teams. Consideration
should however be given to the level of skill that is possessed by the
athletes, with both Jones et al., (2004) and Hughes & Franks (2005)

23
concluding that the level of skill that players possess can influences how
teams utilise possession. They go on to suggest that this can considerably
influence the way in which teams use the ball and it is not therefore
necessarily indicative of strategy differences between both groups of teams.
Results for build-up play passing sequences can be further validated by
numerous studies that have analysed the level of success of teams and their
subsequent possession statistics. Hook & Hughes (2001) and Lago-Penas &
Dellal (2010) both suggested there is a direct link between the ability to retain
possession of the ball for prolonged periods and success. Whilst results from
this study can be used to reconfirm findings from Jones et al., (2004), which
was that successful teams in the English Premier League enjoy a larger
percentage of longer passing lengths in comparison to those of unsuccessful
teams.
5.3 Assist and Type of goal
Results show that there was not a significance in the level of association
(p>0.5) in the area of assists and type of goal scored between top 6 and
bottom 6 sides. Previous work has grouped goals into two major categories
(direct and counterattack or possession and elaborate style of play), with the
classification of each goal being determined on the subsequent build up play
passing lengths (e.g. Reep & Benjamin, 1968; Hughes & Franks, 2005;
Tenga et al., 2010). These classifications have been used to summarise how
goals are being scored with there being limited publicised literature on where
goals are being scored from. However, Jinshan et al., (1993) analysis of
goals of the 13th and 14th world cup allow for a degree of understanding as to
where goals were being created from for all teams that participated in these
tournaments. Results show goals created from wide positions equated to
36.9% and 32.1% for tournaments respectively, in comparison to goals
created through central penetration 21.2% & 18.3%. Results from this study
further validate the findings of Jinshan et al., (1993) that goals are more
frequently created from wide areas of the pitch.

24
5.4 Tactical Implementation
Results from this study further validate findings from (Hughes & Franks,
2005) that teams who experience more success have a better ratio of goals
that are created through longer passing sequences. Supported further with
conclusions from Hook & Hughes (2001) and Lago-Penas & Dellal (2010)
that teams achieve greater levels of success by controlling possession, it
would be fair to suggest that implementing tactics that look to retain
possession of the ball could improve goal scoring form.
This can be further supported knowing that there is now an emphasis placed
upon shots as being the key performance indicator that constitutes the key to
success in today’s soccer (Casamichana et al., 2013). Hughes & Bartlett
(2002) concluded that there is a direct relationship between attempts on goal
and resulting goals scored. Results from numerous studies (e.g. Hughes &
Churchill, 2004; Hughes & Franks, 2005; Hughes & Snook, 2006; Tenga et
al., 2010) support the findings that longer passing sequences are more
effective than short ones when creating shooting opportunities. This allows
for a conclusion that implementing a style of play that builds up possession
will have a more successful ratio with regards to creating shooting
opportunities and therefore goals.
Results from this study show that goals are predominantly scored from wide
areas in the English Premier League. It would therefore be suggested that
implementing tactics that encourage wide play could increase goal scoring
rates. There is however limited work publicised in this area and although
previous work (Jinshan et al., 1993) could be used to further support this
argument, consideration should be given to the lack of findings, with more
work needed to provide better validity on subsequent tactical advice.
One possible area that could perhaps increase goal scoring through greater
levels of tactical consideration is set plays. Results from this study show
goals from direct set plays equated to 11.93%. Given that these results only
consider goals scored from direct set plays and not all set plays and their
subsequent build up would suggest that the set play offers an important role
in the final outcome of a game. Bangsbo and Peitersen (2000) point out the

