Journal of Sports Sciences

Identifying playing talent in professional football using artificial neural networks --Manuscript Draft--

Full Title: Identifying playing talent in professional football using artificial neural networks Manuscript Number: RJSP-2019-0277R2 Article Type: Special Issue Keywords: soccer; talent identification; Premier League; Championship; Artificial Intelligence Abstract: The aim of the current study was to objectively identify position-specific key performance indicators in professional football that predict out-field players league status. The sample consisted of 966 out-field players from 2 seasons in the Football League Championship. Players were assigned to one of three categories (0, 1 and 2) based on where they completed most of their match time in the following season, and then split based into five positions. 340 performance, biographical and esteem variables were analysed using a Stepwise Artificial Neural Network approach. A Monte Carlo cross-validation procedure was used to avoid over-fitting and the neural network modelling involved a multi-layer perceptron architecture with a feed-forward back- propagation algorithm. The models correctly predicted between 72.7% and 100% of test cases (Mean prediction of models = 85.9%), the test error ranged from 1.0% to 9.8% (Mean test error of models = 6.3%). Variables related to passing, shooting, regaining possession and international appearances were key factors in the predictive models. This is highly significant as objective position-specific predictors of players league status could be used to aid the identification and comparison of transfer targets as part of the due diligence process in professional football. Order of Authors: Donald Barron Graham Ball Matthew Robins Caroline Sunderland Response to Reviewers: Response to Reviewers – Journal of Sports Sciences

Reviewer #4: I suggest highlighting in the discussion that the network does not merely predict the subjective scouting decisions, as you've explained in your response. Otherwise, I am satisfied with the revised version.

Response

The text in the discussion has been updated to answer your query around merely predicting subjective scouting decisions. Please provide feedback if you feel this is not sufficient or needs revision.

Discussion The aim of the current study was to develop objective models that identified position- specific key performance indicators that predict out-field players league status. The artificial neural network created fifteen position-specific models to predict out-field players league status. The artificial neural network’s ability to correctly classify more than 75% of the players league status for fourteen different position comparisons is a key result. The models were able to accurately predict the league status of players being transferred between different levels of competition and those who were promoted or demoted with their team. Therefore, they did not simply predict subjective scouting decisions.

1 2 3 4 5 6 7 Identifying playing talent in professional football using artificial 8 9 10 neural networks 11 12 13 14 Donald Barron1, Graham Ball2, Matthew Robins3, Caroline Sunderland4 15 16 1 School of Science, Technology and Engineering, University of Suffolk, Ipswich, UK (Email: 17 18 [email protected], Tel: +44 (0)1473 338035). 19 20 21 2 John van Geest Cancer Research Centre, School of Science and Technology, Nottingham Trent 22 23 University, Nottingham, UK (Email: [email protected], Tel: +44 (0)115 848 8308). 24 25 26 27 3 Institute of Sport, University of Chichester, Chichester, UK (Email: [email protected], Tel: +44 28 (0)1243 816456). 29 30 31 32 4 Sport, Health and Performance Enhancement Research Centre, School of Science and Technology, 33 34 Nottingham Trent University, Nottingham, UK (Email: [email protected], +44 (0)115 35 8486379). 36 37 38 39 Corresponding author: Donald Barron 40 41 Keywords: Soccer, Talent Identification, Premier League, Championship, Artificial 42 43 Intelligence 44 45

46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Abstract 8 9 The aim of the current study was to objectively identify position-specific key performance 10 11 indicators in professional football that predict out-field players league status. The sample 12 13 consisted of 966 out-field players who completed the full 90 minutes in a match during the 14 15 2008/09 or 2009/10 season in the Football League Championship. Players were assigned to 16 one of three categories (group 0, 1 and 2) based on where they completed most of their match 17 18 time in the following season, and then split based on five positions including full backs (n = 19 20 205), centre backs (n = 193), centre midfielders (n = 205), wide midfielders (n = 168) and 21 22 forwards (n = 195). 340 performance, biographical and esteem variables were analysed using 23 24 a Stepwise Artificial Neural Network approach. The models correctly predicted between 72.7% 25 26 and 100% of test cases (Mean prediction of models = 85.9%), the test error ranged from 1.0% 27 to 9.8% (Mean test error of models = 6.3%). Variables related to passing, shooting, regaining 28 29 possession and international appearances were key factors in the predictive models. This is 30 31 highly significant as objective position-specific predictors of players league status have not 32 33 previously been published. The method could be used to aid the identification and comparison 34 35 of transfer targets as part of the due diligence process in professional football. 36 37 38 Introduction 39 40 Coaches and decision makers in professional football have traditionally used subjective 41 42 observations to assess the performance of their team, to review the strengths and weaknesses 43 44 of future opponents and to identify potential signings (Carling, Williams and Reilly, 2005). 45 46 Match analysis research into the individual’s performance in football has focused heavily on 47 48 the physical demands of the sport (Carling, 2013). Research led by sport scientists with a heavy 49 focus upon the physical aspects of performance in football has not managed to identify key 50 51 predictors of match outcome or team success (Bradley et al., 2016; Carling, 2013). 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 However, studies investigating physical performance during matches have also incorporated 8 9 technical elements and provided some insights into the successful performance of players and 10 11 teams (Bradley et al., 2013; Bradley et al., 2016; Dellal et al., 2010; Dellal et al., 2011). 12 13 Technical factors have been identified that are prominent predictors of team success and match 14 15 outcome. Shots, shots on target and ball possession are the most commonly reported predictors 16 (Castellano, Casamachina and Lago, 2012; Lagos-Penas, Lago-Ballesteros, Dellal and Gomez, 17 18 2010; Liu, Gomez, Lago-Penas and Sampaio, 2015). There has been a heavy emphasis on the 19 20 attacking aspects of play linked to success and more detailed analysis is required into the 21 22 defensive aspects of play to gain a greater understanding of the game. 23 24 25 26 Following on from the research into team success and physical profiles, there has been an 27 increasing interest in the technical profiles of players. Studies have found positional differences 28 29 in Ligue 1 in France, the Premier League in England and in Spain’s La Liga (Dellal, Wong, 30 31 Moalla and Chamari 2010; Dellal et al., 2011). The development of advanced computer 32 33 systems has supported a greater understanding of position profiles in football. However, most 34 35 of the research to date has used subjective methods to select variables for analysis (Taylor, 36 37 Mellalieu and James, 2004) or they have replicated indicators used in other studies 38 (Andrzejewski, Konefal, Chmura, Kowalczuk and Chmura, 2016). Using subjective criteria 39 40 selection rather than exploring a broad spectrum of the data points has meant that many 41 42 variables have yet to be assessed. Therefore, the impact of these variables upon playing success 43 44 and career progression is unknown. 45 46 47 48 A broader analysis of player performance and career progression has been provided by using 49 artificial neural networks to assess a wide range of variables (Barron, Ball, Robins and 50 51 Sunderland, 2018). Artificial neural networks have been shown to be better at identifying 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 patterns in complex non-linear data sets than forms of regression analysis and they are capable 8 9 of generalizing results to solve real world problems (Basheer and Hajmeer, 2000; Lancashire, 10 11 Lemetre and Ball, 2009; Tu, 1996). In a football context, artificial neural networks have been 12 13 shown to be capable of creating models that can differentiate between specific groups and 14 15 identify key variables that predict career progression (Barron et al, 2018). Previous studies 16 though have been limited by assessing players regardless of position and their accuracy could 17 18 be improved by making assessments of each position and the creation of position-specific 19 20 career progression models. 21 22 23 24 To the authors’ knowledge there has not been an objective study carried out to develop a 25 26 position-specific predictive model that could support the scouting and recruitment process in 27 professional football. The efficient and effective identification and assessment of transfer 28 29 targets is a key aspect of any professional football club and requires a thorough due diligence 30 31 process. Therefore, the aim of the current study was to develop an objective model to identify 32 33 position-specific key performance indicators in professional football that predict out-field 34 35 players league status using an artificial neural network. 36 37 38 Methods 39 40 41 Players and Match Data 42 43 The basis of the current study followed Barron et al’s (2018) method but looked to build on it 44 45 and focus on position-specific assessments of players. The sample consisted of 966 out-field 46 47 players (mean ± SD age and height: 25 ± 4 yr, 1.81 ± 0.06 m) who had completed a full 90 48 49 minutes in the English Football League Championship during the 2008/09 and 2009/10 seasons 50 51 (Table 1). Technical performance data and biographical data was collected using ProZone’s 52 53 MatchViewer software (ProZone Sports Ltd., Leeds, UK), the official Football League website 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 (www.efl.com) and Scout7 Ltd’s (Birmingham, UK) site. The Prozone MatchViewer system 8 9 was used to collect performance data due to its accurate inter-observer agreement for the 10 11 number and type of events (Bradley, O’Donoghue, Wooster and Tordoff, 2007). The data 12 13 collected from the Prozone MatchViewer software was made available by STATS LLC 14 15 (Chicago, USA). Institutional ethical approval was attained from the Non-Invasive Human 16 Ethics Committee at Nottingham Trent University. 17 18 19 20 In total, 536 variables were collected in including the total number, accuracy (% success), 21 22 means, medians and upper and lower quartiles of passes, tackles, possessions regained, 23 24 clearances and shots. Additional data on total appearances, playing percentage, total goals and 25 assists, international appearances and heights was also collected. The data set originally 26 27 included 536 variables but low variance statistics were removed. After removing low variance 28 29 data points, the data set included 340 variables for comparison. Each player’s data was 30 31 converted into mean 90-minute performance data before they were assigned to one of three 32 33 categories (group 0, group 1 and group 2). 34 35 36 37 Player Grouping 38 39 Players were allocated to one of five positions (full back, centre back, wide midfielder, central 40 41 midfielder or attacker) based on where they spent most of their playing time during the season 42 43 (See Table 1). They were then assigned to one of three categories (group 0, group 1 and group 44 2) based on where they went on to complete most of their match time during the following 45 46 season. The first category (group 0) included the players who completed most of their match 47 48 time in a lower league during the following season. The second group (group 1) included those 49 50 players who completed most their match time in the English Football League Championship 51 52 during the following season and the final category (group 2) contained the players who 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 progressed to complete most their match time in the English Premier League during the 8 9 following season. 10 11 12 13 Sample sizes for each comparison were balanced to have an equal number of cases using a 14 15 random number selector (i.e. 24 full backs were selected from group 0 to have an equal number 16 of cases for comparisons to group 2). Players who played on loan during the 2008/09 and 17 18 2009/10 seasons were included in the study but players who moved to a club outside England 19 20 were excluded due to the complications in assessing the merits of foreign competitions against 21 22 those in England. The five positions for each category of playing status were subsequently 23 24 analysed using a Stepwise Artificial Neural Network approach to identify the optimal 25 26 collection of variables for predicting playing status. 27

