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

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

ii

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

1

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.

6

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).

8

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

9

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.

10

CHAPTER 3.0

Methodology

3.1 Sample

This study analysed goal scoring data from matches between 10 th 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

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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 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

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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.

13

CHAPTER 4.0Results

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.

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

14

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.

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.

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.

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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%.

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.

16

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

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.

17

34%

15%14%

22%

3%

12%

Penalty Area WidePenalty Area CentralAttacking Half WideAttacking Half CentralDefensive Half WideDefensive Half CentralN/A

Figure 7.0

Assist area percentages for bottom 6 sides.

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

18

35%

12%13%

23%

5%

12%

Penalty Area WidePenalty Area CentralAttacking Half WideAttacking Half CentralDefensive Half WideDefensive Half CentralN/A

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.

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

6 sides.

19

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

20

(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)

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

21

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.

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.

22

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

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

23

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

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

24

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

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.

25

CHAPTER 6.0

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.

26

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32

CHAPTER 8.0

Appendices

Appendix A: Raw Goal Scoring Data

GOAL FORNO.

PASSESZONE FROM

OWN GOAL

Type of Goal

Goal 1 A 1 A1 DWGoal 2 A 6 C1 PWGoal 3 A 0 N/A N/AGoal 4 A 0 D2 DCGoal 5 A 3 B2 Yes DCGoal 6 A 4 C3 DWGoal 7 A 7 C2 PCGoal 8 A 5 D2 PCGoal 9 A 2 H1 DWGoal 10 A 1 D3 DWGoal 11 A 1 A1 DWGoal 12 A 2 B2 DCGoal 13 A 5 C2 PCGoal 14 A 1 A1 DWGoal 15 A 0 N/A N/AGoal 16 A 3 D2 DCGoal 17 A 2 B3 DWGoal 18 A 0 N/A N/AGoal 19 A 0 N/A N/AGoal 20 A 5 D1 PWGoal 21 A 4 D3 DWGoal 22 A 3 B3 DWGoal 23 A 7 B2 PCGoal 24 A 3 C2 DWGoal 25 A 1 A1 DWGoal 26 A 5 B1 PWGoal 27 A 0 N/A N/AGoal 28 A 1 D2 DCGoal 29 A 3 D2 DC

33

Goal 30 A 0 N/A N/AGoal 31 A 0 N/A N/AGoal 32 A 5 D2 PCGoal 33 A 5 B3 PWGoal 34 A 4 B1 PWGoal 35 A 4 C1 PWGoal 36 A 3 D1 DWGoal 37 A 2 B1 DWGoal 38 A 3 E12 DCGoal 39 A 4 B1 DWGoal 40 A 3 C3 Yes DWGoal 41 A 5 B3 PWGoal 42 A 9 A3 PWGoal 43 A 6 C1 PWGoal 44 A 0 N/A N/AGoal 45 A 9 B3 PWGoal 46 A 9 D1 PWGoal 47 A 1 A3 DWGoal 48 A 5 D2 PCGoal 49 A 3 B2 DWGoal 50 A 5 B1 PWGoal 51 A 4 D2 DCGoal 52 A 1 D2 DCGoal 53 A 3 B1 DWGoal 54 A 2 A1 DWGoal 55 A 4 E11 DWGoal 56 A 4 B2 DCGoal 57 A 3 B3 DWGoal 58 A 2 D2 DCGoal 59 A 2 B2 DCGoal 60 A 2 B1 DWGoal 61 A 4 A3 DWGoal 62 A 0 N/A N/AGoal 63 A 3 D2 DCGoal 64 A 5 B3 PWGoal 65 A 3 B2 DCGoal 66 A 4 C3 DWGoal 67 A 0 N/A N/AGoal 68 A 3 D2 DCGoal 69 A 5 C1 PWGoal 70 A 3 D1 DWGoal 71 A 3 B1 DWGoal 72 A 4 A2 DCGoal 73 A 3 E21 DW

