CARDIFF METROPOLITAN UNIVERSITY Prifysgol Fetropolitan Caerdydd CARDIFF SCHOOL OF SPORT DEGREE OF BACHELOR OF SCIENCE (HONOURS)

Transcription

1 CARDIFF METROPOLITAN UNIVERSITY Prifysgol Fetropolitan Caerdydd CARDIFF SCHOOL OF SPORT DEGREE OF BACHELOR OF SCIENCE (HONOURS) SPORT PERFORMANCE ANALYSIS A tactical comparison of attacking build-up play between domestic and international football. (Dissertation submitted under the Sports Performance Analysis area) Luke Brick

2 A tactical comparison of attacking build-up play between domestic and international football.

3 Cardiff Metropolitan University Prifysgol Fetropolitan Caerdydd Certificate of student By submitting this document, I certify that the whole of this work is the result of my individual effort, that all quotations from books and journals have been acknowledged, and that the word count given below is a true and accurate record of the words contained (omitting contents pages, acknowledgements, indices, tables, figures, plates, reference list and appendices). I further certify that the work was either deemed to not need ethical approval or was entirely within the ethical approval granted under the code entered below. Ethical approval code: 15/5/40U (enter code or 'exempt') Word count: Name: Luke Brick Date: 09/ Certificate of Dissertation Supervisor responsible I am satisfied that this work is the result of the student s own effort and was either deemed to not need ethical approval (as indicated by 'exempt' above) or was entirely within the ethical approval granted under the code entered above. I have received dissertation verification information from this student. Name: Date: Notes: The University owns the right to reprint all or part of this document.

4 Contents List of Tables... i List of Figures... ii List of Appendices... iii Abstract... iv 1.0 Introduction Association Football Performance analysis in football Domestic and International football Direction of Study Hypothesis Limitations Delimitations Literature Review Performance Analysis in Sport Match Analysis Feedback Performance Indicators Attacking Build up Play in Football International and Domestic Football Suggestions for Future Research Methodology Research Design Sample Performance Indicators Operational Definitions Possession Origins First Pass Intent During Possession Possession Outcomes Template Design Procedure Reliability Data Analysis Results Overview Performance Variables Possession Origin... 31

5 4.2.2 Possession Origin Areas First Pass of the Possession Passes Per Possession Pitch Progression Possession Outcomes Outcome Areas Final Third and Penalty Area Entries Crosses and Long Passes Possession Combined Variables Possession Origin with Positive Outcomes Length of Possessions and Positive Outcomes Length of possessions prior to goals scored Positive Final 3rd and Penalty Area Entries Discussion Introduction Possession Origins First Attacking Intention Possession Pitch Progression Final Third and Penalty Area Entries Crosses and Long Passes Possession Outcomes Length of Possessions Prior to Goals Scored Implications of Findings Conclusion Main Findings Future Research References... 69

6 List of Tables Table Description Page Number 1 Matches Selected for Analysis 17 2 Operational Definitions Possession Origins 18 3 Operational Definitions Passing 19 4 Operational Definitions Possession Outcomes 22 5 Mean and Standard Deviation of Possession Origins 31 6 Significant Results of Mann-Whitney U test and Cohen s 34 effect size test on Origin Area 7 Mean and standard deviation for length of first pass 35 8 Significant Results of Mann-Whitney U test and Cohen s 35 effect size test on First Pass Length 9 Mean and standard deviation for direction of first pass Significant Results of Mann-Whitney U test and Cohen s 37 effect size test on First Pass Direction 11 Significant Results of Mann-Whitney U test and Cohen s 40 effect size test on Number of Passes per Possession 12 Mean and standard deviation for Pitch Progression Mean and standard deviation for Possession Outcomes Mean and standard deviations for Secondary outcomes Significant Results of Mann-Whitney U test and Cohen s 45 effect size test on Outcome Area 16 Mean and standard deviation for Final Third and Penalty 46 Area Entries 17 Mean and standard deviations for Crosses and Long 47 Passes 18 Significant Results of Mann-Whitney U test and Cohen s 47 effect size test on Crosses and Long Passes 19 Location of where positive outcomes originated Number of passes and length of possessions in seconds that preceded a goal. 52 i

7 List of Figures Figure Description Page Number 1 Template Design Final 3 rd and Penalty Area Entries 19 2 Template Design Pitch Progression 20 3 Template Design Pitch Map 21 4 Template Design Pre pilot test code window 24 5 Template Deign pre pilot test code window with links shown 24 6 Sequence of how possessions were coded 25 7 Template Design finalised code window after pilot testing 26 8 Example of pilot test data 27 9 Intra-rater reliability test results Results Percentage of possession origins Results Possession origin locations Results First pass lengths in possession Results Direction of first pass in a possession Results Passes per possession Results Pitch Progression Results Outcome locations Results Final third and penalty area entries Results Average possession and mean possession length 48 (seconds) 19 Results Location of where positive outcomes originated Results Length of possessions which ended with a positive 51 outcome 21.1 Results Final third and penalty area entries with positive 53 outcomes for Bayern Munich 21.2 Results Final third and penalty area entries with positive 53 outcomes for Chelsea 21.3 Results Final third and penalty area entries with positive 54 outcomes for England 21.4 Results Final third and penalty area entries with positive outcomes for Germany 54 ii

8 List of Appendices Appendix Description Page Number A Intra-rater Kappa Scores A1 B Cohen s Kappa value and level of agreement B1 C Cohen s effect score ranking C1 D SPSS Scores D1 Bayern Munich Comparison with Chelsea D1 England Comparison with Germany D4 Bayern Munich Comparison with Germany D7 Chelsea Comparison with England D10 iii

9 Abstract The Purpose of this study was to identify if there were any differences between styles of attacking build up play between international and domestic football and if this also differed across different countries. The same number of competitive fixtures (N=6) were analysed for four teams, Two international teams, England and Germany, and two domestic teams from their respective leagues, Chelsea and Bayern Munich. A wide variety of performance indicators were used in order to record multiple variables that would allow differentiation between successful and unsuccessful performances (Hughes and Bartlett, 2004). For the domestic teams, footage was collected from six of their 2014/15 league fixtures, while for the international teams, Germany s footage was collected from the 2014 FIFA World Cup, while three matches came from this tournament for England and another three from the 2012 European Championship, due to their lack of progression in the tournaments. Footage was analysed using StudioCode V10 (Sportstec, Australia), with a pilot study used to provide data collection experience for the analyst using the system that was tested for reliability using Kappa values. Data collected was put through Microsoft Excel and SPSS so that it could be subjected to Mann-Whitney U tests and Cohen s effect size to check data collected for significant differences. Statistical differences for the Mann-Whitney U test were set at (p<0.05) and for the Cohen s effect size test at (d>0.8). Possession based results found that domestic teams had a greater number of possessions per game and used more passes prior to scoring a goal. Within this study, German teams were found to have more possession on average per game and began attacks from deeper within their own half than English teams, this proved their ability to make more progression up the pitch with each possession and penetrate the oppositions final third more often than English teams. The results of this research found that International teams were less successful than domestic teams at crossing the ball and making long passes, but did suggest that both the English teams in the study played in a more direct style by beginning possessions with longer forward passes and had shorter possession times than both the German sides. iv

10 It was therefore concluded that there are differences in the styles of play between international and domestic football, however one is not necessarily better than the other as they were both effective due to both international and domestic teams scoring the same number of combined goals (N=22). This research also found that a direct style of build-up is not always the most effective method, contradictorily to most previous research, as both methods had success throughout this study. v

11 Chapter I Introduction 1

12 1.0 Introduction 1.1 Association Football Football is the world s most popular sport and was formalised into the sport as we know it today by the establishment of The Football Association in The sport soon globalised forcing the development of a world governing body FIFA (Federation of the International Football Association) in 1904 (Reilly and Williams, 2003). Association football is an invasion game which involves two teams of 11 players where the aim is to score more times than the opposition in the regulation 90 minutes (Gifford, 2009; The Football Association, No Date). Over the years since its professionalization, association football has developed rapidly with performance levels being higher now than ever before. Thus, with such high standards being set, it is essential for teams and athletes to keep up with their opposition. This has brought to light the discipline of sports science and how reflecting on performance can bring about meaningful impacts in football (Williams, 2013). 1.2 Performance analysis in football Performance analysis in invasion sports such as football is difficult to appraise compared to individual sports, as measurements of performance is far more subjective (Carling et al., 2005). Notational analysis is commonly used in football and other team invasion sports as it allows data to be gathered efficiently and means that almost any data required can be recorded. More recently, this notational analysis is being completed electronically allowing for analysts to link incidents to video footage of matches. This increased flexibility of notational analysis means that both qualitative and quantitative analysis can be accommodated (Hughes and Franks, 2007). Hughes (1998) and O Donoghue (2015) highlight five main purposes of notational analysis in sport as: Analysis of technique Analysis of effectiveness Tactical analysis Movement analysis Analysis of decision making 2

13 All of these are used in football analysis and are seen as a way of improving performance. O Donoghue (2009) highlighted an example of successful use of notational analysis, when the former Director of Coaching at the Football Association discovered an emergence of British teams adopting the long ball pattern of attacking play. This finding may have led to discovering why some teams were more effective going forward than others. 1.3 Domestic and International football The English nation football team has come under great scrutiny over recent years for it s under achievements at major international tournaments, however, many herald the English Premier League (EPL) as the greatest league in the world. So why are the English players unable to transfer this onto the international stage? International sides such as Germany who have a much less competitive domestic league, quite often having a two team race for the title, are far more successful to England in international tournaments. This may display how an influx in foreign players playing in the EPL has hindered the progression of English talent and the English national team, or it may come down to the differences in styles of play between the nations that is more effective. 1.4 Direction of Study The purpose of this study was to address the lack of literature surrounding the link between international and domestic football. By observing a domestic side from the English and German leagues followed by their respective national side, the aim was to determine whether or not there was a difference in attacking build up play between the club and international sides and whether there was a difference between nations. These differences were documented by analysing key performance indicators such as possession statistics, lengths and frequency of passing, areas of the pitch being attacked and shots on goal. The aim of this study was to determine if the style of attacking play in England is less effective than the more successful German team. 3