28 29 Artificial Neural Network Model 30 31 32 The artificial neural network modelling was based on the approach previously used in gene 33 34 profiling with breast cancer data (Lancashire et al., 2009) and used in assessing player 35 performances in the Football League Championship (Barron et al., 2018). It used in house code 36 37 written in Microsoft visual basic 6 to call Statistica 10.0 (Statsoft Inc., Tulsa, USA) artificial 38 39 neural network model at each loop of the stepwise procedure and output the results in a text 40 41 format. 42 43 44 45 Before training the artificial neural network, the data was randomly split (60% for training 46 purposes, 20% for validation and 20% blind test cases). A Monte-Carlo cross validation 47 48 procedure was used to avoid over-fitting of the data. The artificial neural network modelling 49 50 involved a multi-layer perceptron architecture with a feed-forward back-propagation 51 52 algorithm. This algorithm used a sigmoidal transfer function and weights were updated by 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 feedback from errors. Results were provided for the average test performance and the average 8 9 test error. The average test performance indicates the percentage of test cases that are correctly 10 11 predicted. The average test error is the root mean square error for the test data set, this indicates 12 13 the difference between the values predicted by the model and the actual values of the test data 14 15 set (Salkind, 2010). Further information on the artificial neural network model can be viewed 16 in the supplementary information. 17 18 19 20 Results 21 22 23 Analysis using the artificial neural network created fifteen position-specific models to predict 24 out-field player’s league status. The models correctly predicted between 72.7% and 100% of 25 26 test cases (Mean prediction of models = 85.9%), the test error ranged from 1.0% to 9.8% (Mean 27 28 test error of models = 6.3%). Fourteen models correctly predicted 75% or more of the test 29 30 players league status with an error of 9.6% or less (Table 2). The fifteen models, created in 31 32 total, contained between five and twenty variables to predict the players league status with 134 33 34 variables in total being required to make the position models. The most prominent set of 35 variables were those related to the players passing ability, with 48 of the 134 variables (35.8%) 36 37 being passing statistics. The next most prominent type of variable was related to players 38 39 shooting. In total, twenty variables (14.9%) related to shooting were selected in the models. 40 41 Statistics related to regaining possession accounted for eleven of the variables (8.2%) selected. 42 43 Variables related to international appearances were selected nine times (6.7%). A full outline 44 45 of the categories of variables selected can be viewed in full (Table 3). 46

47 48 Full Back Models 49 50 The performance of the full back models as a group were the lowest of the five positions 51 52 (Average test performance = 78.4% ± 8.0% and average test error = 8.6% ± 1.7%) (Table 4). 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 The group 0 v1 comparison had the lowest average test performance and highest test error out 8 9 of all the models created (Average test performance = 72.7% and average test error = 9.8%). 10 11 Total appearances and mean percentage of backwards passes successful were key variables in 12 13 the model (Table 5). The group 1 v 2 comparison had an average test performance of 75% and 14 15 a test error of 9.3%. The percentage of sideways passes successful (upper quartile) and median 16 total shots were the most prominent variables in the model (Table 6). The best full back model 17 18 was for group 0 v 2 which had an average test performance of 87.5% and a test error of 6.6%. 19 20 The mean goals scored and minimum headers were the two most prominent factors in the model 21 22 (Table 7). 23 24 25 26 Centre Back Models 27 The performance of the centre back models as a group had an average test performance of 28 29 94.4% ± 5.1% and an average test error of 3.5% ± 2.3%. The group 0 v 1 model had an average 30 31 test performance of 93.3% and an average test error of 4.1% using twenty variables. The 32 33 percentage of successful passes in the opposition half (upper quartile) and shooting accuracy 34 35 (upper quartile) were the most prominent variables in the model (Table 8). The group 1 v 2 36 37 model had the lowest average test performance and highest test error of the three centre back 38 models (average test performance = 90.0% and average test error = 5.5%). Backwards passes 39 40 (lower quartile) and maximum short passes were the top two factors in the model (Table 9). 41 42 The group 0 v 2 model had the highest average test performance of any model and the lowest 43 44 test error of any model (average test performance = 100% and test error = 1.0%). The group 0 45 46 v 2 centre back model contained eighteen variables with 0-6 assists mean (group 0 = 0.1 ± 0.1, 47 48 group 2 = 0.2 ± 0.1), mean shots on target inside the box (group 0 = 0.2 ± 0.2, group 2 = 0.3 ± 49 0.2) and minimum penalty area entries (Group 0 = 0.2 ± 0.4, Group 2 = 0 ± 0) being key 50 51 variables (Table 10). 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Wide Midfielder Models 8 9 The wide midfield models group average test performance was 84.8% ± 13.2% with an average 10 11 test error of 6.3% ± 2.5%. The group 0 v 1 model had an average test performance of 79.4% 12 13 and a test error of 8.2%. The maximum percentage of unsuccessful headers and forward passes 14 15 successful (upper quartile) were the biggest predictors in the model (Table 11). The group 1 v 16 2 model had an average test performance of 77.8% and a test error of 7.4%. U21 international 17 18 caps and median forward passes unsuccessful were the most prominent factors in the model 19 20 (Table 12). The group 0 v 2 model had the second highest average test performance and third 21 22 lowest test error of all the models created (average test performance = 100% and a test error of 23 24 3.4%). The group 0 v 2 wide midfielder model contained six variables including: total goals 25 26 (group 0 = 1.4 ± 1.9, group 2 = 5.5 ± 3.8), passes attempted opposition half upper quartile 27 (group 0 = 16.2 ± 6.3, group 2 = 21.4 ± 5.8), fouls in the defensive third mean (group 0 = 0.2 28 29 ± 0.2, group 2 = 0.3 ± 0.3), total shots on target (excluding blocked) maximum (group 0 = 1.0 30 31 ± 0.8, group 2 = 2.6 ± 1.1), % forward passes successful mean (group 0 = 53.4% ± 14.8%, 32 33 group 2 = 55.2% ± 9.7%) and forward passes successful median (group 0 = 5.0 ± 3.2, group 2 34 35 = 6.1 ± 2.2) (Table 13). 36 37 38 Centre Midfielder Models 39 40 The best overall average was for the centre midfielder’s models as a group (Average test 41 42 performance = 86.1% ± 6.6 and average test error = 6.8% ± 2.5). The group 0 v 1 model had 43 44 the lowest average test performance of the centre midfield models and had the second highest 45 46 test error across all models (Average test performance = 78.6% and average test error = 9.6%). 47 48 Fouls and maximum first time passes were the most prominent variables in the model (Table 49 14). The group 1 v 2 model had an average test performance of 88.9% and a test error of 5.9%. 50 51 Successful passes (lower quartile) and penalty area entries (lower quartile) were two key 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 variables in the model (Table 15). The group 0 v 2 model had an average test performance of 8 9 90.9% and a test error of 4.8%. The number of starts and maximum shots on target outside the 10 11 box were the highest predictors in the model (Table 16). 12 13 14 15 Attacker Models 16 The performance of the attacker models as a group had an average test performance of 84.7% 17 18 ± 6.6% and an average test error of 6.2% ± 3.2%. The group 0 v 1 model had an average test 19 20 performance of 80% and an average test error of 8.7%. The most prominent variables in the 21 22 model were international caps and the number of touches (lower quartile) (Table 17). The group 23 24 1 v 2 model had an average test performance of 81.8% and a test error of 7.2%. U21 25 26 international caps and international caps were the two most important factors in the model 27 (Table 18). The best average test performance for an attacker model was recorded for the group 28 29 0 v 2 model and it had the lowest overall test error of all models (average test performance = 30 31 92.3% and test error = 2.6%). The group 0 v 2 attacker model contained ten variables with total 32 33 goals (group 0 = 2.7 ± 3.0, group 2 = 10.0 ± 6.2), blocks upper quartile (group 0 = 1.0 ± 0.5, 34 35 group 2 = 1.5 ± 0.7) and short passes minimum (group 0 = 4.9 ± 2.5, group 2 = 4.3 ± 2.4) being 36 37 key variables (Table 19). 38