34

Goal 74 A 4 D1 DWGoal 75 A 2 E21 DWGoal 76 A 11 B3 PWGoal 77 A 5 D2 PCGoal 78 A 4 D1 DWGoal 79 A 0 N/A N/AGoal 80 A 4 D2 DCGoal 81 A 2 D2 DCGoal 82 A 9 B1 PWGoal 83 A 3 E21 DWGoal 84 A 5 C2 PCGoal 85 A 1 D3 DWGoal 86 A 2 A1 DWGoal 87 A 4 D2 DCGoal 88 A 3 D2 DCGoal 89 A 7 E12 PCGoal 90 A 8 B1 PWGoal 91 A 5 D1 PWGoal 92 A 0 N/A N/AGoal 93 A 3 B3 DWGoal 94 A 2 B2 DCGoal 95 A 8 B3 PWGoal 96 A 7 D3 PWGoal 97 A 9 B3 yes PWGoal 98 A 6 D1 PWGoal 99 A 2 D2 DCGoal 100 A 6 D3 PWGoal 101 A 0 N/A N/AGoal 102 A 6 B2 PCGoal 103 A 1 A3 DWGoal 104 A 4 E11 DWGoal 105 A 2 C2 DCGoal 106 A 5 B3 PWGoal 107 A 3 C3 DWGoal 108 A 6 B2 PCGoal 109 A 3 E11 DWGoal 110 A 1 D2 DCGoal 111 A 10 D2 PCGoal 112 A 0 N/A N/AGoal 113 A 3 D2 DCGoal 114 A 7 D2 PCGoal 115 A 4 D2 DCGoal 116 A 6 B2 PCGoal 117 B 1 E12 DC

35

Goal 118 B 1 E21 DWGoal 119 B 1 D2 DCGoal 120 B 0 N/A N/AGoal 121 B 3 A3 DWGoal 122 B 4 E12 DCGoal 123 B 1 D1 DWGoal 124 B 4 A1 DWGoal 125 B 1 A1 DWGoal 126 B 4 B2 DCGoal 127 B 4 C2 DCGoal 128 B 0 N/A N/AGoal 129 B 1 A1 DWGoal 130 B 5 D2 PCGoal 131 B 3 D3 DWGoal 132 B 5 B2 PCGoal 133 B 2 B2 DCGoal 134 B 1 D2 DCGoal 135 B 1 A3 DWGoal 136 B 2 B2 DCGoal 137 B 1 F1 DWGoal 138 B 1 D3 DWGoal 139 B 2 E23 DWGoal 140 B 2 C3 DWGoal 141 B 2 A3 DWGoal 142 B 1 A3 DWGoal 143 B 5 D1 DWGoal 144 B 4 C3 DWGoal 145 B 1 D3 DWGoal 146 B 3 C1 DWGoal 147 B 2 C1 DWGoal 148 B 3 D3 DWGoal 149 B 0 N/A N/AGoal 150 B 1 A3 DWGoal 151 B 4 D2 DCGoal 152 B 0 N/A N/AGoal 153 B 1 D3 DWGoal 154 B 3 C3 DWGoal 155 B 1 A1 DWGoal 156 B 3 A1 DWGoal 157 B 6 A3 PWGoal 158 B 1 D1 DWGoal 159 B 5 B3 PWGoal 160 B 0 N/A N/AGoal 161 B 2 D2 DC

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Goal 162 B 4 D2 DCGoal 163 B 4 B3 DWGoal 164 B 2 D2 DCGoal 165 B 4 B3 DWGoal 166 B 2 D2 DCGoal 167 B 0 N/A N/AGoal 168 B 4 D2 DCGoal 169 B 3 B3 DWGoal 170 B 0 N/A N/AGoal 171 B 3 C1 DWGoal 172 B 1 C2 DCGoal 173 B 1 D2 DCGoal 174 B 2 A3 DCGoal 175 B 2 D2 DCGoal 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 of build-up passes 176 100.0% 0 0.0% 176 100.0%

Descriptives

Statistic Std. Error

Number of build-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

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Skewness .851 .183

Kurtosis .691 .364

Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

Number of build-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 of build-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)

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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/ACount 15 7 22

Expected Count 14.5 7.5 22.0

DirectCount 48 38 86


PossessionCount 53 15 68


TotalCount 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.

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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 wideCount 40 22 62


Penalty area centralCount 17 6 23


Attacking half wideCount 16 9 25


Attacking half centralCount 25 13 38


Defensive half wideCount 4 3 7


N/ACount 14 7 21




Chi-Square Tests


sided)

Pearson Chi-Square .995a 5 .963


Linear-by-Linear Association .004 1 .951


a. 2 cells (16.7%) have expected count less than 5. The minimum


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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 CentralCount 20 13 33


Direct WideCount 29 25 54


Possession CentralCount 19 6 25


Possession WideCount 34 9 43


N/ACount 14 7 21




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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