14 1.5 Hypothesis A total of 24 games were analysed played by Chelsea (n=6), Bayern Munich (n=6), England National Team (n=6) and Germany National Team (n=6), where performance indicators were recorded. The following hypotheses were tested: H1 There will be significant differences in the styles of attacking build-up play between the four teams. H2 More successful teams will keep possession of the ball for longer periods H3 Less successful teams will use a more direct approach to attacking H4 Bayern Munich and Germany will have more positive outcomes to their possessions. 1.6 Limitations All footage came from television broadcasts meaning that during certain periods of the games, television replays were being shown over live footage, resulting in sections of possessions being missed. However, the effect this had on the data was minimal. Lago-Penas et al. (2010) suggest that using teams from one off tournaments, such as in this case, means that teams are unbalances in terms of the strength of opposition. 1.7 Delimitations All analysis was conducted by one observer, meaning that inter analyst subjectivity was minimal and definitions of events were consistent. Large subject area was used meaning that results can be seen as valid. 4

15 Chapter II Literature Review 5

16 2.0 Literature Review 2.1 Performance Analysis in Sport Performance analysis is completed through the use of observation, whether this be live or post-match, to provide feedback which can influence how future performance can be changed and improved (O Donoghue, 2015). Primarily concerned with exploring aspects of team and player performance, performance analysis covers technical, tactical, patterns of play and work rate, (O Donoghue, 2005) performance analysis is ever becoming a more integral part of the coaching process (Carling et al., 2005; Groom et al., 2011; Lyle, 2002), due to the advances in technology making match footage and more accessible (Carling et al., 2005). Through the development of professional football, a greater importance has been placed on performance analysis to keep up with the high demands of coaches, players and supporters. This is why more or less every professional side engages in performance analysis of some kind (Mackenzie & Cushion, 2013). Sports performance analysis is used to try and overcome the complexity and ambiguity of the coaching process (Jones and Wallace, 2005). The role of the coach is ultimately to improve a team s performance (Nash and Collins, 2006), a process which relies on the coach to observe and analyse in order to identify strengths and weaknesses and to implement changes. However, this process is often highly subjective and tends to be based on the coach s perceptions of the performance, frequently meaning that the outcome of the performance may emotionally affect their feedback (Hughes and Franks, 2008). Research has shown that recalling match events correctly is highly difficult as human memory systems have limitations, especially for longer events such as 90-minute football match. Franks and Miller (1991) found that International level football coaches could only recall 30 per cent of key moments during a game, these tended to be the more memorable aspects of the match, including things like; controversial decisions, errors and outstanding technical performances. This form of highlighting can lead to many important aspects to be omitted from post-match feedback, this is why the use of notational analysis can be highly influential in shaping preparation for future performance as it is capable of recording all events and is not influenced by coach or analyst bias (Hughes and Franks, 2007; Maslovat and Franks, 6

17 2008). Therefore, augmented feedback provided to players by a coach should be justified through the use of performance analysis to gather as much information as possible in order to base their decisions on objective data (Carling et al., 2005). Performance analysis can be provided both quantitatively and qualitatively to display various types of information in order to aid the feedback process. However, quantitative data is more commonly used as it delivers an easier visual representation of what happened during a sporting performance. In team sports, match statistics can be presented through tables, charts and diagrams. This way data can be easily compared to highlight shortcoming in performance and if there are any mismatches against future opposition (O Donoghue and Mayes, 2013). With the advancement of technology, statistics can be generated through the use of computer software, allowing for match footage to be linked to the statistics, therefore enhancing accessibility and making the analysis process more objective (Mackenzie and Cushion, 2014 and Carling, et al, 2005). Match analysis software such as SportsCode (Sportstec Inc., Warriewood, New South Wales, Australia) and Focus X2 (Elite Sports Analysis, Delgaty Bay, Fife, Scotland) allow for the user to decide what aspects of the game require coding, meaning it can be specific to the analyst or coach, and then once coded, the incidents can be linked to video clips, giving the statistics context (O Donoghue and Holmes, 2015). Other quantitative data can derive from individual athlete tracking through the use of GPS trackers and commercial data such as Prozone3 (Prozone Sports Ltd, Leeds, UK). This can gather data that allows for visual representation of a player s movement during a game, giving information on performance intensity allowing for judgement on an athlete s levels of fatigue. Tracking data can also allow for tactical elements of performance to be analysed as team movements can be recorded to view the distribution of play during a match (Fonseca et al.) This data produced can be subjected to qualitative analysis to determine the effectiveness of play (O Donoghue and Holmes, 2015). 7

18 2.2 Match Analysis Performance analysis in football is commonly undertaken through the use of match analysis which is described by Lago (2009, p. 1463) as the objective recording and examination of behavioural events that occur during competition. Over the last 30 years, performance analysis in football has become more and more essential, and the development of performance analysis has led to the improvements of analysis systems and research specific to football, making it accessible for most professional teams (Hughes and Franks, 2005; Carling et al. 2005; James, 2006). The desired outcomes of performance analysis in football are quite often dictated by the coach depending on what they deem to be a team s area of weakness or strength. Carling et al. (2005) focused on four key areas of performance that can be analysed in order to collect relevant data: 1) Technical aspects focus on video footage of a player performing skills which can be used to evaluate technique to devise relevant training. 2) Behavioural aspects assess personal traits such as game reading, decision making and concentration 3) Physical aspects used to investigate a player s movement and work-rate to see if they are able to cope with the physiological demands and to assess potential injuries for player s health and well-being. 4) Tactical aspects - focus on the effectiveness of strategies used during a game to evaluate why, or why not, certain tactical plans worked. Can also be used in preparation for future matches through opposition analysis, which could collect match statistics to see where the opposition is most effective. This can be used to set up a match strategy to overcome the opposition s strengths (Groom et al., 2011). 2.3 Feedback Due to the increased pressure for professional athletes to continually improve their performance, it is vital the appropriate feedback is given. Feedback is a vital part of the coaching process and can be generated through the use of match analysis to elicit performance improvements in athletes (Franks and Goodman, 1986). Augmented feedback allows for athletes to receive reassurance on their own intrinsic views 8

19 surrounding their knowledge of results and performance (Magill, 2001). Therefore, if feedback is not given after performance, areas for improvement cannot be identified, meaning that the athletes cannot learn from their performance. Consequently, using match analysis to provide feedback is vital to the performance analysis element of the coaching process (O Donoghue, 2008a; Drust 2010). Most feedback in sports, especially association football, now derives from information technology systems as a result of using video footage, which can be used to provide; a) Immediate feedback b) Statistical comparisons c) Incidents linked to video footage d) Development of a database Liebermann et al. (2002) All of these have allowed for feedback to be far more objective and reliable, making it easier for athletes to focus on developing on weaknesses in their performance. However, Newell (1981) emphasised how the nature of feeding back information is pivotal in athlete progression as more precise feedback will yield greater benefits. The advancement of technology has allowed for this feedback to be more precise and almost immediate after performance (Bangsbo et al., 1991). Therefore, failure to provide this knowledge may prevent an athlete from reaching their potential and stop them from developing (Franks, 2004). 2.4 Performance Indicators Within performance analysis, a great importance is based on having a group of set definitions or parameters that make the analysis process more objective and accurate. Hughes and Bartlett (2004) state that a performance indicator aims to define some small or all aspects of successful performance. These indicators are used to assess the performance of an individual, a team or a skill and are commonly used to generalise a definition so that everyone will be looking for and measuring the same variables, rather than leaving assessment open to opinion. O Donoghue (2008b) and Choi (2008), state how key performance indicators are essential for real-time analysis systems as data can be entered quickly within live performance so that data can be delivered back to players and coaches almost immediately. 9

20 Before setting up an analysis system, it is important for the performance analyst to choose and define these key performance indicators so that it can be standardised throughout performance. There are a number of ways in which performance indicators can be assigned, McCorry et al (1996) and Choi (2008) used focus groups with expert coaches to gain different opinions and begin discussions, while Choi et al (2006) used statistical tests and regression analysis to determine which process indicators led to the positive outcomes. Some studies have attempted to generate position specific performance indicators for invasion games so that players have a better idea of what their role is in a game. Hughes et al (2012) researched the KPI s in association football by position. For each position, seven KPI s were defined for each of these categories; physiological, tactical, technical defensive, technical attacking and psychological. As a whole, for the outfield players, the KIP s were very similar for each position, but were ranked in order of importance. This way a player is being measured on skills more relevant to them. From these KPI s, over a period of time, an analyst is able to decipher the tactical approaches used by a team and see what is more effective (Hughes and Bartlett, 2004). Possession in football is a KPI to identify which team has had control in a game, over recent years, teams such as FC Barcelona, who averaged 69.3% in all competitions for season, and FC Bayern Munich, 62.5%, tend to dominate teams by keeping the ball for prolonged periods of time (Opta via Squawka.com). Jones, James and Mellalieu (2004) conducted research involving possessions of English Premier League Teams in the season and concluded that teams with longer durations of possession were more successful in the outcome of their match. While Grant, Williams and Reilly (1999) and Hook and Hughes (2001) who studied cup competitions also agree. 2.5 Attacking Build up Play in Football Great importance is being placed on tactical preparation prior to performance in association football as the employed playing style can have a critical impact on the outcome of the game (Tenga and Larsen, 2003). Commonly, there are two distinct styles of play within football, one where a team will attempt to move the ball into a shooting position in as few passes as possible, known as direct play, or one where a 10