39 40 Model Comparisons 41 42 The models produced comparing positions for group 0 v 1 had the lowest overall average test 43 44 performance and highest test error (mean test performance = 80.8% ± 7.6% and average test 45 46 error = 8.1% ± 2.3%). The overall average test performance across all five positions for group 47 48 1 v 2 comparisons was 82.7% ± 6.6% and the average test error was 7.1% ± 1.5. The highest 49 overall average test performance across the five positions was for group 0 v 2 (mean test 50 51 performance = 94.1% ± 5.6% and average test error = 3.7% ± 2.1%) (Table 20). The top three 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 models produced by the neural network were for 0 v 2 centre back (average test performance 8 9 100% and 1.0% test error), group 0 v 2 wide midfielder (average test performance 100% and 10 11 3.4% test error) and group 0 v 1 centre back (average test performance 93.3% and 4.1% test 12 13 error). The means and standard deviations for key variables for the top three models can be 14 15 reviewed in full (Tables 21-23). 16

17 18 Discussion 19 20 21 The aim of the current study was to develop objective models that identified position-specific 22 23 key performance indicators that predict out-field players league status. The artificial neural 24 network created fifteen position-specific models to predict out-field players league status. The 25 26 artificial neural network’s ability to correctly classify more than 75% of the players league 27 28 status for fourteen different position comparisons is a key result. The models were able to 29 30 accurately predict the league status of players being transferred between different levels of 31 32 competition and those who were promoted or demoted with their team. Therefore, they did not 33 34 simply predict subjective scouting decisions. 35

36 37 The results surpass the previous prediction rates reported using artificial neural networks in 38 39 other team sports, such as those undertaken in cricket (Iyer and Sharda, 2009; Saikia, 40 41 Bhattacharjee and Lemmer, 2012). Their studies could predict classification of batsmen and 42 43 bowlers with accuracy levels ranging from 49% to 77%. In individual sports, artificial neural 44 45 networks have been able to predict 80.2% of gymnast’s future classifications based on a multi- 46 dimensional testing process (Pion, Hohmann, Liu, Lenoir and Segers, 2017). Therefore, the 47 48 current artificial neural network prediction rates are among the highest reported to date in an 49 50 athlete classification study. 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Passing Variables 8 9 The most prominent set of variables were those related to the players passing ability, with 48 10 11 of the 134 total variables included in models (35.8%) being passing statistics. Many passing 12 13 variables have been highlighted previously as key indicators when differentiating between 14 15 players of various playing levels and linked to team success (Bradley et al., 2013; Rampinini, 16 Impellizzerie, Castagna, Coutts and Wisloff, 2009). Comparisons between players within the 17 18 English football pyramid showed that players in the Premier League performed a greater 19 20 number of total passes, successful passes and forward passes (Bradley et al., 2013). Out of the 21 22 48 passing variables identified in the models, 29 were related to the success of the passing 23 24 variables. The passing variables related to their success were a mixture of 27 different statistics 25 26 accounting for the direction (forwards, sideways and backwards) of the pass, the origin of the 27 pass (own half or opposition half) and the mean, median, minimum, maximum and upper and 28 29 lower quartile figure for different variables. 30 31 32 33 In further agreement with Bradley and colleagues (2013) findings, thirteen of the passing 34 35 variables were related to forward passing. Forward passes have been shown to have the lowest 36 37 chance of success when compared to sideways or backwards passes (Szczepanski and McHale, 38 2016). Yet, to create scoring opportunities and in turn score goals players are required to 39 40 progress the play with forward passing. Variables relating to forward passes appeared in 41 42 models for full backs (group 0 v 1 and group 0 v 2), centre backs (group 0 v 1), wide midfield 43 44 (group 0 v 1, group 1 v 2 and group 0 v 2), centre midfield (group 0 v 1 and group 0 v 2) but 45 46 did not feature prominently in any models for attackers. This would appear logical as attackers 47 48 play in more advanced areas and have fewer opportunities to perform forward passes. The 49 prevalence of forward passing variables for a number of positions and different comparisons 50 51 highlights its importance in playing success. 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 The current study also highlighted two variables related to short passing with the maximum 8 9 and minimum variables being selected in two models (group 1 v 2 centre back and group 0 v 2 10 11 attacker). Research into factors that distinguish between top four and bottom four English 12 13 Premier League teams highlighted short passes as a key variable (Adams, Morgans, 14 15 Sacramento, Morgan and Williams, 2013). Specifically, the mean frequency of successful short 16 passes played by centre backs and full backs was the biggest factor differentiating between the 17 18 two groups. 19 20 21 22 Using the artificial neural network methodology has highlighted some overlap between factors 23 24 previously identified by research articles. The current study has also identified novel findings 25 26 for variables that have not previously been analysed or identified as key variables. Eight 27 passing variables were related to those in the opposition half and they appeared in six different 28 29 position models (group 0 v 1 centre back, group 0 v 1 and 0 v 2 centre midfield, group 0 v 1 30 31 and 0 v 2 wide midfield and 0 v 2 attacker models). Six of the variables were also related to 32 33 first time passes played and they appeared in the group 0 v 1 and 0 v 2 centre back, group 1 v 34 35 2 full back, group 0 v 1 and 1 v 2 centre midfield and group 0 v 1 attacker models. Passes in 36 37 the opposition half indicate possession taking place in more offensive pitch locations and could 38 indicate the involvement of players in attacking moves. The ability to pass the ball accurately 39 40 over a range of distances and directions is a key factor in performance and for differentiating 41 42 between players of varying ability. This is accepted knowledge amongst coaches but the 43 44 models have accurately identified specific key variables and provided an objective assessment 45 46 of their impact on league status. 47 48 49