25
magnitude of the dead-ball situations in modern football and report that 20
are estimated to appear, in average, for each team in every match. They also
site three other studies concerning the 1990 and 1994 World Cups and the
1996 European Championship, reporting that the goal scoring patterns in
these tournaments was 32%, 25% and 27% respectively, pointing out the
proposition that the percentage of goals scored after set plays makes up the
1/3 of the total number of goals scored, irrespective of the tournament (Saltas
and Ladis, 1992; Jishan et al., 1993; Pappas, 2002; Bekris et al., 2005).
These figures indicate the level of importance that set plays can have with
regards to goals being scored. Tactical implementation through rigorous work
on the training ground could increase goal scoring frequency for both groups
of teams, and would therefore be suggested.
As a conclusion consideration should be given before implementing or
generating tactics as a result of findings from publicised studies. Tactical
advice created as a result of Reep & Benjamin (1968) findings which were
supported by Bate (1988) have since been contradicted by numerous studies
that suggest retaining possession increases likelihood of success with
regards to match outcome (e.g. Hook & Hughes, 2001; Jones et al., 2004;
Hughes & Franks, 2005, Lago-Penas & Dellal, 2010). Coaches should realise
that goal scoring is often a result of a combination of factors, including
technical (e.g. passing precision), psychological (e.g. coping with stress),
physical (e.g. endurance), social (e.g. cooperation), and tactical (e.g.
exploitation of imbalances in the opponent’s defence) (Burwitz, 1997). They
should therefore consider numerous areas of performance when looking to
improve goal scoring frequency and match outcome for their team.
5.5 Implications
One pertinent issue regarding notational analysis studies is the sample
selected for analysis. It is usual to select matches on the basis of some
common theme (e.g. World Cup, a domestic league or one particular team).
This is sensible with respect to providing summary statistics and assessing
trends for a team or teams but care should be taken not to assume that the
findings are necessarily relevant for future matches (James, 2004). Previous

26
research in the area has used similar sample sizes but focused on a wider
spread of teams. European Championships and FIFA World Cups have
proved popular when analysing goal scoring and build up play data
historically (e.g. Franks et al., 1983, 1990; Hughes et al., 1988; Partridge &
Franks, 1989; Grehaigne, 1999; Hughes & Franks, 2005) which often allows
for a sample size of between 52 and 64 matches involving between 24 and
32 teams.
It is important to consider analysing a sample of matches that can provide
enough information on performance for it to be considered representative of
a typical performance. Hughes, Evans and Wells (2001) suggest the
establishment of performance profiles concluding that a reasonable number
of matches for this to be considered in football are 6. However, this is
dependent on typical variability of the performance between matches and
consideration should be given to opposition faced. Franks (1994) suggested
that a player exhibit greater consistency in play when matched against the
same opponent rather than against different opposition.
An argument could be raised on the classification of top and bottom 6 sides
used for the study. Teams were classified on 2012/2013 final EPL standings
with the exception of newly promoted teams who were added to the bottom 6
group. With it being impossible to predict a season’s outcome in the EPL
there can be no guarantee on the level of success experienced by each team
in the study. However an analysis of the previous EPL 5 seasons final
standings show that there is a level of stability with regards to top 8 standings
in the league. Not once did one of top 6 sides analysed fail to finish in the top
half of the table with Manchester City in 2008/2009 season representing the
only side to not finish in the top 8. Whilst on average 1 newly promoted side
suffered relegation in the first season of being in the league with 1 other
consistently finishing in the bottom 6.
5.6 Future Research
Future research in the area should look to extend work by analysing greater
quantities of data, whilst also establishing the amount of data that is acquired

27
to establish sufficient performance profiles. The method proposed by Hughes
et al., (2001) determines how many matches should be observed for a given
subject for the mean of a performance indicator for that subject to stabilise. It
has a number of strengths firstly; it reduces variability due to individual match
effects by basing performance indicators on multiple match data. Secondly, it
provides a systematic means of determining the number of matches required
to produce a stable value for a performance indicator (O’Donoghue, 2005).
This would be beneficial as it would allow for an analysis of more reliable
data whilst providing a valid mean for build-up play passing sequences to
compare against. This may be easier to implement than expected with some
recent work demonstrating that shooting and goal scoring data stabilise with
fewer matches than expected (Hughes et al., 2001, 2003).
Further research is also required to allow for a greater understanding into
areas of goal scoring that has received little attention. In particular an
emphasis should be placed upon the analysis of where goals are being
created from; this could be done through an analysis of zonal information
with subsequent assist area being notated. Previous work in the area
(Jinshan et al., 1993) only allows for a degree of understanding as how goals
are being scored through subsequent build up information. Tenga et al.,
(2010) also suggested one aspect of goal scoring that should be explored
further is the sequential analysis of playing tactics. A greater understanding
of how and where teams utilise possession in the build up to goals being
scored will allow for a more reliable foundation for the creation of tactical
advice.
CHAPTER 6.0