21 team keeps the ball for longer periods of time with high passing sequences, known as possession play (Hughes and Franks, 2004). Possession play often refers to numerous short passes as a low risk way of maintaining possession, while direct play involves more hazardous long passes which are easier to cut out for defenders. Konstadinidou and Tsigilis (2005) studied the 1999 FIFA Women s World Cup semi-finalists playing styles and found clear differences between their build up play. The eventual winners of the tournament, USA, used combinations of short and medium passes to create scoring opportunities, while the 4 th place team, Norway, used long balls from their defensive area and took shots from a further distance than the other teams. This perhaps shows how short and quick passes will create better, and more goal scoring opportunities. Similarly, Johnson and Murphy (2010), conducted research into 84 games from 2007/08 Australian Soccer league and found that longer passing sequences of four or more passes was a much more effective way of scoring goals than those below. On the other hand, a study completed by Yiannakos and Armatas (2006) into the goals scored at EURO 2004 showed that in the actions prior to goals, teams used long passes more frequently to combination or possession play. This expresses that in this tournament a more direct style yielded greater success than keeping the ball with frequent low risk short passes. Wade (1996) proposed three key principles of play in football; attack, defence and preparation or midfield play. He broke down the attacking principle into five subsections, highlighting the importance of penetration, width, mobility, improvisation and depth. Later literature by Ouellette (2004) supported this, with both emphasising the importance to practice each of these during each training session so that it can be successfully transferred into match situations. Wade underlined the importance of the preparation phase as this can determine the strength of any attack, if a team is more organised, this means that when they do go forward they will attack with greater effectiveness and will be more likely to penetrate the oppositions defence. This preparation phase may link to the style of play that a team are adopting, whether this me more direct with fewer longer passes or more indirect with an aim to keep possession and remain patient. Where a team wins back possession of the ball can be an indicator as to whether they defend using a high press tactic to gain possession in the attacking third or to get players behind the ball and build an attack from the back. Garganta, Maia and Basto 11

22 (1997) conducted a study into where a team won possession of the ball amongst other things by five European teams. They found from this research that PSG, Bayern Munich and Milan, who began attacks in the offensive third more often, had a tendency to get more shots at goal away per game compared to Barcelona and Porto who began attacks in the middle and defensive third most often respectively. Winning the ball back quickly in the attacking third is an important component in winning football games (Miller, 1994), as also proven by Bate (1988) who found that 50-60% of all movements leading to a shot on target originated in the attacking third of the field. Build up play can alter between teams depending on the areas of the pitch in which they aim to attack. For example, pre-match analysis may highlight that a team has been poor at defending crosses, so the attacking side may attempt to use the wings more often. There is a distinct lack of literature in terms of areas of the pitch in which team s tent to attack more often, however, a study completed by Ensum, Pollard and Taylor (2004) into goals scored at the 2002 FIFA World Cup looked at the probabilities of goal scoring from shots on goal. The eventual winners of the World Cup, Brazil, created and scored the most amount of goals, and found most success from wide areas, with 45% of all their goals coming from crosses. This study also found that throughout the tournament, the best chances were created from crosses. This was similar to the 1986 FIFA World Cup where Hughes, Robertson and Nicholson (1988) found that the successful teams used the wings when entering the final third. However, Lago-Penas et al. (2010) conducted research into 380 matches from the 2008/09 Spanish La Liga, and found that unsuccessful teams were using the wide areas and crossing tactics more often, perhaps as a speculative effort to get the ball into the 18- yard box. This more recent research may show how attacking build up play has changed over the years and teams are using possession football now to greater success rather than working play wide for a crossing opportunity. It may also demonstrate a difference between playing patters of international sides playing in the World Cup and club teams playing in their domestic league. 12

23 2.6 International and Domestic Football Domestic football in Europe s top leagues has seen an influx in the recruitment of foreign players over the past 20 years, many believe that this is having a detrimental effect on the national football team as potential youth talents are being pushed aside by big money transfers of already proven international stars (Solberg and Haugen, 2008). However, in the same study, Solberg and Haugen (2008) proved that there was a positive correlation between the number of foreign players being brought into the English Premier League (EPL) and the performance of the England National team. Although, this study proved that in countries such as France, Norway and Greece, the more players they export, the better their national teams become. This could be because it is giving the players opportunity to gain experience abroad, and also leaves room for more home grown players to be produced and play first team football. Despite this influx foreign players said to be improving the standard of football in the EPL, there are still questions regarding England s performances in major tournaments. Collet (2013) reported that teams in England play with a less effective style of play compared to sides from Germany, who maintain possession for long periods of games and have a greater pass to shots on goal ratio, this may demonstrate that more direct build up play is less effective. During the 2014 FIFA World Cup, there was a clear difference between these two nations, the eventual winners, Germany attempted 726 passes with a success rate of 82%, while England only attempted 548 passes per game with a success rate of 78% (FIFA, 2014). This is a similar representation of the domestic teams of both these nations, perhaps suggesting that the style of play used by the club teams can have a positive effect on the performance of the national team. In spite of this, there is very little literature directly comparing performance and tactics between domestic teams and their national teams. 13

24 2.7 Suggestions for Future Research From the literature reviewed, build up play in association football is an area that has been closely documented, specifically in terms of passing and possession and what outcome this leads to. Key themes throughout the literature involved how lengths of possessions and types of passing can lead to a team being deemed as successful. Most studies suggested that more direct styles are more effective and give more positive outcomes. However, over recent years it seems that the more successful teams in important competitions are using possession play as a way of overcoming opposition. So future research may prove that keeping the ball and patiently breaking down oppositions will be a more effective method of scoring, as opposed to playing quickly and directly, which might yield more, yet less clear cut, chances. From the literature, little research has been found supporting areas of the pitch used by teams in build-up play. Future research may consider where an attack originates from on the pitch and areas used by a team to penetrate the opposition, whether this be by going wide or central. 14

25 Chapter III Methodology 15

26 3.0 Methodology 3.1 Research Design A computerised notional analysis system has been used to collect data and analyse (n=24) competitive matches split between the English Premier League (EPL), German Bundesliga and international matches played by England and Germany in their most competitive tournaments. Match footage has been obtained via the Centre for Performance Analysis at Cardiff Metropolitan University as well as an online scouting database, Wyscout. Through the use of Studiocode (Sportstec, Australia), a code window was designed to record instances when the teams had possession of the ball and what happened during that possession. The data generated from putting the match footage through Studiocode was then exported to Microsoft Excel where it could be organised into a simpler visual representation so that it could be analysed. 3.2 Sample With the aim of comparing international and domestic styles of attacking build-up play, the sample used for this study included the champions from the 2014/15 EPL, Chelsea, and the English national team as well as the champions from the 2014/15 Bundesliga, Bayern Munich, and the German national team. Six games were analysed for each team, in the case of the international sides, matches were used from the most recent international tournaments, the 2014 World Cup and the 2012 European Championships. The reasoning behind choosing six games for each team comes from Hughes, Evans and Wells (2001), who suggested that 6 matches would provide enough data to assess typical performance. In total, across these 24 matches, a total of 44 goals have been analysed to attempt to determine the attacking styles of these teams. To make the study more valid, the domestic teams had the same number of home and away fixtures analysed as each other, while all of the international fixtures were played in neutral venues. The (n=24) matches selected for analysis were: 16

27 Table 1: Matches selected for analysis. Chelsea (All EPL) England* Bayern Munich (All Bundesliga) Germany (All World Cup 2014) Arsenal (A) France (N) Mainz 05 (H) Portugal (N) Burnley (A) Sweden (N) Paderborn (H) Ghana (N) Manchester United (H) Ukraine (N) Hamburg (A) USA (N) Aston Villa (H) Italy (N) Hannover 96 (A) France (N) Newcastle (A) Uruguay (N) Shalke 04 (A) Brazil (N) Tottenham Hotspur (A) Costa Rica (N) Borussia Dortmund (A) Argentina (N) * First three matches from Euro 2012, rest from WC Performance Indicators Performance indicators are essential when it comes to determining the success of a team s performance. These performance indicators will allow for comparisons to be made between the teams (O Donoghue, 2010) and to see how they each differ in their attacking build up play. In this study, the performance indicators used to determine the success of the teams were: Attacking intent First pass of a possession Passes per possession Positive or negative play How far up or down the pitch the team moved during a possession Shooting accuracy Attack conversion rate Lengths of possession Final third and penalty area entries Positive/negative outcomes From these performance indicators, it will also be possible to measure other aspects of the game such as passing accuracy and match possession. As a result of measuring these variables, a quantitative analysis can be undertaken allowing for objective results. However, there will be some level of subjectivity associated with the observer s judgement. This is why Hughes and Franks (2004) encouraged the use of operational definitions as a way of overcoming this issue. 17

28 3.4 Operational Definitions Operational definitions give clear descriptions to events that happen during performance, they are essential in analysis as they provide opportunity for the same things that happen during performance to be compared. By defining variables that happen during a performance, it should mean that any observer could use the system and get more or less exactly the same results (Hughes and Franks, 2004) Possession Origins The origins of possession considered how a team gained possession and in what area of the pitch this occurred. There are eight variables that dictate how a team gained possession (all of which are described in table 2), as well as ten zones of the pitch in which they may have gained this possession. Possessions began when the team was in clear control of the ball and has the chance to release the ball. Table 2: Origins of possession Possession Origin Kick Off Free Kick Throw in Interception Corner Tackle Penalty Goal Kick Operational Definition At the beginning of a game to start the match or to resume play following a goal or half time Play resuming after as a result of an infringement by the opposition, either direct or indirect Returning the ball into play by the use of the hands Preventing the ball from reaching its intended target. Play resuming as a result of the opposition turning the ball behind their own goal line Dispossessing the opposition Retaining possession in the form of a shot at goal as a result of player being fouled in the oppositions penalty area Goal keeper taking a kick from inside the penalty area as a result of opposition taking the ball out of play behind goal line 18