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Shooting Variables 8 9 The next most prominent type of variable was related to players shooting ability. In total, 10 11 twenty variables (14.9%) related to shooting were selected in the models. This agrees with 12 13 previous research into team success in football, with total shots and shooting accuracy being 14 15 the most commonly reported predictors in matches (Castellano et al., 2012; Lagos-Penas et al., 16 2010; Liu et al., 2015). Surprisingly, all positions except attacker included shooting variables 17 18 in the models created in the current study. However, one of the attacker models (group 0 v 2) 19 20 did include total goals as a key variable. Many teams now prefer to play with one lone attacker 21 22 in their line-up that spreads the need for scoring goals throughout the team and the requirements 23 24 of the centre forward position could be changing as a result (Adams et al., 2013). 25 26 27 Attacking Entries 28 29 Other attacking variables selected as part of the models were related to crossing and entries 30 31 into the final third and penalty area. Final third and penalty area entries were selected three 32 33 times and in three different models. Crosses are a factor that have been repeatedly identified as 34 35 being key to differentiating between successful and unsuccessful teams (Lagos-Penas et al., 36 37 2010; Lagos-Penas et al., 2011). They have not been identified as key when differentiating 38 between players of different performance levels previously, they were only selected twice in 39 40 the current study meaning they did not play a prominent role in the position models. The mean 41 42 number of crosses were selected in the group 0 v 2 attacker model (crosses mean group 0 1.0 43 44 ± 0.8, group 2 1.75 ± 1.23). The inclusion of the number of crosses in the attacker model and 45 46 the higher values reported for group 2 may offer more evidence for the evolving role of the 47 48 attacker. 49

50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 As well as crosses, final third and penalty area entries were selected three times and in three 8 9 different models. Previous research has indicated that penalty area entries differentiate between 10 11 winning and losing teams (Ruiz-Ruiz, Fradua, Fernandez-Garcia and Zubillaga, 2013). 12 13 However, in the current study they were selected in one model for centre backs (group 0 v 2), 14 15 the centre backs from players dropping down to a lower playing level reported higher values 16 (minimum penalty area entries group 0 0.2 ± 0.4, group 2 0.0 ± 0.0). The identification of 17 18 minimum penalty area entries in the centre back model and group 0 having a higher value is a 19 20 novel finding. It may appear counter intuitive but centre backs who drop down to a lower level 21 22 may play in teams who use a more direct style of play and play longer passes from their centre 23 24 backs as opposed to building the play with shorter passing combinations. 25 26 27 Defensive Variables 28 29 The models also highlighted several defensive variables as key predictors of league status. 30 31 Statistics related to regaining possession accounted for eleven of the variables (8.2%). Previous 32 33 research into match outcomes and players technical and tactical ability has heavily focused on 34 35 the attacking aspects of play (Mackenzie and Cushion, 2013), passing (Adams et al., 2013; 36 37 Szczepanski and McHale, 2016) and possession (Castellano et al., 2012; Collett, 2013; Lagos- 38 Penas et al., 2010; Liu et al., 2015). A limited number of defensive variables have been 39 40 researched or identified that are linked to success. A balanced defensive shape (Tenga, Holme, 41 42 Ronglan and Bahr, 2010), defensive reaction after losing possession (Vogelbein, Nopp and 43 44 Hokelmann, 2014) and regaining possession in the final third have been identified previously 45 46 (Almeida, Ferreira and Volossovitch, 2014). 47 48 49 The current study highlighted possession won based on the minimum, median, maximum and 50 51 upper quartile variables as being key predictors of league status. Possession gained upper 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 quartile and interceptions median and maximum were also selected as key variables in models. 8 9 The defensive variables were not selected as part of any of the full back models. They were 10 11 commonly selected as part of the wide midfield (group 0 v 1 and group 1 v 2) and attacker 12 13 models (group 1 v 2 and group 0 v 2). This may appear counter intuitive and these factors 14 15 would not normally be assessed when profiling more attacking positions within the team. 16 Modern playing philosophies valuing high pressing tactics from forward players to regain 17 18 possession in more advanced areas of the pitch, this may explain the importance of these factors 19 20 in wide midfield and attacker models within the current study (Perarnau, 2014). 21 22 23 24 International Recognition 25 26 Other key variables selected throughout several models relate to international appearances, 27 international caps and U21 international caps were selected nine times (6.7%) in total. This is 28 29 a novel finding as previous assessments of player’s performances have limited themselves to 30 31 match performance and season totals of performance data. Previous research into international 32 33 recognition and team or playing success has not been undertaken to the author’s knowledge. 34 35 However, international recognition has been found to be linked with player salary allocation, 36 37 particularly at the higher levels of the game (Frick, 2011). 38