28
Conclusion
In conclusion, the current data indicates statistically there is a significant
difference between the passing sequences in the build up to goals scored
between top 6 and bottom 6 sides in the English Premier League (p<.05).
Results from this study support previous findings that successful sides (top
six) favour lengthier passing sequences in the build up to goals. In
comparison unsuccessful teams (bottom six) prefer more direct passing
sequences in the build up to the goals they score. This could be explained by
the level of skill acquired by both groups with top 6 side’s players having a
greater ability to retain possession of the ball. However, data relating to
assist information and type of goals scored from both groups of teams show
no significance in difference (p>.05). The areas of assists were almost
identical between the two groups indicating a further analysis of the tactics
that teams use to improve goal scoring frequency is necessary. This should
be carried out with an emphasis being placed upon zonal information of
passing sequences to allow for a better understanding into how teams utilise
possession in the build up to goals. Work should also be further extended by
establishing performance profiles for teams and their goal scoring data. This
would provide a more reliable dataset for analysis to be undertaken.

29
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35
CHAPTER 8.0
Appendices
Appendix A: Raw Goal Scoring Data
GOAL FOR
NO.
PASSES
ZONE
FROM
OWN
GOAL
Type of
Goal
Goal 1 A 1 A1 DW
Goal 2 A 6 C1 PW
Goal 3 A 0 N/A N/A
Goal 4 A 0 D2 DC
Goal 5 A 3 B2 Yes DC
Goal 6 A 4 C3 DW
Goal 7 A 7 C2 PC
Goal 8 A 5 D2 PC
Goal 9 A 2 H1 DW
Goal 10 A 1 D3 DW
Goal 11 A 1 A1 DW
Goal 12 A 2 B2 DC
Goal 13 A 5 C2 PC
Goal 14 A 1 A1 DW
Goal 15 A 0 N/A N/A
Goal 16 A 3 D2 DC
Goal 17 A 2 B3 DW
Goal 18 A 0 N/A N/A
Goal 19 A 0 N/A N/A
Goal 20 A 5 D1 PW
Goal 21 A 4 D3 DW
Goal 22 A 3 B3 DW
Goal 23 A 7 B2 PC
Goal 24 A 3 C2 DW
Goal 25 A 1 A1 DW
Goal 26 A 5 B1 PW
Goal 27 A 0 N/A N/A
Goal 28 A 1 D2 DC
Goal 29 A 3 D2 DC
Goal 30 A 0 N/A N/A
Goal 31 A 0 N/A N/A
Goal 32 A 5 D2 PC
Goal 33 A 5 B3 PW
Goal 34 A 4 B1 PW
Goal 35 A 4 C1 PW
Goal 36 A 3 D1 DW