29 3.4.2 First Pass Intent The first pass of a possession can be essential as it can dictate the intention of the possession. In this study the first pass has been classified into three possible categories to describe the direction of the pass. The length of the pass was also recorded as a way of determining how directly the attacking team is trying to play. Table 3: Passing definitions Pass Forward Lateral Backwards Long Medium Short Incomplete No First Pass Operational Definition A pass in the direction of the oppositions goal A pass to either the left or the right of the pitch A pass in the direction of the teams own goal A pass 30 meters or further A pass between 15 and 30 meters A pass up to 15 meters A pass that does not reach its intended recipient When the team in possession does not make a pass before losing possession During Possession When a team is in possession of the ball, the observer will record final third and penalty area entries. This category allows for description as to where a team entered the attacking third, whether this be through the central, left or right channel of the pitch. This is the same for a team s entrance into the penalty area (See figure 1 below). From this it is possible to determine if a team has a preference as to which side of the pitch to attack and can also determine how often a team enters the final third of the pitch without penetrating into the penalty area. If a possession starts within the attacking third, then a final third entry will not be recorded during that attack. Figure 1: Final third and penalty area entries 19

30 The observer was also required to count the number of passes in the team s possession. This can allow for analysis on the style of play a team prefers to undertake, whether this be direct through fewer longer passes as an attempt to quickly disrupt the defence, or through possession play consisting of a greater number of shorter passes in an attempt to move the defensive players out of position. The final variable measured during possession was the progression up or down the pitch during a possession. Here the pitch is split into six separate zones, three-thirds in the defensive half and three-thirds in the offensive half. To record this variable, the observer had to remember where possession was obtained and how many zones of the pitch they moved forwards or backwards. A scale from -5 to 5 measures this, with the number corresponding to how many zones the attack advanced or retreated (See figure 2 Below). From this variable, it will be possible to observe how effective a team is at going forward. For example, if on average a team is progressing positively by three then this will show that they are continually moving forward, whereas a team with a neutral progression of zero on average will show that their attacks break down behind where it started as often as they go forward. Figure 2: Pitch progression and the six zones of the pitch 20

31 3.4.4 Possession Outcomes The outcomes of a possession consider how a team lost possession of the ball whether this be positive or negative. There are 13 outcomes of possession, six of which positive, six negative and one neutral outcome. Crosses were not immediately considered as an outcome as they can lead to a different outcome. However, crosses were included as a performance indicator and have been split into either being complete or incomplete (all outcomes and crosses have been defined in table 4). The same pitch map has been used to record the location of the outcomes as was done for the origins (See Figure 3 below). Possessions were concluded in the zone in which the last touch by the attacking team was taken. Pitch map key: PA: Penalty Area AR: Attacking third, right channel AC: Attacking third outside penalty area, central channel AL: Attacking third, left channel MR: Midfield third, right channel MC: Midfield third, central channel ML: Midfield third, left channel DR: Defensive third, right channel DC: Defensive third, central channel DL: Defensive third, left channel Figure 3: Pitch map for origin and outcome areas 21

32 Table 4: outcomes of possession Possession Outcomes Operational Definition Positive Goal When the ball crosses the goal line and is confirmed by the referee Shot on Target The goalkeeper prevents a shot that would have crossed the goal line Throw in Won When the opposition touch the ball last before it crosses either of the touchlines Corner Won When the opposition touch the ball last before it crosses the oppositions goal line Free kick Won Player being foul outside the oppositions penalty area Penalty Won Player being fouled inside the oppositions penalty area Negative Shot Off Target A shot that misses the goal completely Intercepted When the opposition prevent a pass from reaching the intended recipient Offside Giving away a free kick as a result of a player being offside Throw in Conceded When a player touches the ball last before it crosses either of the touchlines Tackled When an opposition player takes the ball away from a player whilst they are in possession Goal Kick Conceded When a player takes the ball beyond the oppositions goal line, but is not a shot at goal Neutral Shot Blocked When an opposition outfield player stops a shot at goal Can lead to Crosses Complete When a cross reaches its intended target either a positive or negative Incomplete When a cross does not reach its intended target outcome 22

33 3.5 Template Design For the data collection phase of this study, it was essential that a user friendly and logical template was designed in order for the information to be collected efficiently. It should also allow for the analysis to be as simple as possible and reflect the order of events that occur within the performance (O Donoghue, 2015; Hughes and Franks, 2004). A draft system was initially drawn out on paper to incorporate all of the variables that would be required to collect the data. Once this had been evaluated, a computerised notation system was designed using Studiocode. This system uses a systematic possession-by-possession analysis, meaning that to record what occurred, the observer coded how possession started, what happened during the possession and how the possession ended, then the cycle starts again. The code window included just one code button, this was Possession (identified by the red diamond in figure 4), which was activated as soon as an origin of possession was selected. Within the possessions, multiple variables were recorded (identifiable by blue circles), this allowed for differences to be highlighted within each possession. Possession would be ended manually after selecting an area of outcome. The code window was set out in such a way that the observer is able to work their way down the page. Colours were used to differentiate between positive and negative events and pitch maps used to aid visualisation. Figure 4 on following page displays the code window used for data collection. Activation links were used throughout the code window as a method of saving time and collecting a greater quantity and depth of information. Figure 5 on following page shows where the activation links were used. For pitch progression and outcomes, activation links were used to give a positive or negative description, this will therefore allow for the observer to see how effective a team were at attacking. A deactivation link could have been used to connect the outcome zones and when a possession ends, however, the reasoning for ending this manually is that a team may partially lose possession through a loose pass but could regain it unexpectedly straight away meaning that possession is not technically over. 23

34 Figure 4: Code window used for data collection Figure 5: Code window displaying the activation links on top 24

35 3.6 Procedure The analysis procedure began by observing each of the 24 matches using the completed code window (Figure 4). Match footage would be linked to a timeline in studiocode meaning that observations could begin to be recorded. As analysis was only being conducted on one of the team s possessions in each game, the matches could be fast forwarded whilst the opposition were in possession. Within each possession a maximum of 10 variables could be recorded and a minimum of 6 variables. When a possession began, there was a particular order used to input the data on the timeline, apart from final third entries, penalty area entries and crosses which could occur at any point during a possession but not necessarily end the possession (see figure 6). This would begin with the origin of possession, working through what occurred before inputting the outcome of each possession. As a result of coding each possession separately, timing for each possession can be recorded meaning that an average possession length can be calculated. Possession begins Origin location Final third, penalty area entries and crosses when applicable Passes per Possession Outcome type Possession ends Origin type First pass type: direction and length Pitch Progression: Positive or negative Outcome location Figure 6: Timeline to display the order of variables inputted to the timeline 3.7 Pilot Testing A pilot study was conducted on the initial data collection system to test that the correct information was being gathered and to familiarise the observer with how the system worked. The pilot was carried out on a half of a 2014/15 Spanish La Liga match between FC Barcelona and Sevilla FC. The aim of the pilot study was to hopefully identify any errors within the system that could be amended prior to data collection and to test the method of storing the data in a way that would make it easier for the 25

36 observer to analyse the output. As a result of this study, amendments included adding labels for when teams were successful or unsuccessful in attempting a long pass and secondary outcomes for events that occur after an outcome already being used on the system. For example, secondary outcomes would include throw in won as this may occur as a result of being tackled, corner won as this may occur as a result of a shot at goal or goal as this may occur as a result of a shot being on target (see figure 7). Furthermore, from the data produced from the pilot study, a sample table for raw data was produced using Microsoft Excel to make the data analysis process more efficient (See figure 8). Figure 7: Updated code window after pilot testing. 26

37 Figure 8: Excel spreadsheet showing data produced from pilot study 3.7 Reliability Ensuring that a study is reliable is essential when conducting notational analysis in sport, this is why testing was completed on this system to ensure that the results were consistent and repeatable (Thomas and Nelson, 1996). Using an intra-rater reliability study, the same first half was analysed using the new system. An intra-rater reliability study was chosen as this assesses the degree of reliability of one operator using the system, which is more applicable than an inter-rater reliability study as the system was only ever intended to be used by one person. To improve the reliability of this process, a one-week gap was required between the two tests as this minimises the chances of the operator recalling specific events and predicting what happens next, rather than coding what they see (O Donoghue, 2015). Testing was conducted on the second half of the game used in the pilot study between FC Barcelona and Sevilla FC from 2014/15 La Liga season. Ten variables were tested from the intra-rater reliability study using a measure of the percentage of agreement between the two observations. The level of agreement was determined by interpreting Cohen s K coefficient (1960) (Appendix B). 27

38 All of the variables (n=10) achieved at least a moderate level of agreement of 62% or above (See figure 9). The most reliable variable to be recorded was the penalty area entries which scored 95%, this may have been because this was the least occurring variable so will have been more noticeable when it occurred. On the other hand, the least reliably recorded variable was the origin area. This was tested using a weighted percentage of agreement as there were ten areas in which this option could be selected meaning that on occasions, possession would originate on the border of areas meaning that it could be classed in one or the other. Despite this being considerably lower than the highest percentage of agreement, it is still considered to be a strong score. The results of the percentage agreement tests can be found in appendix A. 100% 80% 60% Percentage Agreement 40% 95% 89% 88% 88% 75% 74% 67% 67% 63% 62% 20% 0% Penalty Area Entry Possession Outcome Possession Origin Final 3rd Entry Outcome Areas First Pass Direction Performance Variables First Pass Length Passes Per Pitch Origin Area Possession Progression Figure 9: Graph to show the level of agreements between variables within the pilot test. 3.8 Data Analysis Once the data had been collected from the footage, the results were organised using the transcriber organiser within Studiocode to present what happened within each possession. This data was then exported into Microsoft Excel where data from all the matches can be brought together so that means, standard deviations and percentages can be calculated for relevant variables. This means that data can be sorted for further analysis within SPSS (Version 22). The data did not have the characteristics to use a parametric test, therefore, was tested using a non-parametric test. Four comparisons were made within this testing in order to address the hypothesis that there are 28