39 40 Position-Specific Models 41 42 The current study created a number of strong predictive models for player’s league status, there 43 44 were also some key findings relating to the prediction rates of specific positions. Three of the 45 46 five positions had very similar levels of classification accuracy (centre midfield 86.1%, wide 47 48 midfield 85.7% and attacker 84.7%) but the full back position’s overall accuracy was only 49 78.4% and the centre back position’s overall accuracy was 94.4%. The full back results are still 50 51 an important finding but below the levels reported for other positions. The group 0 v 1 full back 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 model had the lowest classification accuracy of all the models and the group 1 v 2 full back 8 9 model had the second lowest classification accuracy. The full back position is one that requires 10 11 a complex set of technical and tactical skills as it requires a wide array of attacking and 12 13 defensive qualities (Bush, Archer, Hogg and Bradley, 2015). 14 15 16 Recent evaluations of the changes within performance data for playing positions has shown 17 18 extensive changes over time in the Premier League (Bush et al., 2015). Pronounced increases 19 20 were found for the levels of high-speed running and the distances covered while sprinting, with 21 22 full backs showing the largest increases between 2006-07 and 2012-13 (Bush et al., 2015). 23 24 Therefore, the full back position may be influenced more by the physical aspects of 25 26 performance. This could explain the lower prediction rates for full backs due to the lack of 27 physical tracking data being available. 28 29 30 31 Study Limitations 32 33 Strong models were identified for fourteen out of the fifteen position comparisons assessed but 34 35 there are some limitations to the present study that should be addressed in future research. The 36 37 match running performance data for players was not available for the current study. There is 38 an acceptance amongst the sports science community that running performance is not a 39 40 predictor of team success or match outcome (Bradley et al., 2016; Carling, 2013). However, 41 42 including match running performance data could provide a higher level of classification 43 44 accuracy for some of the positions assessed. Another limitation of the study is the lack of 45 46 contextual data available and the inability of the data to provide a detailed assessment for off 47 48 the ball parameters. The final limitation of the study relates to the sample size for players 49 progressing to play in the Premier League. The samples for the players progressing from the 50 51 five positions to play in the Premier League were the smallest of all the groupings. Statistical 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 power tests on similar sample sizes have reached the required levels (Lancashire et al., 2009). 8 9 However, future studies should look to increase the sample available to increase confidence 10 11 that the results are repeatable to new cases. 12 13 14 15 Conclusions 16 17 The current study has shown that artificial neural networks are a valid and highly effective tool 18 19 to classify and predict players league status. Fourteen models across all five positions were 20 21 created that provided strong prediction accuracy levels for players league status. This is an 22 23 important result as it outlines an objective methodology that can aid the scouting and 24 recruitment process in professional football. The process of identifying and recruiting players 25 26 in professional football has largely been a subjective process in the past. Further research 27 28 should look to combine assessments of physical and technical performance data to provide a 29 30 more accurate prediction of league status. Studies should also look to create models to predict 31 32 the career progression of players from multiple leagues to provide a better practical tool for 33 34 scouting and recruitment purposes. The combination of subjective assessments and more 35 objective tools could lead to a more effective overall process in the highly competitive football 36 37 transfer market. 38 39 40 41 Acknowledgments 42 43 The authors would like to thank STATS for providing access to the performance data that is used 44 in this study. We would also like to thank Scout7 for providing access to their system to include 45 46 biographical and international appearance data in the current study. 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 References 8 9 10 Adams, D., Morgans, R., Sacramento, J., Morgan, S., and Williams, M. D., 2013. Successful 11 short passing frequency of defenders differentiates between top and bottom four English 12 13 Premier League teams. International Journal of Performance Analysis in Sport, 13, pp. 653- 14 668. 15 16 17 Almeida, C. H., Ferreira, A. P., and Volossovitch, A., 2014. Effects of match location, match 18 status and quality of opposition on regaining possession in UEFA Champions League. Journal 19 20 of Human Kinetics, 41 (2014), pp. 2013-214. 21 22 Andrzejewski, M., Konefal, M., Chmura, P., Kowalczuk, E., and Chmura, J., 2016. Match 23 24 outcome and distances covered at various speeds in match play by elite German soccer players. 25 International Journal of Performance Analysis in Sport, 16 (3), pp. 817-828. 26 27 28 Basheer, I.A., and Hajmeer, M., 2000. Artificial neural networks: fundamentals, computing, 29 design and application. Journal of Microbiological Methods, 43 (2000), pp. 3-31. 30 31 32 Bradley, P. S., Archer, D., Hogg, B., Schuth, G., Bush, M., Carling, C., and Barnes, C., 2016. 33 Tier-specific evolution of match performance characteristics in the English Premier League: 34 35 It’s getting tougher at the top. Journal of Sports Sciences, 34 (10), pp. 980-987. 36 37 38 Bradley, P. S., Carling, C., Diaz, A. G., Hood, P., Barnes, C., Ade, J., Boddy, M., Krustrup, P., 39 and Mohr, M., 2013. Match performance and physical capacity of players in the top three 40 competitive standards of English professional soccer. Human Movement Science, 32 (2013), 41 42 pp. 808-821. 43 44 Bradley, P. S., O’Donaghue, P., Wooster, B., and Tordoff, P., 2007. The reliability of ProZone 45 46 MatchViewer: a video-based technical performance analysis system. International Journal of 47 Performance Analysis in Sport, 7 (3), pp. 117-129. 48 49 50 Bush, M. D., Archer, D. T., Hogg, R., and Bradley, P. S., 2015. Factors influencing physical 51 and technical variability in the English Premier League. International Journal of Sports 52 53 Physiology and Performance, 10, pp. 865-872. 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 8 9 Carling, C., 2013. Interpreting physical performance in professional soccer match-play: Should 10 we be more pragmatic in our approach. Sports Medicine, 43 (2013), pp. 655-663. 11 12 13 Carling, C., Williams, A. M., and Reilly, T. R., 2005. Handbook of soccer match analysis: A 14 systematic approach to improving performance. London: Routledge. 15 16 17 Castellano, J., Casamichana, D., and Lago, C., 2012. The use of match statistics that 18 discriminate between successful and unsuccessful soccer teams. Journal of Human Kinetics, 19 20 31 (2012), pp. 139-147. 21 22 Collett, C., 2013. The possession game? A comparative analysis of ball retention and team 23 24 success in European and international football, 2007-2010. Journal of Sports Sciences, 31 (2), 25 pp. 123-136. 26 27 28 Dellal, A., Chamari, K., Wong, D. P., Ahmaidi, S., Keller, D., Barros, R., Bisciotti, G. N., and 29 Carling, C., 2011. Comparison of physical and technical performance in European soccer 30 31 match-play: FA Premier League and La Liga. European Journal of Sport Science, 11 (1), pp. 32 51-59. 33

34 35 Dellal, A., Wong, D. P., Moalla, W., and Chamari, K., 2010. Physical and technical activity of 36 soccer players in the French First League – with special reference to their playing position. 37 38 International SportMed Journal, 11 (2), pp. 278-290. 39 40 Frick, B., 2011. Performance, salaries, and contract length: Empirical evidence from German 41 42 soccer. International Journal of Sport Finance, 6, pp. 87-118. 43 44 Iyer, S., R., and Sharda, R., 2009. Prediction of athletes performance using neural networks: 45 46 An application in cricket team selection. Expert systems with applications, 36 (2009), pp. 5510- 47 5522. 48 49 50 Lancashire, L. J., Lemetre, C., and Ball, G. R., 2009. An introduction to artificial neural 51 networks in bioinformatics – application to complex microarray and mass spectrometry 52 53 datasets in cancer studies. Briefings in Bioinformatics, 10 (3), pp. 315-329. 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 8 9 Lago-Penas, C., Lago-Ballesteros, J., Dellal, A., and Gomez, M., 2010. Game-related statistics 10 that discriminated winning, drawing and losing teams from the Spanish soccer league. Journal 11 of Sports Science and Medicine, 9, pp. 288-293. 12 13 14 Liu, H., Gomez, M. A., Lago-Penas, C., and Sampaio, J., 2015. Match statistics related to 15 winning in the group stage of 2014 Brazil FIFA World Cup. Journal of Sports Sciences, 33 16 17 (12), pp. 1205-1213. 18 19 20 Mackenzie, R., and Cushion, C., 2013. Performance analysis in football: A critical review and 21 implications for future research. Journal of Sports Sciences, 31 (6), pp. 639-676. 22 23 24 Perarnau, M., 2014. Pep confidential: The inside story of Pep Guardiola’s first season at 25 Bayern Munich. Edinburgh: Arena Sport. 26 27 28 Pion, J., Hohmann, A., Liu, T., Lenoir, M., & Segers, V. (2017). Predictive models reduce 29 talent development costs in female gymnastics. Journal of Sports Sciences, 35(8), 806-811. 30 31 32 Rampinini, E., Impellizzeri, F. M., Castagna, C., Coutts, A. J., and Wisloff, U., 2009. Technical 33 performance during soccer matches of the Italian Serie A league: Effect of fatigue and 34 35 competitive level. Journal of Science and Medicine in Sport, 12, pp. 227-233. 36 37 38 Ruiz-Ruiz, C., Fradua, L., Fernandez-Garcia, A., and Zubillaga, A., 2013. Analysis of entries 39 into the penalty area as a performance indicator in soccer. European Journal of Sport Science, 40 13 (3), pp. 241-248. 41 42 43 Saikia, H., Bhattacharjee, D., and Lemmer, H., H., 2012. Predicting the performance of bowlers 44 in IPL: An application of artificial neural network. International Journal of Performance 45 46 Analysis in Sport, 12 (1), pp. 75-89. 47 48 49 Salkind, N., J., 2010. Encyclopedia of research design. California: Sage. 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Taylor, J., B., Mellalieu, S., and James, N., 2004. Behavioural comparisons of positional 8 9 demands in professional soccer. International Journal of Performance Analysis in Sport, 4 (1), 10 pp. 81-97. 11 12 13 Tenga, A., Holme, I., Ronglan, L. T., and Bahr, R., 2010. Effects of playing tactics on goal 14 scoring in Norwegian professional soccer. Journal of Sports Sciences, 28 (3), pp. 237-244. 15 16 17 Tu, J. V., 1996. Advantages and disadvantages of using artificial neural networks versus 18 19 logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 49 (11), 20 pp. 1225-1231. 21

