Cardiff School of Sport DISSERTATION ASSESSMENT PROFORMA: Empirical 1

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1 Cardiff School of Sport DISSERTATION ASSESSMENT PROFORMA: Empirical 1 Student name: Gregory Adlam Student ID: ST Programme: SES Dissertation title: Defensive performance variables and their effect on team success within elite football Supervisor: Ray Ponting Comments Section Title and Abstract (5%) Title to include: A concise indication of the research question/problem. Abstract to include: A concise summary of the empirical study undertaken. Introduction and literature review (25%) To include: outline of context (theoretical/conceptual/applied) for the question; analysis of findings of previous related research including gaps in the literature and relevant contributions; logical flow to, and clear presentation of the research problem/ question; an indication of any research expectations, (i.e., hypotheses if applicable). Methods and Research Design (15%) To include: details of the research design and justification for the methods applied; participant details; comprehensive replicable protocol. 1 This form should be used for both quantitative and qualitative dissertations. The descriptors associated with both quantitative and qualitative dissertations should be referred to by both students and markers.

2 Results and Analysis (15%) 2 To include: description and justification of data treatment/ data analysis procedures; appropriate presentation of analysed data within text and in tables or figures; description of critical findings. Discussion and Conclusions (30%) 2 To include: collation of information and ideas and evaluation of those ideas relative to the extant literature/concept/theory and research question/problem; adoption of a personal position on the study by linking and combining different elements of the data reported; discussion of the real-life impact of your research findings for coaches and/or practitioners (i.e. practical implications); discussion of the limitations and a critical reflection of the approach/process adopted; and indication of potential improvements and future developments building on the study; and a conclusion which summarises the relationship between the research question and the major findings. Presentation (10%) To include: academic writing style; depth, scope and accuracy of referencing in the text and final reference list; clarity in organisation, formatting and visual presentation 2 There is scope within qualitative dissertations for the RESULTS and DISCUSSION sections to be presented as a combined section followed by an appropriate CONCLUSION. The mark distribution and criteria across these two sections should be aggregated in those circumstances.

3 CARDIFF METROPOLITAN UNIVERSITY Prifysgol Fetropolitan Caerdydd CARDIFF SCHOOL OF SPORT DEGREE OF BACHELOR OF SCIENCE (HONOURS) SPORT & EXERCISE SCIENCE Defensive performance variables and their effect on team success within elite football (Dissertation submitted under the discipline of Performance Analysis) GREGORY ADLAM ST

4 DEFENSIVE PERFORMANCE VARIABLES AND THEIR EFFECT ON TEAM SUCCESS WITHIN ELITE FOOTBALL

5 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). Word count: 9,106 Name: Gregory Adlam Date: 16/03/2014 Certificate of Dissertation Supervisor responsible I am satisfied that this work is the result of the student s own effort. 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.

6 Table of Contents Page ABSTRACT i CHAPTER 1: INTRODUCTION The development of performance analysis in football Performance variables and key definitions Limitations and delimitations 3 CHAPTER 2: LITERATURE REVIEW Football Methodology Performance analysis feedback in football Aims and objectives Hypotheses 9 CHAPTER 3: METHOD Subjects Equipment Notation system Variables Procedure Expert opinion Data analysis 17 CHAPTER 4: RESULTS 18 CHAPTER 5: DISCUSSION The results The equipment The notation system Opposition Effects Attacking considerations Match status Practical applications Theoretical applications 29

7 CHAPTER 6: CONCLUSION 31 REFERENCE LIST 32 APPENDIX 35 Appendix 1 Ethics Status 35 Appendix 2 List of match footage 36

8 List of Tables Page Table 1 Operational Definitions 14 Table 2 Successful Teams 18 Table 3 Unsuccessful Teams 18 Table 4 Successful and Unsuccessful Teams 19 Table 5 Win/Draw/Loss 19 Table 6 Total Frequencies 21

9 List of Figures Page Figure 1 Code window 12 Figure 2 Labels tree 12 Figure 3 Timeline 12 Figure 4 Matrix 13 Figure 5 Successful vs Unsuccessful (%) 20 Figure 6 Successful vs Unsuccessful (Frequency) 20

10 Abstract This study compares the defensive performance of the most successful and least successful teams within the Euro 2012 football competition. 20 matches were analysed, 3 matches from the perspective of each of the 4 successful and 4 unsuccessful teams. The performance analysis software Studiocode was used to execute the match analysis. The data were broken down into 11 defensive performance variables. Some variables were quantified as a percentage of successful outcomes from the variables and some were quantified as frequencies. Independent t-tests were employed to determine whether significant differences (p<0.05) existed between successful and unsuccessful teams. The analysis further addressed any differences existing between varying match outcomes (win/draw/loss). It was concluded that successful and unsuccessful teams demonstrated significantly different frequencies or percentages of 5 of the 11 variables. Successful teams performed higher success percentages in clearances (32±4.6) (p<0.05) and overall open defensive situations (95±2.2) (p<0.05). Successful teams spent less time defending (29±4.5) (P<0.05), achieved more interceptions (12±2.3) (p<0.05) and conceded fewer corners (4±1.3) (p<0.05). The only significant difference found between varying match outcomes was that games drawn, recorded higher success rates of overall defensive situations than games that were lost or won (p<0.05). No other variable was found to significantly differ in this analysis. i