39 differences in the style of attacking build-up play between different countries. These were; EPL (Chelsea) against Bundesliga (FC Bayern Munich), English National Team against German National Team, EPL against English National Team and Bundesliga against German National team. A Mann-Whitney U test was used to identify significant differences between the two groups in each of the four tests, significant statistics were identifiable where (p<0.05). Cohen s d for effect size was also used to assess the meaningfulness of the data. Effect size is defined as the difference between the means of two groups, divided by the standard deviation between the two groups (Durlak, 2009). 29

40 Chapter IV Results 30

41 4.0 Results 4.1 Overview Once the observation stage had been completed and all data had been collected, it was transferred into Microsoft Excel (2016) and IBM SPSS (Version 22) where it could be manipulated. The results from the data are displayed throughout this chapter in table and graphical form. Significant differences were set at (p<0.05) for Mann- Whitney U tests, while Cohen s d value scores were rounded to two decimal places and are displayed in appendix C. 4.2 Performance Variables Possession Origin Table 5: Displays the mean and standard deviation of how possession started for each team. Team Bayern Munich Chelsea England Germany Origin M ±SD M ±SD M ±SD M ±SD Corner 3.0 ± ± ± ±1.8 Free kick 8.7 ± ± ± ±3.6 Goal kick 2.2 ± ± ± ±1.8 Interception 42.5 ± ± ± ±5.6 Kick off 1.2 ± ± ± ±0 Penalty 1 ±0 n/a n/a 1 ±0 Tackle 7.8 ± ± ± ±2.4 Throw in 12.2 ± ± ± ±4.1 Table seven shows a breakdown of how possession originated for each team and which occurred most often for each side. Significant differences were found within this performance variable when comparing the EPL team, Chelsea, with their respective national team, England. The Mann-Whitney U test highlighted that there was a significant difference (p<0.05) with the number of possessions originating from free 31

42 Percentage % kicks and corners. Cohen s effect size also highlighted large levels of significance when comparing the English and German national sides possession origins from corners (d=1.13), while very large significant differences were found when comparing Bayern Munich and the German national team with origins of possession by goal kicks (d=-1.33). A common theme for each team is that the majority of possessions began because of an interception, emphasised through the fact that out of 1979 possessions recorded throughout the study, 986 of them originated from an interception. Figure 11 below displays the percentage of how possession originated for each team, from this, it is easy to see where the main differences lie Bayern Munich Chelsea England Germany Corner Free Kick Goal Kick Interception Kick Off Penalty Tackle Throw In Figure 10: shows the percentage distribution of each possession origin for the four teams being studied. 32

43 4.2.2 Possession Origin Areas DL ML AL DL ML AL 3.5 ± % 8.5 ± % 3.6 ±1.3 4% 2.8 ± % 13.2 ±5 16.4% 2.5 ± % DC MC AC PA DC MC AC PA 22.8 ± ± ±1 1 ± ± ± ±0.8 n/a 30.2% 27.6% 1.3% 0.2% 23.2% 30.9% 1.9% 0% DR MR AR DR MR AR 3.2 ± % 12 ± % 2.6 ± % 4.5 ± % 10.3 ± % 3.4 ± % Figure 11: Percentages, means and standard deviations of where Bayern Munich (left above), Chelsea (right above), England (left below) and Germany (right below) obtained Possession. DL ML AL DL ML AL 4.2 ± % 10.7 ± % 5.4 ± % 7.1 ± % 17.7 ± % 4.3 ± % DC MC AC PA DC MC AC PA 27.5 ± ± ±1.9 n/a 42.9 ± ± ±0.5 1 ±0 30.2% 25.5% 1.6% 0% 30.7% 27.3% 1.6% 0.2% DR 3 ± % MR 11.0 ± % AR ± % DR 4.9 ± % MR 17.7 ± % AR 5.7 ± %

44 Table 6: Highlights the significant results from the Mann-Whitney U test along with the corresponding Cohen s effect size score. Comparison Origin Area P value D Value England vs Germany AL Bayern Munich vs Germany DC Chelsea vs England AL AR DC From the Mann-Whitney U test, a number of significant differences were found when comparing the four teams. Firstly, England obtained possession in the AL zone significantly more than Germany (p=0.016). A number of significant differences were found when comparing zones of possession origin between Chelsea and England; AL (p=0.016), AR (p=0.03) and DC (p=0.009). Interestingly, when comparing Bayern Munich and Germany, the Mann Whitney U test identified the central defensive zone as significantly different, although the Cohen s effect score did not. Cohen s effect score identified a high significant difference between Chelsea and Bayern Munich in the ML zone (d=1.03). The d score found more high scoring significant differences when comparing Chelsea and the English national team, where five out of the ten zones scored either high or very high; AL (d=-1.08), DC (d=-0.94), DL (d=-0.91), AR (d=-1.5) and DR (d=0.83). 34

45 4.2.3 First Pass of the Possession Length Table 7: Displays the Mean and standard deviation for the length of the first pass in each possession for the four teams. Short Medium Long Incomplete No First Pass Bayern Munich 42.8 ± ± ± ± ±1.8 Chelsea 38.7 ± ± ±4.4 4 ± ±0.5 England 50 ± ± ±4.9 9 ± ±1.6 Germany 45.5 ± ± ± ± ±0.8 60% 50% 40% 30% 20% 10% 0% Bayern Munich Chelsea England Germany No First Pass Incomplete Short Medium Long Figure 12: Percentage of first pass lengths in a possession Table 8: Highlights the significant results from the Mann-Whitney U test along with the corresponding Cohen s effect size score. Comparison Pass Length P value D Value England vs Germany Medium Incomplete Chelsea vs England Medium Incomplete

46 The Mann-Whitney U test identified significant differences when comparing the English national team with Chelsea s and the German national team s percentage of incomplete passes and medium length passes (p<0.05). Cohen s effect size identified moderate levels of significance when differentiating the English and German national team s long passes (d=0.73) and when comparing Chelsea and England s short passes (d=-0.73). 36

47 Direction Table 9: Displays the Mean and standard deviation for the direction of the first pass in each possession for the four teams. Forward Backward Lateral n/a Bayern 33.3 ± ± ±5.1 5 ±3.2 Munich Chelsea 36.8 ± ± ± ±1.8 England 39.8 ± ± ± ±4 Germany 37.2 ± ± ± ±1.8 50% 40% 30% 20% 10% 0% Bayern Munich Chelsea England Germany Backwards Forwards Lateral N/a Figure 13: Percentage of the directions of the first pass during a possession. N/a includes when no pass was attempted and all incomplete passes Comparison Pass Length P value D Value England vs Germany Backward n/a Chelsea vs England n/a Table 10: Highlights the significant results from the Mann-Whitney U test along with the corresponding Cohen s effect size score. 37

48 The Mann-Whitney U test highlighted significant differences when comparing percentage of England and Germany s backward passes (p<0.05) and when comparing England s percentage of n/a passes with both Chelsea and Germany (p<0.05). Cohen s effect size identified a high level of significance between England and Chelsea s percentage of backwards passes (d=0.86) and a moderate level of significance when comparing Bayern Munich and Germany s percentage of forward passes (d=-0.52). 38

49 FREQUENCY Passes Per Possession 90 Bayern Munich Chelsea England Germany PASSES PER POSSESSION Figure 14: Graph to show the number of passes each team completed per possession and how often that occurred. 39

50 The Mann-Whitney U test identified a number of significant differences when comparing the four teams, each of these are shown in table 11 below. Table 11 also displays selected high and very high Cohen effect size scores. Interestingly, not all number of passes that have a significant d value have a significant p value and vice versa. Table 11: Displays the number of passes per possession that have a significant Mann- Whitney U test score or a significant Cohen s effect score. Team Comparison Passes P Value D Value Chelsea vs Bayern Munich N=1 > N= N= 4 > N= England vs Germany N= N=1 > N=4 > N=11 > N= N= Bayern Munich vs Germany N= N=9 > N= Chelsea vs England N= N= N=7 > N=15+ >

51 4.2.5 Pitch Progression Table 12: Displays the mean and standard deviation of each team s pitch progression when in possession of the ball Bayern Munich ± ± ± ± ± ± ± ±1.2 Chelsea ±0.4 ±2.3 ±3.4 ±5.7 ±5.7 ±9.3 ±2.3 ±2.1 ±1.8 England ± ±0.5 ±1.9 ±6.7 ±4.7 ±7.2 ±2.5 ±1.6 ±2.1 Germany 0 1 ± ±3.4 ±2.9 ±4.4 ±5.5 ±1.7 ±3 ±2 35% 30% 25% 20% 15% 10% 5% 0% Bayern Munich Chelsea England Germany Figure 15: Graph to show percentage of pitch zone progressions per possession. 41

52 The Mann-Whitney U test found significant differences between Chelsea and Bayern Munich in percentage of -2 pitch progressions, between England and Germany in percentage of 0 and 3 pitch progressions and between England and Chelsea in percentage of 0 pitch progression (p<0.05). The Cohen effect size test identified a high significant difference between England and Chelsea in percentage of -1 pitch progressions (p=0.94). 42

53 4.2.6 Possession Outcomes Table 13: Displays the mean and standard deviation of all the initial outcomes of possessions. Bayern Munich Chelsea England Germany Free kick conc. 1.3 ±0.5 2 ±1 1.4 ± ±0.6 Free kick won. 6.7 ± ± ± ±1.8 Goal kick conc. 3.2 ± ± ± ±1.5 Intercepted 31 ± ± ± ± 5.6 Offside 2.3 ± ±0.6 2 ± ±1.9 Penalty won 1 ±0 n/a n/a 1 ±0 Shot blocked 1.8 ± ± ±1.3 2 ±1.2 Shot off target 4.6 ± ± ± ±1.2 Shot on target 5.2 ± ±2.5 4 ± ±2.5 Tackled 18 ± ± ± ±3.2 Throw in conc. 5.8 ± ± ± ±2.7 The Mann-Whitney U test found just one significant difference within the possession outcomes, when comparing the number of possessions ending in being tackled between Chelsea and England (p<0.05). Cohen s effect size identified a high significant difference when comparing how often possession was intercepted between Bayern Munich and Chelsea (d=1.05). This study also included secondary outcomes, all of which are positive for the team in possession, these can be found in table 10 below. No significant differences were identified for secondary possession outcomes. Table 14: Displays the mean and standard deviations of all the secondary outcomes of possession. Bayern Chelsea England Germany Munich Corner Won 3.7 ± ±3 4.5 ± ±1.8 Goal 2.3 ± ± ±0.9 3 ±2.5 Throw in Won 7.7 ± ± ± ±3.8 43