22 23 Vogelbein, M., Nopp, S., and Hokelmann, A., 2014. Defensive transition in soccer – are prompt 24 possession regains a measure of success? A quantitative analysis of German Fusball- 25 26 Bundesliga 2010/11. Journal of Sports Sciences, 32 (11), pp. 1076-1083. 27 28 29 30 31 Formatted: Normal, Space After: 0 pt, Line spacing: 1.5 lines 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 1. Biographical data represented as means and standard deviations for player groupings. 8 9 10 Group Players (n) Age (years) Height (cm) 90 Minute Appearances Total Minutes 11 Group 0 Full Back 56 24.2 ± 4.3 180.5 ± 4.4 10.1 ± 10.7 1112 ± 1040 12 13 Group 1 Full Back 125 24.9 ± 4.2 180.2 ± 4.3 20.0 ± 12.1 2603 ± 1107 14 Group 2 Full Back 24 25.4 ± 3.3 179.7 ± 3.6 18.5 ± 12.5 1919 ± 1200 15 16 Group 0 Centre Back 37 27.5 ± 5.1 187.2 ± 5.1 15.9 ± 10.9 15901 ± 1023 17 Group 1 Centre Back 131 25.6 ± 3.7 186.7 ± 4.2 22.5 ± 12.4 2186 ± 1116 18 19 Group 2 Centre Back 25 25.6 ± 3.4 187.4 ± 3.7 22.8 ± 12.0 2173 ± 1141 20 Group 0 Wide Midfield 42 24.4 ± 4.3 179.1 ± 5.5 6.6 ± 7.0 1119 ± 858 21 22 Group 1 Wide Midfield 103 24.6 ± 3.7 177.2 ± 5.6 12.6 ± 9.6 1840 ± 1000 23 Group 2 Wide Midfield 23 24.8 ± 3.7 179.2 ± 4.8 19.4 ± 11.5 2425 ± 1109 24 25 Group 0 Centre Midfield 36 25.6 ± 4.8 179.7 ± 5.1 12.4 ± 11.9 1505 ± 1147 26 Group 1 Centre Midfield 148 25.6 ± 3.9 178.8 ± 5.8 19.5 ± 11.1 2238 ± 1006 27 28 Group 2 Centre Midfield 21 26.3 ± 4.5 178.5 ± 4.5 25.6 ± 13.6 2693 ± 1253 29 Group 0 Attacker 38 26.6 ± 4.8 182.2 ± 6.5 6.2 ± 6.9 1096 ± 920 30 31 Group 1 Attacker 130 26.0 ± 3.9 181.6 ± 5.9 11.8 ± 9.3 1845 ± 931 32 33 Group 2 Attacker 27 26.2 ± 4.5 181.7 ± 5.8 13.2 ± 9.3 2081 ± 930 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 2. Results for all models with balanced data sets. The best average test performance = 8 100.0% and the best average test error = 1.0% (Using a combination of eighteen variables) – 9 Centre Back Group 0 v 2. The worst average test performance = 72.7% and the worst average 10 test error = 9.8% (Using a combination of five variables) – Full Back Group 0 v 1. 11 12 Position Groups Average Test Performance (%) Average Test Error (%) Number of 13 Variables 14 Full Back 0 v 1 72.7 9.8 5 15 Full Back 0 v 2 87.5 6.5 10 16 17 Full Back 1 v 2 75 9.3 6 18 Centre Back 0 v 1 93.3 4.1 20 19 20 Centre Back 0 v 2 100 1.0 18 21 Centre Back 1 v 2 90 5.5 6 22 23 Wide Midfield 0 v 1 76.5 8 10 24 Wide Midfield 0 v 2 100 3.4 6 25 26 Wide Midfield 1 v 2 77.8 7.4 9 27 Centre Midfield 0 v 1 78.6 9.6 9 28 29 Centre Midfield 0 v 2 90.9 4.8 10 30 Centre Midfield 1 v 2 88.9 5.9 5 31 32 Attacker 0 v 1 80 8.7 5 33 Attacker 0 v 2 92.3 2.6 10 34 35 Attacker 1 v 2 81.8 7.2 6 36 Average NA 85.7 6.3 9.0 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 3. Summary of the variables in all position models by grouping. 8 9 Variable Grouping Times Selected Selected (%) 10 11 Passing 48 35.8 12 13 Shooting 20 14.9 14 Regains 11 8.2 15 16 International Appearances 9 6.7 17 Heading 8 6.0 18 19 Fouls 5 3.7 20 Goals 5 3.7 21 22 Appearances 4 3.0 23 24 Entries 3 2.2 25 Possession Lost 4 3.0 26 27 Tackled 3 2.2 28 Time in Possession 3 2.2 29 30 Assists 2 1.5 31 32 Blocks 2 1.5 33 Clearances 2 1.5 34 35 Crossing 2 1.5 36 Touches 2 1.5 37 38 Balls Received 1 0.7 39 Possessions 1 0.7 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 4. Comparison of overall average test performance scores from position models as 8 means and standard deviations. 9 10Position Comparison Overall Average Test Performance (%) Overall Average Test Error (%) 11 12Full Back 78.4 ± 8.0 8.6 ± 1.7 13Centre Back 94.5 ± 5.1 3.5 ± 2.3 14 15Wide Midfield 84.8 ± 13.2 6.3 ± 2.5 16Centre Midfield 86.1 ± 6.6 6.8 ± 2.5 17 18Attacker 84.7 ± 6.6 6.2 ± 3.2 19 20 21 Table 5. Results for Group 0 v Group 1 Full Back balanced data set comparison. The best 22 average test performance = 72.7% and the best average test error = 9.8% (Using a combination 23 of five variables).

24 Rank Variable Average Test Performance (%) Average Test Error (%) 25 261 Total Appearances 63.6 11.2 27 282 % Backwards Passes Successful (Mean) 72.7 10.6 293 Total Minutes 72.7 9.8 30 314 % Forwards Passes Successful (Mean) 72.7 9.8 325 Forwards Passes (Maximum) 72.7 9.8 33 346 Blocks (Mean) 70.5 9.9 357 % Unsuccessful Headers (Median) 68.2 10.0 36 378 Forward Passes Successful (Median) 68.2 10.0 389 % Passes Successful Own Half (Mean) 72.7 9.9 39 4010 Passes Own Half 25% (Lower Quartile) 72.7 10.0 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 6. Results for Group 1 v Group 2 Full Back balanced data set comparison. The best 8 average test performance = 75.0% and the best average test error = 9.3% (Using a combination 9 of six variables). 10 11Rank Variable Average Test Average Test Error 12 Performance (%) (%) 13 1 % Sideways Passes Successful 75% (Upper Quartile) 60.0 11.3 14 152 Total Shots (Median) 60.0 10.9 16 173 International Caps 70.0 9.7 184 Tackled (Mean) 70.0 9.3 19 205 First Time Passes (Maximum) 70.0 9.1 216 Number of Possessions (Median) 75.0 9.3 22 237 Tackled (Minimum) 70.0 9.4 248 % Sideways Passes Successful 25% (Lower Quartile) 70.0 9.4 25 269 Total Assists 70.0 9.8 2710 % First Time Passes Unsuccessful 25% (Lower Quartile) 70.0 9.8 28 29 30 Table 7. Results for Group 0 v Group 2 Full Back balanced data set comparison. The best 31 average test performance = 87.5% and the best average test error = 6.6% (Using a 32 combination of ten variables).