11 1 INTRODUCTION 1.1 The Development of Performance analysis in football The requirement for the development of performance analysis has grown greatly since the 1970s (Hughes, 2003). Most, if not all professional football clubs employed video analysts (James, 2006). As the popularity of video analysis increased amongst professional football, so did the competition to use it more effectively. A study by Olsen and Larsen (1997) in Norway employed a variety of techniques of match analysis. Olsen and Larsen concluded that notational analysis enabled Norway to maximise its limited resources, overcome its extreme weather conditions and maximise playing population to a degree that allowed them to defeat both England and Holland in qualification for the 1994 World Cup, they also defeated Brazil in the 1998 World Cup. Early use of performance analysis provoked scepticism toward the discipline. Understandably a coach may wonder what performance analysis could offer performance that they couldn t. Franks and Miller (1986, 1991) suggested how a coach s recollections of performances immediately following the event produced inaccurate and unreliable data. Hughes and Franks (2004) suggested that controversial and emotive events within a performance distorted a coach s perception of performances. Furthermore, Hughes and Franks (2004) found that coaches were unable to monitor and analyse behaviours occurring in different areas of the playing field at the same time. Video of past performances were therefore necessary in order to produce accurate data of the 2012 Euro tournament. Specific software has been designed for the purpose of bridging the gap between computerised observation and hand notation. Software such as Studiocode and Nacsport enable the analyst to analyse the video using computerised notation. The work in Norway may have inspired a quest to maximise the effectiveness of performance analysis in soccer. The success that Olsen and Larsen attributed to performance analysis would have made the discipline very attractive to rival professional football teams. Many areas were explored and it was found that key to maximising the effectiveness of performance analysis was the clarity of communication through feedback (Maslovat & Franks, 2008). Performance analysis feedback provides the avenue for information to travel from the performance to the coach and athlete. The theoretical 1

12 background for methods of feedback showed a clear evolution of models from Winkler (1991) to O Donoghue (2007). Key to effective feedback is clear information; key to clear information is relevant data collection. Relevant data collection can only be accomplished if the variables chosen are agreed upon as the most salient to the situation. In order to achieve applicable data through computerised notation, the performance variables used must to some extent explain the outcome. The critique of measurable performance variables is a process key to any performance analysis study. Reviews of the existing football performance analysis studies lead to the conclusion that a few variables were commonly employed and regarded as an indicator of performance (Shafizadeh, Taylor & Lago-Penas, 2013; Jones, James & Mellalieu, 2004). The cause for a limited number of variables being employed across multiple studies lies within the aims and objectives of each study. No two studies appeared to have a specifically common aim and therefore employed different variables. The majority of performance analysis studies addressed attacking football, perceiving defensive performance as a less influential element to match outcomes. The lack of defensive football literature went some way to justifying the aims of this study. Relevant football performance variables have been studied and developed over recent years with the aim of furthering understanding of which characteristics discriminate the successful teams from average and unsuccessful teams (Castellano, Casamichana & Lago, 2012). These indicators have been applied to several studies, mostly focussed on the attacking aspects of football performance. Few studies therefore attempt to describe the impact of defensive performance on success. Some studies review defensive parameters within a wider scoped approach to football analysis. These studies do not focus on defensive indicators with much specificity, justifying a need for defensive specific notational analysis. This may lead to the understanding of the magnitude of contribution made by defensive performance to match outcome. By examining the saliency of defensive performance variables with regards to success, coaches may utilise their training time more efficiently by focussing on the defensive areas of performance that may have been proven to contribute to match outcomes. The problem existed that defensive performance variables remained relatively unexamined. A study into analysing defensive variables from a recent elite football tournament may go 2

13 some way toward prioritising these variables with intent to inform future football studies. Elaborating on the prioritisation of defensive variables, limited literature exists to suggest which variables may discriminate successful and unsuccessful performances and the magnitude of the contribution defensive performance makes to match outcomes 1.2 Performance variables & key definitions For the purpose of this study, positive performance variables represent behaviours that have an observable facilitative effect on overall performance. Negative performance indicators have an observable detrimental effect on performance. A study focussing on defensive contributions to team success should analyse both of these categories of performance. By analysing both positive and negative variables, a study would build a more detailed illustration of a defensive performance. This would increase the probability of identifying the variables most likely to influence match outcomes. Finding the key performance indicators has potential to enable coaches to prioritise training interventions relative to their overall training aims. Successful teams were determined by their progress in the Euro 2012 international football tournament and unsuccessful teams were identified as the lowest finishers in their respective groups. Performance variables were simply the categories under which behaviours of a football team were organised and quantified. Performance variables are commonly confused with performance indicators. A performance indicator is the term used to describe a behaviour which has been proven to have an effect on the match outcome, as no evidence has been found suggesting that the variable selected at this stage have a significant effect on match outcomes, they shall be referred to as performance variables. 1.3 Limitations and delimitations There were a number of limitations that could disrupt the results of this study. Firstly, not all factors that contribute to elite performance can be quantified, and not all factors may be evident in match films. A great limitation within this study is a lack of background knowledge of the players and staff involved. Factors such as form, psychological issues, illnesses and personal disagreements may have contributed to the limited performance of 3