54 4.2.7 Outcome Areas DL ML AL DL ML AL 2.5 ± % 8.5 ±1.9 11% 7.3 ± % 2.8 ±1 2.3% 11.5 ± % 4.5 ± % DC MC AC PA DC MC AC PA 2.8 ± ± ± ± ± ± ± ±6 3% 20.1% 12.3% 16% 8.9% 19.9% 11.8% 17.8% DR MR AR DR MR AR 3 ± % 10.8 ±4.8 14% 7.3 ± % 1.5 ± % 8.8 ±2.8 11% 6.2 ± % Figure 16: Percentages, means and standard deviations of where possession ended for Bayern Munich (left above), Chelsea (right above), England (left below) and Germany (right below). DL ML AL DL ML AL 1.8 ±1 1.3% 10.5 ± % 8.2 ±4.1 9% 3.2 ± % 8.8 ± % 7.2 ± % DC MC AC PA DC MC AC PA 5.2 ± ± ± ± ± ± ± ± % 21.8% 12.8% 10.4% 5.3% 21.3% 14.5% 14.5% DR MR AR DR MR AR 1.4 ± % 11.5 ±3 12.6% 12.3 ± % 2.5 ±0.7 1% 9.2 ± % 6.8 ± % 44

55 The Mann-Whitney U test ran to compare the four teams identified significant differences in a number of zones, all of which have been identified in table 9 below. Table 15: Displays the significant Mann-Whitney U test results and the Cohen effect size score for the outcome areas of possession for each team. Outcome Area P Value D Value Chelsea vs Bayern Munich AL England vs Germany DL PA Chelsea vs England AR AL > PA

56 4.2.8 Final Third and Penalty Area Entries Table 16: Displays the mean and standard deviation of each team final third entries and penalty area entries. Final 3 rd Entries Penalty Area Entries Left Central Right Left Central Right Bayern Munich 10.7 ± ± ± ±1 5.5 ± ±1.9 Chelsea 12.7 ± ± ± ± ±4.7 5 ±2.1 England 10.7 ± ± ±3.6 3 ± ± ±2.1 Germany 10 ± ± ±4.6 3 ± ± ±1.6 20/74 = 27% 34/105 = 32.4% 18/57 = 31.6% 18/78 = 23.1% 21/74 = 28.4% 25/105 = 23.8% 16/57 = 28.1% 34/78 = 43.6% 33/74 = 44.6% 46/105 = 43.8% 23/57 = 40.4% 26/78 = 33.3% 64/203 = 31.5% 76/193 = 39.4% 64/190 = 33.7% 60/214 = 28% 61/203 = 30% 57/193 = 29.5% 56/190 = 29.5% 74/214 = 34.6% 78/203 = 38.4% 60/193 = 31.1% 70/190 = 36.8% 80/214 = 37.4% Bayern Munich Chelsea England Germany Figure 17: Displays the number and percentage of final third and penalty area entries for each team. 46

57 The Mann-Whitney U test identified no significant differences for the final third entries, although the Cohen effect size test found a very high significance between Chelsea and Bayern Munich s entry into the final third down the left wind (d=1.12). As for the penalty area entries, the Mann-Whitney U test found significant differences between England and Bayern Munich with Germany entering the penalty area through the right side (p<0.05). It also identified significant differences between Chelsea and England when entering through the left (p<0.05). Significant differences were also recognised when looking at the total number of penalty area entries between England and Germany, and Chelsea and Bayern Munich Crosses and Long Passes Table 17: Displays the longer types of passes in the game and how often they were attempted by each team along with the success rate. Crosses Long Passes Complete Incomplete Success Complete Incomplete Success Bayern Munich 5.8 ± ± % 12.7 ± ± % Chelsea 5.5 ± ± % 17.5 ± ±6.1 58% England 3 ± ± % 12.5 ± ± % Germany 3 ± ± % 11.2 ± ± % No significant differences were identified by the Mann-Whitney U Test. However, the Cohen s effect size test identified very high levels of significance for the number of completed crosses by the domestic teams in comparison to their respective national side (d>0.8), see table 18. Table 18: displays the significant Cohen effect size scores for the crossing and long passing variables Cross/Long Pass D Value Chelsea vs England Cross Complete 0.97 Long Pass Complete 1.09 Bayern Munich vs Germany Cross Complete 1.06 Chelsea vs Bayern Munich Long Pass Complete

58 Possession Germany England Chelsea Bayern Munich Average Possession Per Game % Mean Possession Time (secs) Figure 18: Displays the average percentage of possession across each of the teams six matches and their average possession time in those games. No significant differences were found from the Mann-Whitney U test on this data. However, Cohen s effect score found a very high significance in possession time between Germany and Bayern Munich (d=-1.61) and between England and Germany (d=-1.05). 48

59 4.3 Combined Variables Possession Origin with Positive Outcomes Table 19: Displays the locations in which positive outcomes began and what percentage of each team s possessions were positive. Area Bayern Munich Chelsea England Germany DL DC DR ML MC MR AL AC AR PA Total Positive Possessions Total Possessions % Positive 33.5% 28.2% 26.2% 31.1% 49

60 DL ML AL DL ML AL 2.7± % 2.8 ± % 1.8 ± % 2 ±0 1.5% 3.8 ±2.2 14% 1 ±0 2.9% DC MC AC PA DC MC AC PA 10.2 ± ±4 1 ±0 1 ±0 6.2 ± ±4.2 1 ±0 n/a 32.9% 27.7% 1.3% 1.3% 22.8% 32.4% 1.5% 0% DR MR AR DR MR AR 2.0 ± % 4.8 ± % 1.3 ± % 2.7 ± % 4.0 ± % 1.5 ± % Figure 19: Displays the mean, standard deviation and percentage where positive outcomes originated for each team. DL ML AL DL ML AL 1.6 ± % 4.5 ± % 1.8 ± % 2 ± % 4.2 ± % 1.5 ± % DC MC AC PA DC MC AC PA 7.4 ± ±2.9 1 ±0 n/a 8.2 ±7.3 8 ± ±0.6 1 ±0 25.9% 18.2% 2.1% 0% 32.2% 26.3% 2.6% 1.3% DR MR AR DR MR AR 1.5 ± % 5.6 ± % 2.8 ± % ± % 3.5 ± % 2.3 ± %

61 Number of Positive Outcomes Length of Possessions and Positive Outcomes Zero One Two Three Four Five Six Seven Eight Nine Ten Eleven Twelve Thirteen Fourteen Fifteen Plus Passes Per Possession Figure 20: Displays the frequency of positive outcomes of possessions for each team compared to the number of passes in each possession. Bayern Munich Chelsea England Germany 51

62 4.3.3 Length of possessions prior to goals scored Table 20: Displays the number of passes and possession lengths in time when a goal is the outcome of the possession for each team. Bayern Munich Chelsea England Germany Time Passes Time Passes Time Passes (secs) Prior to (secs) Prior to (secs) Prior to Passes Prior to Goal Time (secs) Goal Goal Goal Mean Table 12 shows how the domestic teams take longer before scoring a goal in both number of passes during the possession and time of the possession, with Bayern Munich taking the longest, having an average possession time before a goal being scored of 29.3 seconds. However, Germany scored a greater number of goals in shorter periods of time and with fewer passes. 52

63 4.3.4 Positive Final 3rd and Penalty Area Entries Positive 5/20 = 25% Positive 5/21 = 23.8% Positive 15/33 = 45.5% Positive 7/64 = 10.9% Positive 16/61 = 26.2% Positive 17/78 = 21.8% Final third entries Penalty Area Entries - 74 Figure 21.1: Percentage of positive outcomes that come from final third and penalty area entries for Bayern Munich. This also displays the conversion rate of final third entries into penalty area entries for Bayern Munich. Conversion Rate 36.5% Positive 7/34 = 20.6% Positive 8/25 = 32% Positive 12/46 = 26.1% Positive 8/75 = 10.7% Positive 14/58 = 24.1% Positive 11/60= 18.3% Final third entries Penalty Area Entries Conversion Rate 54.4% Figure 21.2: Percentage of positive outcomes that come from final third and penalty area entries for Chelsea. This also displays the conversion rate of final third entries into penalty area entries for Chelsea. 53

64 Positive 4/18= 22.2% Positive 9/23 = 39.1% Positive 5/16 = 31.3% Positive 10/64 =15.6% Positive 12/56 = 21.4% Positive 11/70= 15.7% Final third entries Penalty Area Entries - 57 Conversion Rate 30% Figure 21.3: Percentage of positive outcomes that come from final third and penalty area entries for England. This also displays the conversion rate of final third entries into penalty area entries for England. Positive 4/18= 22.2% Positive 11/34 = 32.4% Positive 11/26 = 42.3% Positive 5/60 = 8.3% Positive 14/74 = 18.9% Positive 15/80= 18.8% Final third entries Penalty Area Entries - 78 Conversion Rate 36.4% Figure 21.4: Percentage of positive outcomes that come from final third and penalty area entries for Germany. This also displays the conversion rate of final third entries into penalty area entries for Germany. 54