33 Rank Variable Average Test Average Test Error 34 Performance (%) (%) 35 361 Goals (Mean) 75.0 9.1 37 2 Headers (Minimum) 75.0 8.6 38 393 % Forward Passes Unsuccessful (Mean) 81.3 8.2 40 4 Shots Off Target (Exc. Blocked) (Maximum) 78.1 8.1 41 425 % Forward Passes Unsuccessful 75% (Upper Quartile) 75.0 8.2 43 6 U21 Caps 75.0 8.0 44 457 Shots Inside the Box (Mean) 81.3 7.7 46 8 Possession Lost (Mean) 81.3 7.0 47 489 Shots On Tgt Outside the Box (Maximum) 81.3 7.2 49 10 Total Assists 87.5 6.6 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 8. Results for Group 0 v Group 1 Centre Back balanced data set comparison. The best 8 average test performance = 93.3% and the best average test error = 4.1% (Using a combination 9 of twenty variables). 10 11Rank Variable Average Test Average Test Error 12 Performance (%) (%) 13 1 % Passes Successful Opp Half 75% (Upper Quartile) 66.7 10.9 14 152 Shooting Accuracy 75% (Upper Quartile) 73.3 9.3 16 173 % Successful Headers 75% (Upper Quartile) 80.0 7.6 184 Balls Received 75% (Upper Quartile) 80.0 7.6 19 205 Crosses (Median) 80.0 7.9 216 % First Time Passes Successful 25% (Lower Quartile) 80.0 6.8 22 237 Total Shots on Target (Mean) 86.7 6.4 248 Passes Successful Opp Half (Minimum) 86.7 6.0 25 269 U21 Caps 86.7 6.1 2710 Shooting Accuracy 25% (Lower Quartile) 86.7 5.2 28 2911 Medium Passes (Mean) 86.7 5.2 3012 Forward Passes Successful (Minimum) 93.3 4.5 31 3213 Total Shots on Tgt (Excluding Blocked) (Mean) 86.7 5.0 3314 Goals (Mean) 86.7 4.5 34 3515 % Unsuccessful Headers 25% (Lower Quartile) 90.0 4.7 3616 Long Passes (Median) 93.3 4.5 37 3817 % Passes Successful Opp Half (Minimum) 93.3 4.2 3918 Avg Time in Possession (Mean) 86.7 4.8 40 4119 % Forwards Passes Successful (Minimum) 86.7 4.7 4220 Shooting Accuracy (Median) 93.3 4.1 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 9. Results for Group 1 v Group 2 Centre Back balanced data set comparison. The best 8 average test performance = 90.0% and the best average test error = 5.5% (Using a combination 9 of six variables). 10 11Rank Variable Average Test Average Test Error 12 Performance (%) (%) 13 1 Backwards Passes 25% (Lower Quartile) 70.0 10.7 14 152 Short Passes (Maximum) 70.0 9.4 16 173 Interceptions (Maximum) 80.0 8.1 184 Shots on Target Inside the Box (Mean) 80.0 6.8 19 205 Sideways Passes Unsuccessful (Mean) 80.0 6.6 216 Sideways Passes Successful 75% (Upper Quartile) 90.0 5.5 22 237 Passes Successful Own Half (Mean) 90.0 5.5 248 % Passes Successful Opp Half (Minimum) 80.0 6.3 25 269 % Sideways Passes Successful (Median) 90.0 6.4 2710 Shots On Tgt Outside the Box (Mean) 85.0 6.6 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 10. Results for Group 0 v Group 2 Centre Back balanced data set comparison. The best 8 average test performance = 100% and the best average test error = 1.0% (Using a combination 9 of eighteen variables). 10 11Rank Variable Average Test Performance (%) Average Test Error (%) 12 131 0-6 Assists (Mean) 80.0 8.1 142 Shots on Target Inside the Box (Mean) 80.0 5.8 15 163 Penalty Area Entries (Minimum) 90.0 4.4 174 International Caps 90.0 3.7 18 195 Long Passes 25% (Lower Quartile) 90.0 3.2 206 Shots Outside the Box (Mean) 90.0 2.9 21 227 U21 Caps 100.0 2.4 238 Possession Gained 75% (Upper Quartile) 100.0 1.5 24 259 Avg Time in Possession (Median) 100.0 1.5 2610 Clearances (Maximum) 100.0 1.2 27 2811 Shots Outside the Box (Median) 100.0 1.1 2912 First Time Passes (Mean) 100.0 1.3 30 3113 Unsuccessful Passes (Minimum) 100.0 1.4 3214 Interceptions 75% (Upper Quartile) 100.0 1.3 33 3415 Possession Gained (Minimum) 100.0 1.3 3516 Shots Inside the Box 25% (Lower Quartile) 100.0 1.1 36 3717 Total Shots on Target (Mean) 100.0 1.2 3818 Tackled (Minimum) 100.0 1.0 39 4019 Final Third Entries (Mean) 100.0 1.0 4120 Medium Passes 25% (Lower Quartile) 100.0 1.3 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 11. Results for Group 0 v Group 1 Wide Midfield balanced data set comparison. The 8 best average test performance = 79.4% and the best average test error = 8.2% (Using a 9 combination of nine variables). 10 11Rank Variable Average Test Performance Average Test Error (%) 12 (%) 13 1 % Unsuccessful Headers (Maximum) 70.6 10.8 14 152 Forward Passes Successful 75% (Upper Quartile) 73.5 10.0 16 173 Possession Won 75% (Upper Quartile) 70.6 9.8 184 Shooting Accuracy 25% (Lower Quartile) 76.5 8.9 19 205 % Unsuccessful Headers 75% (Upper Quartile) 79.4 8.5 216 % Successful Headers (Median) 76.5 8.4 22 237 Sideways Passes Successful 75% (Upper Quartile) 76.5 8.2 248 Fouls (Mean) 76.5 8.1 25 269 Tackled (Maximum) 79.4 8.2 2710 8.0 Passes Attempted Opp Half (Mean) 76.5 28 29 30 Table 12. Results for Group 1 v Group 2 Wide Midfield balanced data set comparison. The 31 best average test performance = 77.8% and the best average test error = 7.4% (Using a 32 combination of nine variables). 33 34Rank Variable Average Test Performance Average Test Error (%) 35 (%) 36 371 U21 International Caps 66.7 10.3 382 Forwards Passes Unsuccessful (Median) 77.8 9.3 39 403 % Sideways Passes Unsuccessful (Median) 77.8 9.1 414 Fouls (Mean) 77.8 8.9 42 435 Possession Won (Maximum) 77.8 8.6 446 % Unsuccessful Headers (Maximum) 77.8 8.5 45 467 Backwards Passes Unsuccessful (Maximum) 77.8 8.7 478 Possession Lost (Maximum) 77.8 7.9 48 499 Possession Won (Minimum) 77.8 7.4 5010 % Unsuccessful Headers 25% (Lower Quartile) 77.8 7.6 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 13. Results for Group 0 v Group 2 Wide Midfield balanced data set comparison. The 8 best average test performance = 100% and the best average test error = 3.4% (Using a 9 combination of six variables). 10 11Rank Variable Average Test Performance (%) Average Test Error 12 (%) 13 1 Total Goals 84.6 7.2 14 152 Passes Attempted Opp Half 75% (Upper Quartile) 84.6 6.3 16 173 Fouls in Defensive 3rd (Mean) 84.6 6.1 184 Total Shots on Tgt (Excluding Blocked) 19 (Maximum) 92.3 4.5 205 % Forwards Passes Successful (Mean) 92.3 3.3 21 226 Forward Passes Successful (Median) 100.0 3.4 237 Tackled 75% (Upper Quartile) 92.3 3.7 24 258 % Unsuccessful Passes 75% (Upper Quartile) 92.3 3.6 269 Backwards Passes Unsuccessful (Mean) 92.3 3.5 27 2810 Possession Lost (Median) 92.3 3.1 29 30 31 Table 14. Results for Group 0 v Group 1 Centre Midfield balanced data set comparison. The 32 best average test performance = 78.6% and the best average test error = 9.6% (Using a 33 combination of nine variables). 34 35Rank Variable Average Test Performance Average Test Error 36 (%) (%) 371 Fouls 57.1 11.5 38 392 First Time Passes (Maximum) 64.3 10.9 403 Backwards Passes 75% (Upper Quartile) 64.3 10.6 41 424 Number of Touches (Median) 64.3 10.6 435 Fouls (Maximum) 64.3 10.5 44 456 Total Minutes 71.4 9.9 467 % Forward Passes Unsuccessful 25% (Lower 71.4 9.6 47 Quartile) 48 498 Sideways Passes (Median) 71.4 9.6 509 Passes Attempted Opp Half (Minimum) 78.6 9.6 51 5210 Height 71.4 9.7 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 8 9 Table 15. Results for Group 1 v Group 2 Centre Midfield balanced data set comparison. The 10 best average test performance = 88.9% and the best average test error = 5.9% (Using a 11 combination of five variables).