14 highly ranked teams. This study assumed that the sample of matches observed would be representative of the general level of performance executed by the respective teams. Although performance analysis may have described the performances observed, other unquantifiable factors may have existed that manipulated the observed performances making them unrepresentative of that team s general level of performance. For example, Netherlands were identified as an unsuccessful team, regardless of their superior world ranking to other teams in the unsuccessful group. The significance of this study lay in the prioritisation of defensive performance variables and the establishment of the magnitude of the contribution of defensive variables to success. Secondly, the sample size of the matches observed, although possibly representative of the general performance across the whole Euro 2012 tournament were not representative of the level of performance given across a greater time structure. This limits application of the results of this study when predicting future levels of performance. Thirdly, opposition effects should be considered when drawing conclusions from the analysis of a sample of performances (O Donoghue, Mayes, Edwards & Garland, 2008). It was suggested that patterns exist within performance variables when teams of different ranks compete. Thus, a limitation of this study is that the resources are not available to accurately analyse the opposition effects due to the number of factors that contribute to performance. As performance is a result of so many constituents, quantifying the current form and ranking each team would be inaccurate and would lead to inaccurate assumptions being made about how each team should perform against their opponents. 4

15 2 LITERATURE REVIEW 2.1 Football Football is a source of economic benefits (Hoffmann, Lee & Ramasamy, 2002), national pride and entertainment, for these reasons and many others, the international competition to be the best spirals into new heights every year, brought about by generally improved physical fitness and skill of the athletes. The top clubs have financial incentive to buy and develop the best talent in the world (Reilly, Bangsbo & Franks, 2000). How do they identify this talent and how do they optimise their training to raise the best talent possible? Possibly the most popular sport in the world, football is a demonstration of skill, fitness and athleticism, but how do you measure it? Performance analysis has proved popular amongst professional football for answering these questions. Football presents a challenging application for performance analysis due to the complex tactics and techniques involved. The high level of physiological demands on individual football players is a requirement for success (Stølen, Chamari, Castagna & Wisløff, 2005). When fighting for possession, running off the ball, dribbling tackling, counter-attacking, overlapping, jumping for headers and long passes, every player needs to be able to produce a high standard at any instance throughout the whole game (Njororai, 2010). Mohr, Krustrup and Bangsbo (2003) used video based match analysis to explore the physical and technical demands of football. It was found that top level players spend significantly more match time enduring high intensity activity than moderate players, suggesting that high levels of fitness are a predisposition for elite football performance. Further findings demonstrated the variations in physical exertion across individual playing positions and seasonal periods. However, little reference was made to the match status, which may influence physical effort. For example, if a team is winning by a large margin with 10 minutes remaining in the match, less effort is likely to be made due to a lesser motivation to score and a lesser challenge from the demotivated opposition. Over a full season, this could produce significantly skewed data in top-level teams who regularly demonstrate success. Success of football teams emerges from a combination of many factors. However performance is divided mostly into technical and tactical aspects. Technical, closely linked with fitness and practice is a requirement for top level football, whereas tactical factors vary 5

16 between leagues, teams and individual matches. Studies into tactical execution are scarce as pre-existing relationships between coach and analyst must exist to know what to look for when analysing tactical performance. Tenga, Holme, Ronglan and Bahr (2010) attempted to assess the effect of playing tactics on goal scoring. Through investigating opponent interactions in Norwegian professional football, Tenga et al. (2010) aimed to establish a valid analysis of team match performance. It was concluded that differences existed in the probability of scoring between varying tactics, however only observable when playing against unbalanced defence. However, this conclusion could not be drawn when competing against balanced defences so lacked applicability in top-level professional football. A more recent study into Brazilian first division football (Moura et al., 2013) used a combination of recording methods to collect team dynamics and tactics data. Modern automated player tracking methods were used to monitor player motion. The study used a complex technique called spectral analysis to calculate the surface area and spread of the team. This information helped describe the organisation of the team and how offensive and defensive units interacted. Successful teams demonstrate a combination of structure and improvisation that allows for purposeful individual movement whilst maintaining team shape (Daniel, 2003). Daniel also states that successful teams regain possession in the other team s half. For the purpose of this study that would result in decreased time spent defending. Furthermore, the winning of individual battles and anticipation of opportunities to intercept the ball are considered elements of a successful team. 2.2 Methodology A performance indicator is a selection, or combination, of action variables that aims to define some or all aspects of a performance (Hughes & Bartlett, 2002). The eight Key performance indicators highlighted by Hughes and Bartlett (2002) as relevant to invasion games, such as football, included three defensive performance indicators, suggesting the importance of defensive play on the match outcome. This paper went on to underline the key aspects to a thorough indicator relative to a wide variety of sports indicators. As Hughes and Bartlett (2002) suggested indicators specific to sport types and not specific to individual 6