65 Chapter V Discussion 55

66 5.0 Discussion 5.1 Introduction The purpose of this study was to assess the difference in attacking build up play between domestic and international teams and to evaluate any differences between countries styles of play to see what was most effective. A domestic club was selected from the English and German elite leagues along with their respective national side. This chapter will discuss the findings from this study and consider which team has been most effective in its attacking build up play and why. Due to a lack of literature comparing international and domestic differences in football, results are being compared with relatable literature. 5.2 Possession Origins Table 7 identifies how possession originated for each team in the study and highlights that for all four of the teams an interception was the most occurring method for possession to start. The results for this particular variable indicate that possession originated more for the English sides, Chelsea and England, from open play such as making tackles or intercepting the ball, England made 41.5 intercepts and 11.8 tackles per game and Chelsea made 42.5 intercepts and 12.5 tackles per game, compared to their German counterparts. However, this does not agree with the findings of Wright et al. (2011) which suggested that interceptions accounted for 19% of all goals scored when they studied 1788 attempts at goal in the English Premier league, as the English teams in this current study only scored 18 goals compared to both of the German teams 26 goals combined. The data also shows how the international sides started possession from goal kicks more often per game than the domestic sides, this may suggest that they give their opposition more opportunities at goal than the domestic sides. However, in this study, no information was found that links obtaining possession in a particular way to a more positive outcome. The location of where possession originated for each team also differed significantly. The results highlighted how all of the sides obtained possession a similar number of times in their defensive third, while the domestic sides obtained possession of the ball in the midfield third more so than their respective national teams. However, this does agree with previous studies that highlight the defensive and midfield third as where the majority of possessions originate (Lago-Penas et al., 2012; Tenga et al. 2010). When the area of origin was linked to positive outcomes in this study, the results showed that both Bayern Munich 56

67 (40.7%) and Germany (38.1%) began more often inside their own defensive third which disagrees with Bate (1988) who suggested that teams were more successful when they obtained possession within the attacking third. This may suggest that the German sides will soak up opposition pressure so that when they do win the ball more space is available for them to play into ahead of them. In comparison, both Chelsea (43.4%) and England (53.9%) tended to obtain possession that led to positive outcomes in the wide channels which may imply a common strategy to force opposition wide rather than opening up the pitch and allowing them to play through the middle. Although this does question the work or Armatas et al. (2005) which recognised that the most common area for changes of possession to occur is within the central areas of the opponent half. 5.3 First Attacking Intention This variable refers to the type of pass first played when a team gains possession and can indicate how they intend to attack the opposition, whether this be through direct or possession play. However, there is a distinct lack of literature surrounding the first pass of a possession and how this can lead to positive or negative outcomes. In terms of direction of the first pass, the results of this current study show that all four of the teams use a short first pass at least 48% of the time as a way of retaining possession and beginning an attack. Both Bayern Munich (55.5%) and Germany (55.9%) use the short pass more often than their English counter parts. England struggled consistently with the first pass of the possession and found it being incomplete 9.9% of the time, significantly more when compared to the other international side Germany (4.1%) and their domestic team, Chelsea (4.9%). Similarly, in terms of direction of the first pass, all four of the teams made the intent to move forward the majority of the time. Significant differences were shown when comparing the two international teams, the Mann-Whitney U test identified that England attempted significantly less backwards passes to start a possession than Germany. This contradicts the work of Guai et al. (2003) which stated that if a team is make an effort to score a goal, then they must do so in the most direct manor as possible to utilize space, however, England played in the most direct manor in terms of first pass direction and length, but were the least successful side in terms of goal scoring. Other than England, the remaining three teams all reported very similar results based on first pass direction. 57

68 5.4 Possession Previous studies have highlighted how having longer possessions and having more time on the ball than the opposition can be significant factors in predicting a successful team (Taylor and Williams, 2002). While others have found that there is no link between the two (Bate, 1988; Stanhope, 2001). The number of passes per possession can determine the attacking intent for a team, whether this be through a direct style or possession play. The results from this study show how both England and Chelsea managed 1, 2, 3 or 4 passes per possession more often than both the German sides, this may indicate a more direct style of play as possession was not retained for long. Therefore, with these two teams being the least successful out of the four studied in this research, this concurs with Tenga and Larsen s (2003) findings that, Norway, who used attacks with minimal passes were less effective against a Brazil side who used longer and more elaborate attacks with in excess of five passes. At the other end of the spectrum, three out of the four sides in the study accumulated 39 or more possessions that included 15 or more passes. The only side not to do so was England, who over a six game period only completed 15 or more passes in eight possessions, considerably lower than the rest. A difference this large may have been due to external factors such as opposition influences or playing conditions. Many studies have highlighted possession as a key variable in determining successful and unsuccessful teams, a study by Hook and Hughes (2001) identified that successful teams in Euro 2000 utilised longer possessions than unsuccessful teams. Similarly, Lago-Penas et al. (2010) studied the Spanish La Liga season and concluded that the ability to retain possession is linked to success. These theories, mirror this current study, which found that the two teams with the most possession were the two German sides, Bayern Munich (average 66% possession over the six games) and Germany (58%). However, interestingly, Bayern Munich, who have the highest possession statistics, also have the lowest mean possession time (17 seconds), closely followed by Chelsea (19 seconds). This may indicate some differences between international and domestic styles of play, in that domestic sides wish to have a greater number of shorter length possessions in an effort to repeatedly attack the opposition and not give them opportunities to regroup. These results do however disagree with multiple studies stating that successful teams could keep the ball for longer durations (Bate, 1988; Hughes and Churchill, 2005; James et al., 2004). But in the case of England for this study, does agree with other studies which indicate 58

69 that unsuccessful teams are more likely to have greater possession as they are normally chasing the game to seek a goal (Lago and Martin, 2007, Lago-Penas and Dellal, 2010) on the other hand, Bayern Munich, the second most successful team in the study had a shorter mean possession time than the least successful team in the study, England (20 seconds). Conversely, research from Collet (2013) comparing ball retention success in domestic and international football, stated that international teams have a tendency to have more possession of the ball per game because of the nature of the opposition they are usually playing against compared to domestic teams. Also in this study, the two International sides, Germany (4 th ) and England (9 th ), are ranked relatively highly by FIFA (2016). Having more possession of the ball, however, is only useful if a team uses it in an efficient way to create chances. Lanham (1993) undertook research to compare the number of possession it took for a team to score a goal over a ten-year period between 1981 and He found that teams in general took on average possessions to score a goal. However, this current study highlighted significantly lower ratio of possessions to number of goals scored per team. This research found that over the 1979 possessions analysed for all four teams, a goal was scored on average once every possessions, with Germany being the most efficient, scoring on average every 32.5 possessions, followed by Bayern Munich (42.1 possessions), Chelsea (43.8) and England (78). These considerably lower figures compared to Lanham s 1993 research may display how attacking build up play has differed since then, although, it is more likely that different definitions for possession being won and lost were used between the two studies. However, the results of this study show how the German team are very efficient with their possession and convert chances more often than the other sides. When using Cohen s effect size to compare number of possessions, England had significantly more than Chelsea (d=0.96) and Germany (d=1.04), but were less effective at converting these possessions into goals. The domestic sides had a very similar number of possessions, resulting in a small Cohen s effect size score (d= 0.37), which may indicate a similar style in play between these two sides as they both scored 11 goals across six games that were analysed. 59

70 5.5 Pitch Progression This variable was a good indication as to how well each team used their possessions and how positively they were playing. Significant differences from the Mann-Whitney U test were found between Chelsea and Bayern Munich, when Chelsea would regress two zones (p<0.05), this only occurred for Bayern Munich on three occasions throughout the six matches compared to Chelsea s 18. When comparing England with Germany and Chelsea, significant differences arose where there was no progression or regression. England did not gain or lose any ground on 31.1% of all possessions, greater than any other team in any other category, this may link to their high percentage of lateral and backward first passes, as they showed the least attacking intent when obtaining possession. Germany made progression by three zones significantly more times than England (p<0.05) which can be seen as a considerable portion of the pitch. This progression may stretch the opposition as more of the pitch is being used within a possession, this can result in a team having more chances to score. Having said this, England progressed by five zones on average 3.7 times per game, which is more than the most successful team, Germany. This is surprising given the fact that England used the fewest number of passes per possession, rarely exceeding 15, this may imply that England are much more direct than Germany and after the first pass of the possession, do look to play forward. The English sides were the only teams to regress by three zones, this may link to the frequency in which they attempted to make long passes as they would pass the ball back to defensive areas and then look for long balls to switch play but would not complete them, resulting in negative progression. The German sides on the other hand made positive progressions more often than both of the English teams, with Germany progressing 67.4% of the time and Bayern Munich progressing 71% of the time, showing that more often than not, they would penetrate the opposition. 5.6 Final Third and Penalty Area Entries Linking back to the possession differences between the sides, Bate (1988) found that the greater the number of possessions a team had, the more likely they would be to enter the oppositions final third and therefore create more goal scoring opportunities. However, the results of this study show a disagreement with this statement and indicate that the team with the most amount of possessions (England), in fact, had the fewest number of final third entries. The frequency of final third entries underlined how both Germany and Bayern Munich were able to gain entry into the final third more so 60