36
Goal 37 A 2 B1 DW
Goal 38 A 3 E12 DC
Goal 39 A 4 B1 DW
Goal 40 A 3 C3 Yes DW
Goal 41 A 5 B3 PW
Goal 42 A 9 A3 PW
Goal 43 A 6 C1 PW
Goal 44 A 0 N/A N/A
Goal 45 A 9 B3 PW
Goal 46 A 9 D1 PW
Goal 47 A 1 A3 DW
Goal 48 A 5 D2 PC
Goal 49 A 3 B2 DW
Goal 50 A 5 B1 PW
Goal 51 A 4 D2 DC
Goal 52 A 1 D2 DC
Goal 53 A 3 B1 DW
Goal 54 A 2 A1 DW
Goal 55 A 4 E11 DW
Goal 56 A 4 B2 DC
Goal 57 A 3 B3 DW
Goal 58 A 2 D2 DC
Goal 59 A 2 B2 DC
Goal 60 A 2 B1 DW
Goal 61 A 4 A3 DW
Goal 62 A 0 N/A N/A
Goal 63 A 3 D2 DC
Goal 64 A 5 B3 PW
Goal 65 A 3 B2 DC
Goal 66 A 4 C3 DW
Goal 67 A 0 N/A N/A
Goal 68 A 3 D2 DC
Goal 69 A 5 C1 PW
Goal 70 A 3 D1 DW
Goal 71 A 3 B1 DW
Goal 72 A 4 A2 DC
Goal 73 A 3 E21 DW
Goal 74 A 4 D1 DW
Goal 75 A 2 E21 DW
Goal 76 A 11 B3 PW
Goal 77 A 5 D2 PC
Goal 78 A 4 D1 DW
Goal 79 A 0 N/A N/A
Goal 80 A 4 D2 DC

37
Goal 81 A 2 D2 DC
Goal 82 A 9 B1 PW
Goal 83 A 3 E21 DW
Goal 84 A 5 C2 PC
Goal 85 A 1 D3 DW
Goal 86 A 2 A1 DW
Goal 87 A 4 D2 DC
Goal 88 A 3 D2 DC
Goal 89 A 7 E12 PC
Goal 90 A 8 B1 PW
Goal 91 A 5 D1 PW
Goal 92 A 0 N/A N/A
Goal 93 A 3 B3 DW
Goal 94 A 2 B2 DC
Goal 95 A 8 B3 PW
Goal 96 A 7 D3 PW
Goal 97 A 9 B3 yes PW
Goal 98 A 6 D1 PW
Goal 99 A 2 D2 DC
Goal 100 A 6 D3 PW
Goal 101 A 0 N/A N/A
Goal 102 A 6 B2 PC
Goal 103 A 1 A3 DW
Goal 104 A 4 E11 DW
Goal 105 A 2 C2 DC
Goal 106 A 5 B3 PW
Goal 107 A 3 C3 DW
Goal 108 A 6 B2 PC
Goal 109 A 3 E11 DW
Goal 110 A 1 D2 DC
Goal 111 A 10 D2 PC
Goal 112 A 0 N/A N/A
Goal 113 A 3 D2 DC
Goal 114 A 7 D2 PC
Goal 115 A 4 D2 DC
Goal 116 A 6 B2 PC
Goal 117 B 1 E12 DC
Goal 118 B 1 E21 DW
Goal 119 B 1 D2 DC
Goal 120 B 0 N/A N/A
Goal 121 B 3 A3 DW
Goal 122 B 4 E12 DC
Goal 123 B 1 D1 DW
Goal 124 B 4 A1 DW

38
Goal 125 B 1 A1 DW
Goal 126 B 4 B2 DC
Goal 127 B 4 C2 DC
Goal 129 B 1 A1 DW
Goal 130 B 5 D2 PC
Goal 131 B 3 D3 DW
Goal 132 B 5 B2 PC
Goal 133 B 2 B2 DC
Goal 134 B 1 D2 DC
Goal 135 B 1 A3 DW
Goal 136 B 2 B2 DC
Goal 137 B 1 F1 DW
Goal 138 B 1 D3 DW
Goal 139 B 2 E23 DW
Goal 140 B 2 C3 DW
Goal 141 B 2 A3 DW
Goal 142 B 1 A3 DW
Goal 143 B 5 D1 DW
Goal 144 B 4 C3 DW
Goal 145 B 1 D3 DW
Goal 146 B 3 C1 DW
Goal 147 B 2 C1 DW
Goal 148 B 3 D3 DW
Goal 150 B 1 A3 DW
Goal 151 B 4 D2 DC
Goal 153 B 1 D3 DW
Goal 154 B 3 C3 DW
Goal 155 B 1 A1 DW
Goal 156 B 3 A1 DW
Goal 157 B 6 A3 PW
Goal 158 B 1 D1 DW
Goal 159 B 5 B3 PW
Goal 161 B 2 D2 DC
Goal 162 B 4 D2 DC
Goal 163 B 4 B3 DW
Goal 164 B 2 D2 DC
Goal 165 B 4 B3 DW
Goal 166 B 2 D2 DC
Goal 168 B 4 D2 DC