17 sports, the applicability of these conclusions may suffer due to subtle differences in different invasion games. The indicators suggest should have been tried and tested in each sporting environment before they could be regarded as applicable. Hughes and Franks (2004) claimed that analysis is required by coaching in order to inform information transfer to athletes and illicit observable changes in performance. Historically, coaching interventions have been informed by, often subjective, observations made by the athlete, proving unreliable and inaccurate, thus the need for video based and quantitative performance analysis. Hughes and Franks (2004) highlighted several aspects where notational analysis vastly improved the accuracy and availability of feedback. However make little reference to the process of establishing reliable parameter definitions and therefore lacking in applicability to a competitive sporting environment. Carling and Court (2013) stated how the dynamic nature of football often results in incomplete and inaccurate recollections of events within a performance. Notational analysis allows critical appraisal of every aspect of performance in great objective detail. However, Drust, Atkinson and Reily (2007) demonstrated that before data from analysis could be presented as feedback, the method of observation and analysis must be reliable, accurate and valid. The nature of the performance analysis data occurred as a direct result of the interpretation of previous data, demonstrating the cyclical feedback-performance process suggested by O Donoghue (2006). This process, due to the nature of football, proved difficult. The numerous possible outcomes and events that occur regularly in elite football make defining events often unreliable. This highlighted a need for consultation and repeated experimentation when attempting to categorise a number of events under one variable. Often, indicators were defined based on the success of the outcome. Carling and Court suggested twenty key performance indicators; however, this gave little focus to defensive indicators of performance. 2.3 Performance analysis feedback in football Relative to this study, feedback within a football coaching environment is crucial. The information produced by this study would be ineffective without the appropriate communication. Without the proper means of returning the information to the coaches and players with the desired effect, performance analysis will lack any impact on the players, 7

18 staff and resultantly the matches and success. The aim of performance analysis is to improve performance, so, without feedback; there would never have been a niche for the current study and other studies like it. Pre-requisites for optimal training exist in the combination of performance analysis data and effective feedback methods (O Donoghue, 2006; Winkler, 1991; Franks, 1997; Jenkins, Morgan & O Donoghue 2007; Mayes, O Donoghue, Garland & Davidson, 2009). In order for the findings of this study to impact competitive football, the method of data presentation must be addressed. The method for presenting data is widely debated and is highly dependent on individual coaching and managing styles as well as the behaviour and predispositions of the players in question. This topic has attracted the attention of many studies and has developed greatly in the past 25 years. O Donoghue s (2006) dynamic model of performance analysis follows a cyclical pattern, proposing separate cycles based on the positivity of the feedback. O Donoghue suggested positive feedback should be included into motivational presentations made to individual players, boosting individual confidence. Negative feedback should be presented to the coach directly, allowing the coach to select the correct interventions, informed by their relationship with the players and a number of other external factors. O Donoghue suggested that in order to minimise the negative effects of negative feedback on individuals, the data should be prepared and presented to the whole team and focus should lie in the modification of training. O Donoghue s functional model of performance analysis combines the real time (observed) and delayed (footage) analysis and addresses the development of this data into presentable feedback. It is suggested that observed analysis and video obtained data play equal roles when contributing to the effectiveness of the performance analysis process. Without proper feedback, the performance analysis process would have little influence on performance at an elite level. This illustrates the importance of theory-based and effective feedback. As without an effective feedback system to the players, the performance analysis process would be in vain. 8

19 2.4 Aims and Objectives The present study aimed to analyse the defensive performance variables of elite European International football teams whilst they competed in the 2012 Euro tournament. The study then aimed to identify the significant differences between successful and unsuccessful teams and go on to find the differences between the matches won, drawn and lost. Through this, the study aimed to inform coaches of elite football clubs of the impact defensive performance had on match outcomes and how successful teams defend compared to unsuccessful teams. 2.5 Hypotheses The initial hypothesis for this study was that a there would be a significantly higher frequency for positive defensive performance variables for successful teams and a significantly higher frequency of negative performance variables for the unsuccessful teams. Further hypotheses predicted a significantly greater frequency of positive variables for matches with successful outcomes and significantly a greater frequency of negative performance variables for matches with unsuccessful outcomes. Also, it was predicted that defensive most defensive variables would demonstrate a small effect on match outcomes However, the frequency of time spent defending would be significantly greater for matches lost and drawn. 9