71 than the English teams, which again may identify the efficiency of their attacking play. Despite this, there were no significant differences highlighted from the Mann-Whitney U test for the number of final third entries between any of the four teams. The Cohen effect size score identified a high significance when comparing the percentage of left final third entries between Bayern Munich and Chelsea (d=1.12). Hughes et al. (1988) found that successful teams predominantly used the central areas when entering the final areas of the pitch which disagrees with the findings of this study where all four of the teams preferred using the wings in their build up. From the Mann-Whitney U test, significant differences were found when comparing the percentage of right penalty area entries between England and Bayern Munich with Germany (p<0.05), with Germany preferring to use this side 43.6% of the time. Significant differences were also found when comparing the frequency of both left and right penalty area entries between Chelsea and England. Germany (78 times) and Bayern Munich (74 times) entered the penalty area a similar number of times which may show a likeness in the build-up play within German football. On the other hand, England entered the penalty area only 57 times over the course of six games, compared to Chelsea s 105 times, this may explain for their lack of goals scored as around 90% of all goals are scored within the penalty area (Mitrotasios and Armatas, 2014). It also proves Ruiz-Ruiz et al. (2013) findings correct that teams with less penalty area entries will score fewer goals. Chelsea were significantly better at converting final third entries into penalty area entries, doing so 54.4% of the time, compared to Bayern Munich, 36.5%, England, 30%, and Germany 36.4%. This data however may show how Chelsea should have scored more goals than they did and also highlight how Germany and Bayern Munich, who scored 15 and 11 goals respectively, were more clinical when they entered the penalty area. Germany (42.3%) and Bayern Munich (45.5%) created positive outcomes more often when entering the penalty area through the central channel, while Chelsea (32%) and England (39.1%) had most of their positive outcomes when entering the penalty area from the right channel. This may show that the English teams are more effective when using the wings compared to the German teams who have more success from playing directly through the middle. 61

72 5.7 Crosses and Long Passes In the present study, teams attempted on average 13.4 crosses per game and 24.5 long passes per game. The Mann-Whitney U test identified no significant differences when the number of each of the team s crosses and long passes (p>0.05). However, Cohen s effect size did notice some significance. Firstly, it highlighted the fact that domestic teams are much more successful when crossing the ball than their respective international sides, with the domestic teams having a cross success rate both above 30%. However, there are no significant differences in the number of crosses attempted by the four teams per game, with an average range for the four teams just 2.7. the domestic sides may have better success with this strategy as they train together more often than their respective international side, so will therefore have a better understanding on team-mates tendency s when crossing. This does disagree with Lawlor et al. (2004) which found that successful international teams will use more crosses in their playing patterns, while in this study, Germany who were the most successful in terms of goals scored, did in fact attempt the least amount of crosses per game (12.8). Cohen s effect size identified very high significant differences in long passes when comparing Chelsea with both England and Bayern Munich (d>0.8). Chelsea attempted on average 30.2 long passes per game compared to England and Bayern Munich s 23.3 per game and 22.9 per game respectively. The international teams once again had the lowest average number of long passes per game and were also less successful, with England completing 53.6% of long passes and German 51.9%, compared to 55.5% for Bayern Munich and 58% for Chelsea. This once again may link to the fact that the domestic teams will have greater knowledge in what their teammates are capable of so will have more confidence in giving them a tricky long ball. This however does not disagree nor agree with previous research which indicates that successful teams will use more long passes in a game to create goal scoring opportunities (Yiannakos and Armatas, 2006; Hughes and Franks, 2005), as the domestic teams (22) scored the same amount of goals as the international teams (22). Although, this does imply that because the domestic teams attempted more long passes, they should have in fact scored more goals than they did. 62

73 5.8 Possession Outcomes The mean data identifies how both of the English teams, England and Chelsea, gave away possession by an interception more often than their German counterparts. It also shows how the international teams gave away possession more often by being tackled. The Mann-Whitney U test identified just one significant difference, that was the number of possessions ending in being tackled between Chelsea and England. This may suggest that the international teams attempted to dribble with the ball more often than the domestic teams, giving the opposition more opportunities to make tackles. Of the three positive outcomes here, free kick won, penalty won and shot on target, these occurred most often for Bayern Munich, however this was not a common theme among the domestic teams as Chelsea had the fewest positive outcomes. Interestingly, the least successful team in this study, England, created the second highest amount of positive outcomes (11.8). Furthermore, only the German teams won penalties in this particular study, which is surprising considering that Chelsea were the team who entered the penalty area the most. Secondary outcomes were included to take into account anything that happened as a result of one of the initial outcomes, these fell under three variables, corner won, goal scored and thrown in won. All secondary outcomes are positive for the team in possession. Germany recorded on average 14.7 secondary outcomes per game, more so than the other teams, this may reflect their success across the six games compared to the other teams. There were no significant differences found within the secondary outcomes from the Mann- Whitney U test or the Cohen effect size test. This study found that most positive outcomes originated in either the defensive central area or midfield central area for all teams. It was also noticed that both Bayern Munich (40.7% of the time) and Germany s (38.1%) positive possessions usually originated in the defensive third meaning that they played out well from the back, this may link to their superior number passes per possession, indicating a slow, yet effective, build up style. The present study also shows that both of the German team s hade more positive possessions than the English sides, with Bayern Munich having a positive outcome 33.5% of the time and Germany 31.1%. The English team s positive possessions originated more down the wings compared to the German teams, while the international teams had more success when they obtained possession higher up the pitch in the attacking third. The findings of this present study show that the majority of possessions for each team, except Chelsea, finished in the attacking third of the pitch. Chelsea s possessions 63

74 mainly ended in the midfield third (36.7% of the time). Multiple significant differences were identified for this variable, most commonly when comparing England with Chelsea in three out of the four zones of the attacking third. This suggests that despite possession finishing in the final third more often than their respective domestic side, they were unable to convert this into goals, it also implies that Chelsea were more clinical when they entered this zone. England s possessions also tended to finish in the wide channels more so than any of the other teams (46.7% of the time), which is surprising considering their low success rate at crossing the ball. 5.9 Length of Possessions Prior to Goals Scored In the present study, as seen in table 20, the domestic teams average far more passes prior to scoring a goal than the international teams, with Bayern Munich and Chelsea averaging just over 8 passes (8.3 and 8.5 respectively). Whereas the international teams only average around 3.5 passes prior to scoring (3.3 for England and 3.6 for Germany). In this instance, the international teams agree with multiple research concluding that the majority of goals are preceded by a passing sequence of five or fewer (Reep and Benjamin, 1968; Olsen, 1988; Bate, 1988; Hughes, 1990 Garganta et al.,1997). The time in seconds taken to score a goal varied dramatically between the four teams. The possessions proceeding Bayern Munich s goals took on average 29.3 seconds, showing their capability to keep possession and be patient before finding an opportunity to score, therefore taking control over and dictating play (Carling et al., 2005). This disagrees noticeably with most research completed, for example, Garganta et al. (1997) found that over half of all goals scored occur from possessions lasting less than 10 seconds, while Carling et al. (2005) learnt that the majority of goals scored follow short passing sequences and possessions lasting between 6 and 15 seconds. This is closer to the times recorded by Germany and England who took on average 14.1 and 14 seconds, respectively, to score a goal. Chelsea took slightly longer with an average possession of 16.5 seconds to score a goal, which agrees with Redwood-Brown s (2008) statement English teams use a short build up time before scoring to play more direct, which Franks (1996) reported leads to a higher scoring ratio. 64

75 5.10 Implications of Findings The results of this study allowed for questions to be answered regarding the difference between international and domestic football and if there were any reasons why English teams were less successful than their German counterparts. The results of this study will hopefully bridge any gaps in literature that have not previously been addressed. Pitch progression was a variable that had not previously been widely analysed to see if this had an effect on build up play efficiency. Previously, there had also been very little research comparing the differing styles in international and domestic football and to see if an international side can learn from their respective successful domestic team. The findings from this study may allow for coaches to look at the tendencies of domestic or international teams and find weaknesses in performance to adjust playing styles and strategies (Hughes and Franks, 1997). 65

76 Chapter VI Conclusion 66

77 6.0 Conclusion 6.1 Main Findings The aim of this study was to identify if any differences could be seen when comparing attacking build-up play within international to domestic football, and whether there were any differences between different nations styles of play. The main findings of this study show that domestic teams had a greater number of possessions per game showing their greater ability to turn over possession. Domestic sides were also able to use more passes prior to scoring a goal, perhaps indicating their comfort when in possession of the ball because of the greater knowledge of their surroundings. This was perhaps telling in the way that they were more successful than their respective international teams at crossing the ball and completing long passes. In terms of differences between nations, this study found that the German sides had greater amounts of possession and used it more effectively, by beginning attacks from deep and making good progression up the pitch with most attacks, which would normally lead them to making more final third entries than the English teams. The English sides however had a tendency to use a more direct style of play by beginning possessions with longer passes than the German sides and by using a short attacking build up time with fewer passes per possession to take the opposition off guard. H1 can be accepted as significant differences were found in all bar three of the performance variables within the study. H2 can be rejected as the least successful team, England, were able to keep possession of the ball for longer than two out of the other three teams. H3 can be accepted as the team who scored the fewest goals in this study, England, played with a much more direct style by using fewer passes per possession and attacking over a short period of time. H4 can be accepted as Germany were the highest scorers in this study with Bayern Munich coming joint second, meaning that their positive outcomes were more important. In answer to the initial research question, there are differences between international and domestic football, however, this does not necessarily mean that one plays with a better style of build-up play than the other. There are on the other had different reasons which can lead us to suggest that the German style of attacking build up play is more effective than the English style as more goals were scored in similar circumstances. 67

78 This research does also not agree with most research suggesting that a more direct style of play can be correlated to greater success (Reep and Benjamin, 1968; Olsen, 1988; Bate, 1988; Hughes, 1990; Garganta et al., 1997) as mixed results were found, suggesting that they can both be effective. 6.2 Future Research Further research into this area could perhaps initiate more specific analysis into the difference in goal scoring patterns between domestic and international teams rather than just how teams use a direct or possession style build up. Hopefully, this study has given some insight as to which attacking styles are more effective and bring about greater, not just quantity but quality of, opportunities. Further considerations may take into account different match variables such as the difficulty of the opposition, score line effects, weather and how each of these can alter a team s attacking build-up style. 68

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87 Appendices

88 Appendix A Percentage agreement test results. Possession Origins Origin Area First Pass Direction A1

89 First Pass Length Pitch Progression Possession Outcomes Outcome Areas A2

90 Final 3 rd Entries Penalty Area Entries Passes Per Possession Comp Ag 38 Total Obs 57 % Agree 67 A3