39
Goal 169 B 3 B3 DW
Goal 171 B 3 C1 DW
Goal 172 B 1 C2 DC
Goal 173 B 1 D2 DC
Goal 174 B 2 A3 DC
Goal 175 B 2 D2 DC
Goal 176 B 2 E12 DC
Appendix B: SPSS Output for Kolmogorov – Smirnov test for Normality
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Number ofbuild-up passes 176 100.0% 0 0.0% 176 100.0%
Descriptives
Statistic Std. Error
Number ofbuild-up passes
Mean 3.08 .174
95% Confidence Interval for
Mean
Lower Bound 2.74
Upper Bound 3.42
5% Trimmed Mean 2.91
Median 3.00
Variance 5.354
Std. Deviation 2.314
Minimum 0
Maximum 11
Range 11
Interquartile Range 3
Skewness .851 .183
Kurtosis .691 .364

40
Tests of Normality
Kolmogorov-Smirnova
Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Number ofbuild-up passes .127 176 .000 .927 176 .000
a. Lilliefors Significance Correction
Appendix C: SPSS Output for Mann – Whitney U test
Ranks
Top 6 (1) Bottom 6 (2) N Mean Rank Sum of Ranks
Number ofbuild-up passes
Top 6 116 97.87 11353.00
Bottom 6 60 70.38 4223.00
Total 176
Test Statisticsa
Number of
build-up passes
Mann-Whitney U 2393.000
Wilcoxon W 4223.000
Z -3.427
Asymp. Sig. (2-tailed) .001
a. Grouping Variable: Top 6 (1) Bottom 6
(2)

41
Appendix D: SPSS Output for Pearson’s Chi-Squared test (Build up playing
style)
Build up Possession Category * Top 6 (1) Bottom 6 (2) Crosstabulation
Top 6 (1) Bottom 6 (2) Total
Top 6 Bottom 6
Build up Possession
Category
N/A
Count 15 7 22
Expected Count 14.5 7.5 22.0
Direct
Count 48 38 86
Possession
Count 53 15 68
Total
Count 116 60 176
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 8.333a
2 .016
Likelihood Ratio 8.518 2 .014
Linear-by-Linear
Association
3.349 1 .067
N of Valid Cases 176
a. 0 cells (.0%) have expected count less than 5. The minimum
expected count is 7.50.

42
Appendix E: SPSS Output for Pearson’s Chi-Squared test (Assist Area)
Area of Assist * Top 6 (1) Bottom 6 (2) Crosstabulation
Top 6 Bottom 6
Area of Assist
Penalty area wide
Count 40 22 62
Penalty area central
Count 17 6 23
Attacking halfwide
Count 16 9 25
Attacking halfcentral
Count 25 13 38
Defensive half wide
Count 4 3 7
N/A
Count 14 7 21
Total
Count 116 60 176
Chi-Square Tests
sided)
Pearson Chi-Square .995a
5 .963
Linear-by-Linear
Association
.004 1 .951
a. 2 cells (16.7%) have expected countless than 5. The minimum

43
Appendix F: SPSS Output for Pearson’s Chi-Squared test (Type of Goal)
Type of Goal * Top 6 (1) Bottom 6 (2) Crosstabulation
Top 6 Bottom 6
Type of Goal
Direct Central
Count 20 13 33
Direct Wide
Count 29 25 54
Possession Central
Count 19 6 25
Possession Wide
Count 34 9 43
N/A
Count 14 7 21
Total
Count 116 60 176
Chi-Square Tests
sided)
Pearson Chi-Square 8.446a 4 .077
Linear-by-Linear
Association
3.733 1 .053
a. 0 cells (.0%) have expected count less than 5. The minimum

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