20 3 METHOD This study aimed to objectively quantify defensive soccer performance. Computerised notation was used to analyse the Euro 2012 International soccer tournament. The study aimed to also identify significant differences in defensive performances between successful and unsuccessful teams. 3.1 Subjects This study used video footage of the Euro 2012 international tournament, including eight teams, the highest four finishers (Spain, Germany, Italy and Portugal) and the most unsuccessful in the group stages (Poland, Netherlands, N. Ireland and Sweden). The video footage used was available in the public domain. No individuals were identified within the data presented. 2 matches were analysed, 3 matches from each of the 4 successful and 4 unsuccessful teams. In the interest of comparability only the 90 minutes of each match was coded (0-45 minutes and minutes). 3.2 Equipment The equipment used in this study consists of video coverage (see appendix 2) of the Euro 2012 international soccer tournament saved on an external hard drive and Studiocode software on a Mac desktop computer. Microsoft Office Excel 2007 software was used for data analysis and statistics. 3.3 Notation System Background research was conducted on the saliency of key defensive performance variables before a computerised system for data collection could be designed. Once the list of variables had been confirmed, Studiocode software was utilised to design a code window (Figure 1). A code window contains buttons and links that enable the user to enter data into a timeline (figure 3). A timeline presents the frequency, duration and category of the variables and associates them with the video segment within the time frame of the buttons activation. Only activation links were employed in this system and are visible in Figure 1 as lines connecting buttons together. Activation links activate all buttons connected to the initial button via the activation link. Variable definitions, button placements and links were 10

21 refined as a result of the 2 pilot studies completed for this method. A pilot study consisted of a single operator coding the same footage twice and calculating the correlation coefficient between the two sets of data produced. The initial system produced 95% intraoperator agreement, hence only minor adjustments were made. 95% agreement was considered acceptable as the system included a manually operated timed variable with a degree of precision too high for a human to record consistent times over several trials. The aim of the adjustments was to make the system more user friendly, to minimise the need for subjective decisions and minimise the frequency of manual operations through useful links. The final system included 11 performance variables, as shown in Table 1. Lapsed buttons were used for all variables except for defending time. As defending time can be ongoing, a continuous button was required. Each variable had 2 possible outcomes, positive and negative, which were used to determine the percentage success of the performance of each variable. This method of converting frequencies into percentage success and time spent defending into percentage of match time standardised the data. Through doing this the data was more comparable and therefore easier to draw conclusions from. The pilot tests further assisted the development of the notation system through identifying the grey areas that appeared that were not covered by the initial operation definitions. This enabled the definitions to evolve to successfully categorise all the relevant behaviours observed during the study. In order to produce a percentage success for each of the relevant variables a feature of Studiocode called Labels was employed. Labels are optional add-ons to each event recorded and are edited through the window seen in Figure 2. For example, if aerial battle was activated it was prepared that the labels positive or negative were available for use. Should the team have won the aerial battle, the positive label would have been added to the event. The label function can be seen in action in Figure 3. Where the events on the timeline had positive outcome or negative outcome visible inside them, that event has already been labelled. The frequencies of the labels are presented by the software in the form of a matrix (Figure 4). The matrix is a grid which lists the variables and breaks down their frequencies into the respective labels, enabling the system to automatically calculate the frequency of successful and unsuccessful performances of a specific variable. 11

22 Figure 1 the code window with exposed exclusive links Figure 2 the labels tree Figure 3 Example timeline segment with labels feature in use 12

23 Figure 4 the matrix window displaying an example of the breakdown of variables 3.4 Variables Due to the dynamic nature of professional football, the categorisation of events proved somewhat unreliable and must therefore include an element of professional opinion. Professional opinion, although more unreliable and less accurate, provides the most justifiable method for categorising events that are not easily defined and coded. 11 defensively oriented performance variables were coded for, enough information to establish detailed profiles of defensive performances yet not enough to cause information overload and thus decreasing the probability of user error. Furthermore, information overload causes over complicated data analysis procedures and produces non-applicable and irrelevant data, reducing the efficiency of a system and the usefulness of the data. 13

24 Table 1 Operational definitions for the variables used in this study Variable Definition Value Defending time The duration of phases of play where the defending team are required to act in response to attacking play from the opposition Percentage of 90 minute game time. Open defensive success When a defensive phase ends for any other reason than a shot on goal is conceded. A shot on goal is defined as an attacking player making contact with the ball with the intention of scoring a goal, and the shot resulting in either a save from the keeper or a goal. If the shot is off target, then defensive success is recorded. Percentage of defensive situations where no shot was conceded. Clearance Success A clearance occurs when a defending player kicks the ball away from an area where there is danger of conceding a shot on goal to an area where there is very little or no danger of conceding a shot on goal. A successful clearance is when Percentage of clearances which maintained possession (did not give possession to the opposing team) 14

25 a clearance is performance and the defending team maintain possession. Free kick won in own half When the referee awards a free kick to the defensive team during defending time Frequency over 90 minutes Interception Aerial success A defensive player deflects the ball away from its intended target when an attacking player has kicked the ball with the intention of giving control of the ball to a fellow attacking player. Where a ball is contested by players of opposing teams at a height where only the heads are used Frequency over 90 minutes (Should the interception win possession or not the interception counts as this would still slow or disturb an attack. Should the interception somehow improve the pass, this would be considered a negative defensive act and would therefore not be counted as an interception) Percentage of aerial battles that resulted in the maintenance of possession. Off sides won Referee stops play for an offside, signalled by the linesman Frequency over 90 minutes Corner conceded Referee awards a corner to the attacking team Frequency over 90 minutes 15

26 Fouls committed Referee awards a free kick to opposition Frequency over 90 minutes Yellow Card Referee issues a yellow card to a defensive player Frequency over 90 minutes Red Card Referee issues a red card to a defensive player Frequency over 90 minutes 3.5 Procedure Each game was coded using the timeline function of Studiocode software. A single operator coded each game from the perspective of the appropriate teams. As the playback function of the Studiocode software allowed the operator to pause and rewind of footage, any operational errors were immediately reviewed and corrected. Each timeline was saved and an instance frequency report was exported into a Microsoft Excel file where data analysis occurred. 3.6 Expert opinion Expert opinion is the subjective view of an experienced observer which is most likely to successfully categorise events that fall into grey areas or are hard to define. For example, during pilot tests from this study it was concluded that often the defending team did not have possession but also there was no threat to their goal, these phases of play were labelled attacking pressure. Attacking pressure occurred when a team without possession were acting as though defending but the key difference was the location on the pitch. Attacking pressure occurred within the opposition half, when the defending team s attackers were putting pressure on the opposing defenders. As it was decided that there was no defensive threat during these phases of play, any defensive variables observed during these phases were not considered within a description of a defensive performance. The transition from attacking pressure to defending time was often a drawn out transition with play slowly moving across the pitch from one half to the other often with several 16

27 backward passes. It was decided, that defensive time would begin when the opposition s possession brought the ball over the halfway line and continued travelling towards the defending team s defensive 3 rd. This proved consistent enough to produce good reliability within the pilot tests. 3.7 Data Analysis The raw data (exported frequency reports from the Studiocode timeline software) was organised in Microsoft Excel spreadsheet format. The mean frequencies and percentages and their standard deviations for the successful 4 and unsuccessful 4 teams were calculated. The significance of the differences was determined through multiple independent t-tests. The differences analysed were between average percentages and frequencies of successful and unsuccessful teams. Further analysis examined the differences between matches drawn, won and lost by testing the differences between the mean data for each outcome (Draw against Lost, Draw against Win and Win against Lost). This significance test was chosen due to the parametric nature of the data and the independence of the data sets. The data was deemed parametric as assumptions had to be made concerning the parameters of the population the data was sampled from. For the statistical results to be applicable to the whole international football population, these assumptions must be made. The variables with significant differences between successful and unsuccessful teams were analysed to identify the significance of the differences. Once the null hypothesis had been rejected, Cohen s D effect sizes was used (Cohen, 1988). The effect size statistics were added to quantify the magnitude of the significance differences between successful and unsuccessful teams. Further analysis was carried out to compare the observed performance variables between games that were won, drawn and lost, to highlight the contribution of defensive performance to match outcomes. Effect size results would be interpreted on a scale where ES=0.2 is a small effect, ES=0.5 is a moderate effect and ES=0.8+ is a large effect (Cohen, 1988). d= Cohen s d, = mean of group 1 = mean of group 2 =standard deviation of group 1 = standard deviation of group 2 17

28 4 Results Table 2 Average percentages and frequencies recorded for each successful team over all 3 of their respective games (S=Successful team) (mean ± standard deviation) Variable S 1 S 2 S 3 S 4 Defending Time 27±1.7 34±9.3 30±6.3 23±7.3 Tackle Success 59±7.4 56± ±5.6 70±11.3 Clearance Success 35± ± ±9.2 26±14.8 Open Defensive 97±3.7 93±4.5 97±3.2 94±1.9 Success Aerial Success 43± ±13 47± ±16.7 Off-sides Won 2±1.2 1±1.7 1±0.6 1±1.2 Yellow Cards 1±0.6 2±1.2 3±1.2 1±1 Red Cards 0±0 0±0 0±0 0±0 Free Kicks Won 8±5.5 7±2.1 7±0.6 8±4.4 Corners Conceded 4±1.7 6±5.8 4±2.1 4±5.5 Interceptions 12±3.8 14±6.4 12±4 8±5.9 Table 3 Average percentages and frequencies recorded for each unsuccessful team over all 3 of their respective games (U=Unsuccessful Team) (mean ± standard deviation) Variable U 1 U 2 U 3 U 4 Defending Time % 35±4.6 43±6.8 37±5.8 31±6.9 Tackle Success % 63± ± ± ±13.6 Clearance Success % 22±5.3 21±6.6 34± ±8.5 Open Defensive Success 95±3.8 85±6.9 93±1.2 88±3.3 % Aerial Success % 54± ± ±8.2 56±25.5 Off-sides Won 2±1.2 3±1.5 1±1 3±1.7 Yellow Cards 2±2.5 3±1 2±0 2±0.6 Red Cards 0±0.6 0±0.6 0±0 0±0 18

29 Free Kicks Won 7±3.6 7±4.4 5±0.6 4±1.5 Corners Conceded 5±1.2 9±3 5±2.9 6±1.5 Interceptions 9±7.6 9±3.6 10±6.7 7±2.5 Table 4 Mean frequencies and percentages for Successful and unsuccessful teams over all games Variable Successful Unsuccessful Defending Time % 29±4.5 37±4.8 Tackle Success % 64±8.1 64±2.2 Clearance Success % 32±4.6 24±6.6 Open Defensive Success % 95±2.2 90±4.4 Aerial Success % 55± ±6 Offsides 1±0.7 2±0.9 Yellow Cards 2±0.7 2±0.6 Red Cards 0±0 0±0.2 Free Kicks Won 7±0.9 6±1.3 Corners Conceded 4±1.3 6±1.9 Interceptions 12±2.3 9±1.6 Table 5 Mean frequencies and percentages for games won, drawn and lost over all games Variable Won Drawn Lost Defending Team % 31± ±6.3 35±7.1 Tackle success % 60± ± ±11.7 Clearance Success % 8± ± ±10.3 Open Defensive Success % 94±2.6 98±1.4 90±4.6 Aerial Success % 55±8.5 54± ±25.2 Offsides 1±1.2 1±1.4 2±1.4 Yellow Cards 2±1 2±1.3 2±1.2 19

30 Red Cards 0±0 0±0.4 0±0.3 Free Kicks Won 6±2.8 7±3.6 7±3.1 Corners Conceded 6±4.7 5±1.5 5±3.3 Interceptions 10±5.4 11±4.4 10± * 80 % ** * S U 0 Defending Time Tackle success Clearance success open defensive success Performance Variable areial success Figure 5 Average percentages for successful and unsuccessful teams for the percentagebased performance variables (S=Successful U=Unsuccessful) (* = p<0.05, **=p<0.01) Frequency * * S U 0-2 offsides yellow cards red cards free kicks won in own half corners conceded Interceptions Performance Variable Figure 6 Average frequencies for successful and unsuccessful teams for the frequencybased performance variables (S=Successful U=Unsuccessful) (* = p<0.05, **=p<0.01). 20

31 Table 6 Total frequencies for successful and unsuccessful teams over all games Variable Successful Unsuccessful Yellow Cards Red Cards 0 2 Offsides Free Kicks Won Figure 1 and figure 2 show the comparison between successful and unsuccessful teams. It was found that unsuccessful teams spent a significantly greater percentage of their match time in defending (p<0.01) with a moderate high effect size (ES=0.65). Teams who finished in the top 4 places averaged a significantly greater clearances success (p<0.05), with a moderate effect size (ES=0.58), and a significantly greater open defensive success percentage (p<0.05), with a moderate effect size (ES=0.58). However, there were no significant differences found in tackle success or aerial success between successful and unsuccessful teams. Unsuccessful teams conceded significantly more corners than successful teams (p<0.05), with a moderate effect size (ES=0.52) and top 4 teams managed to average a significantly greater frequency of interceptions than the bottom 4 teams (p<0.05) with a moderate-high effect size (ES=0.6). Analysis of variables between matches won, drawn and lost revealed only that games where the team in question drew the match averaged significantly greater open defensive success than games that were won (p<0.05) (ES=0.7) and games that were lost (p<0.05) (ES=0.76). There was no significance between the open defensive success percentages of games that were won and lost. No further significances were found, however, only 2 red cards were issued, both to unsuccessful teams. Furthermore, unsuccessful teams accumulated more yellow cards (27) overall than successful teams (20) however also managed to win more total off-sides (27) than successful teams (16). Successful teams won more free kicks in their own half (89) compared to unsuccessful teams (71) as shown in Table 6. 21

32 5 Discussion The current study set out to determine the defensive performance variables that discriminate between successful and unsuccessful teams. In this sense, significant findings are existent, quantifying some of the defensive differences between successful and unsuccessful teams. Further analysis aimed to differentiate between the defensive performances of games won, drawn and lost. Although little significance was found, this describes to some extent the role defensive performance plays in determining the outcome of an elite football match. 5.1 The Results The current study found that successful teams defended for significantly less time than unsuccessful teams agreeing with the conclusions drawn by Daniel (2003) who analysed tactics within football. Daniel (2003) found that successful teams regain possession more frequently in the other team s half, resulting in decreased defending time for successful teams. As defending time is a negative performance variable, Daniel (2003) agrees with the initial hypothesis of the current study that the unsuccessful teams would demonstrate greater percentages and frequencies of negative performance variables. Defending time is considered a negative variable as defending for long periods of time increases a team s likelihood of conceding a goal and limits their chances of attacking and scoring goals of their own, hence why the bottom 4 teams defended for greater periods of time. Unsuccessful teams demonstrating greater defending time hints that successful teams reduced their overall defending time more effectively than unsuccessful teams. The mechanisms through which they achieve this may lie in the findings of Daniel (2003), the findings of the current study, or other factors which are not categorised under defensive performance. The findings of the current study could explain the reduced defending time of successful teams through the significant differences found between successful and unsuccessful team s success rates of clearances. A greater clearance success percentage is an indicator that when the defensive team clears the attacking danger, that the midfield or attacking players of successful teams maintain the possession more frequently than unsuccessful teams. The maintenance of possession directly results in the reduced defending time. 22

33 Jones, James and Mellalieu (2004) found that possession was greater for successful teams than unsuccessful teams. In the current study a team with possession could not have been defending, so it could be assumed from successful teams having less defending time that they had more possession than unsuccessful teams, agreeing with the findings of James, Jones and Mellalieu (2004). It was found that open defensive success was significantly greater for successful teams. This suggested that through whatever mechanisms, the successful teams were able to deal with defensive situations more effectively, leading to fewer shots on goal conceded and therefore less chance of conceding a goal. As successful teams achieved significantly more interceptions that unsuccessful teams, this would suggest interceptions were involved in the transition from a defensive scenario to a position of control and possession. The lack of significance within other positive variables (variables which were likely to benefit the defending team) suggests how the successful teams must have demonstrated more effective all-round defensive performances, rather than greatly more frequent demonstrations of a single defensive performance variable. The significance within open defensive success must be an illustration of a more positive defensive performance across all the constituent variables of open defensive success, although no one performance variable was performed significantly more frequently than any other. Another possibility for this outcome could lie in the effectiveness of the similar behaviours between successful and unsuccessful teams. For example, successful teams may have won a similar number of aerial battles to unsuccessful teams, however the unsuccessful teams failed to develop on their possession, leading to another defensive scenario, whereas the successful teams may develop their possession well and spend more time in possession and making the opposition defend for longer. The lack of significant differences found between matches with different outcomes suggested the limited impact defensive performances had on match outcomes. This finding conflicts with a study by Ruiz-Ruiz, Fradua, Fernandez-Garcia and Zubillaga (2013) who concluded that there were significant offensive and defensive differences between teams that were winning and losing a match. Only open defensive success was found to be significantly more positive for matches drawn than matches won and lost (p<0.05). As games drawn are typically, and were in this tournament, low scoring games, high open 23

34 defensive success could therefore be associated with conceding fewer goals. However, these findings could be the result of equally positive defensive performances by both teams, where the team in question defended as positively as a winning team; however the opposition defence was equally successful, restricting their attacking performance and resulting in a draw. Another possibility could be that the team in question struggled to attack with much success, so the winning defensive performance was in vain, only resulting in not conceding and not a win. So what can be drawn from this discussion is that open defensive success provides greater opportunities to win matches, however, attacking effectiveness is the deciding factor when it comes to scoring and winning matches. An interesting comparison to be made between the current study and the study by Lago- Penas, Lago-Ballestros, Dellal and Gomez (2010) is the difference in significant findings when comparing the performance variables of matches with different outcomes. Both studies found that the unsuccessful teams conceded more shots on target (evident in the current study through unsuccessful teams having a significantly lesser percentage of open defensive success); however, Lagos-Penas et al. (2010) found that unsuccessful teams won significantly more offsides. The current study found no significance within this variable however unsuccessful teams did average more offsides conceded. Furthermore, the current study found unsuccessful teams conceded significantly more corners than successful teams, Lagos-Penas et al. (2010) examined corners but found no significant difference between successful and unsuccessful teams. The data analysis may have found the differences in the current study to be insignificant due to the small sample size by comparison (20 matches, where Lagos-Penas et al. Observed 380 matches over a whole season of elite football). The current study went on to analyse the amount of yellow and red cards received by the successful and unsuccessful teams. Unlike a study by Lago-Penas, Lago-Ballestros, Dellal and Gomez (2010), no significant differences were evident, although this could be due to the infrequency of cards received in this study. However, Lagos-Penas et al. concluded that losing teams received significantly more red cards than successful teams. Only two red cards were received in the current study, both for unsuccessful teams however, not enough for significant statistical test results. Ruiz-Ruiz, Fradua, Fernandez-Garcia and Zubillaga (2013) found that a team with fewer players on the pitch were significantly less successful, tying in with the findings from Lagos-Penas et al. and to some extend the current study. 24