12 Rank Variable Average Test Performance Average Test Error 13 (%) (%) 14 151 Successful Passes 25% (Lower Quartile) 66.7 10.2 16 2 Penalty Area Entries 25% (Lower Quartile) 66.7 9.6 17 183 Goals (Mean) 77.8 8.4 19 4 Backwards Passes Unsuccessful (Mean) 88.9 6.2 20 215 First Time Passes Successful (Maximum) 88.9 5.9 22 6 Backwards Passes (Median) 88.9 6.2 23 247 % Sideways Passes Successful 25% (Lower Quartile) 88.9 6.4 25 8 Total Shots 25% (Lower Quartile) 88.9 6.4 26 279 Passes Own Half (Mean) 88.9 6.9 28 2910 Dribbles 75% (Upper Quartile) 83.3 7.2 30 31 32 Table 16. Results for Group 0 v Group 2 Centre Midfield balanced data set comparison. The best average test performance = 90.9% and the best average test error = 4.8% (Using a 33 combination of ten variables). 34 35Rank Variable Average Test Performance Average Test Error 36 (%) (%) 37 381 No. Of Starts 72.7 9.6 392 Shots On Tgt Outside the Box (Maximum) 81.8 8.6 40 413 Possession Lost (Maximum) 77.3 8.0 424 Forwards Passes (Mean) 81.8 7.2 43 445 Possession Won (Median) 81.8 6.0 456 Clearances 25% (Lower Quartile) 81.8 5.5 46 477 Total Shots on Target (Mean) 90.9 5.2 488 Total Blocked Shots (Maximum) 90.9 5.2 49 509 Forwards Passes (Median) 90.9 4.9 5110 % Passes Successful Opp Half 75% (Upper Quartile) 90.9 4.8 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 17. Results for Group 0 v Group 1 Attacker balanced data set comparison. The best 8 average test performance = 80.0% and the best average test error = 8.7% (Using a combination 9 of five variables). 10 11Rank Variable Average Test Performance (%) Average Test Error (%) 12 131 International Caps 73.3 10.4 142 Number of Touches 25% (Lower Quartile) 73.3 9.2 15 163 First Time Passes (Maximum) 73.3 9.1 174 Blocks (Maximum) 73.3 8.9 18 195 Final Third Entries (Mean) 80.0 8.7 206 Passes Successful Own Half (Median) 73.3 8.9 21 227 % Successful Passes (Maximum) 73.3 9.2 238 Tackled 25% (Lower Quartile) 73.3 9.0 24 259 % Forwards Passes Successful (Minimum) 73.3 9.1 2610 % Passes Successful Opp Half (Minimum) 73.3 9.1 27 28 29 Table 18. Results for Group 1 v Group 2 Attacker balanced data set comparison. The best 30 average test performance = 81.8% and the best average test error = 7.2% (Using a combination 31 of six variables). 32 33Rank Variable Average Test Performance (%) Average Test Error (%) 34 351 U21 International Caps 63.6 11.0 362 International Caps 72.7 9.9 37 383 Unsuccessful Passes (Maximum) 72.7 9.6 394 Interceptions (Maximum) 72.7 8.7 40 415 Possession Won (Median) 81.8 7.2 426 % Unsuccessful Passes 75% (Upper Quartile) 81.8 7.2 43 447 Final Third Entries 25% (Lower Quartile) 81.8 7.8 458 Tackles (Maximum) 81.8 7.4 46 479 % Unsuccessful Passes (Minimum) 81.8 7.5 4810 Penalty Area Entries (Minimum) 81.8 7.3 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 19. Results for Group 0 v Group 2 Attacker balanced data set comparison. The best 8 average test performance = 92.3% and the best average test error = 2.6% (Using a combination 9 of ten variables). 10 11Rank Variable Average Test Performance (%) Average Test Error 12 (%) 13 1 Total Goals 76.9 7.6 14 152 Blocks 75% (Upper Quartile) 84.6 5.6 16 173 Short Passes (Minimum) 92.3 5.0 184 Passes Own Half 25% (Lower Quartile) 92.3 4.4 19 205 % Unsuccessful Headers (Maximum) 92.3 4.0 216 Crosses (Mean) 92.3 3.0 22 237 Avg Time in Possession 75% (Upper Quartile) 92.3 2.9 248 Interceptions (Median) 92.3 3.0 25 269 Passes Successful Opp Half 75% (Upper Quartile) 92.3 3.0 2710 Backwards Passes 25% (Lower Quartile) 92.3 2.6 28 29 30 Table 20. Comparison of overall average test performance scores from position models as 31 means and standard deviations. 32 33 Group Comparison Overall Average Test Overall Average Test Error (%) 34 Performance (%) 35 36 Group 0 v 1 Comparisons 80.8 ± 7.6 8.1 ± 2.3 37 Group 1 v 2 Comparisons 82.7 ± 6.6 7.1 ± 1.5 38 39 Group 0 v 2 Comparisons 94.1 ± 5.6 3.7 ± 2.1 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 21. Group 0 v 2 Centre Back model variables represented as means and standard 8 deviations for all player groupings. 9 10 Variables Group 0 Centre Back Group 2 Centre Back 11 12 0-6 Assists (Mean) 0.1 ± 0.1 0.2 ± 0.1 13 Shots on Target Inside the Box (Mean) 0.2 ± 0.2 0.3 ± 0.2 14 15 Penalty Area Entries (Minimum) 0.2 ± 0.4 0.0 ± 0.0 16 4.8 ± 18.3 9.2 ± 14.6 17 International Caps 18 Long Passes 25% (Lower Quartile) 4.3 ± 2.2 4.9 ± 2.0 19 20 Shots Outside the Box (Mean) 0.1 ± 0.2 0.1 ± 0.1 21 U21 Caps 0.3 ± 0.9 3.5 ± 6.6 22 23 Possession Gained 75% (Upper Quartile) 34.2 ± 5.5 36.7 ± 5.7 24 Avg Time in Possession (Median) 2.4 ± 2.2 2.6 ± 0.3 25 26 Clearances (Maximum) 10.9 ± 3.2 11.4 ± 3.2 27 Shots Outside the Box (Median) 0.0 ± 0.2 0.0 ± 0.0 28 29 First Time Passes (Mean) 6.5 ± 1.9 7.0 ± 1.2 30 Unsuccessful Passes (Minimum) 1.4 ± 1.8 1.0 ± 1.2 31 32 Interceptions 75% (Upper Quartile) 29.9 ± 4.2 31.1 ± 5.3 33 Possession Gained (Minimum) 21.1 ± 4.9 18.5 ± 6.3 34 35 Shots Inside the Box 25% (Lower Quartile) 0.1 ± 0.4 0.0 ± 0.1 36 Total Shots on Target (Mean) 0.2 ± 0.2 0.3 ± 0.2 37 38 Tackled (Minimum) 0.2 ± 0.7 0.0 ± 0.2 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 22. Group 0 v 2 Wide Midfield model variables represented as means and standard 8 deviations for all player groupings. 9 10 Variables Group 0 Wide Midfield Group 2 Wide Midfield 11 12 Total Goals 1.4 ± 1.9 5.5 ± 3.8 13 Passes Attempted Opp Half 75% (Upper Quartile) 16.2 ± 6.3 21.4 ± 5.8 14 15 Fouls in Defensive 3rd (Mean) 0.2 ± 0.2 0.3 ± 0.3 16 Total Shots on Tgt (Excluding Blocked) 1.0 ± 0.8 2.6 ± 1.1 17 (Maximum) 18 19 % Forwards Passes Successful (Mean) 53.4 ± 14.8 55.2 ± 9.7 20 Forward Passes Successful (Median) 5.0 ± 3.2 6.1 ± 2.2 21 22 Total Goals 1.4 ± 1.9 5.5 ± 3.8 23 Passes Attempted Opp Half 75% (Upper Quartile) 16.2 ± 6.3 21.4 ± 5.8 24 25 Fouls in Defensive 3rd (Mean) 0.2 ± 0.2 0.3 ± 0.3 26 Total Shots on Tgt (Excluding Blocked) 1.0 ± 0.8 2.6 ± 1.1 27 (Maximum) 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Table 23. Group 0 v 1 Centre Back model variables represented as means and standard 8 deviations for all player groupings. 9 10 Variables Group 0 Centre Back Group 1 Centre Back 11 12 % Passes Successful Opp Half 75% (Upper 81.2 ± 22.3 92.4 ± 13.5 13 Quartile) 14 Shooting Accuracy 75% (Upper Quartile) 23.5 ± 35.6 20.1 ± 33.8 15 16 % Successful Headers 75% (Upper Quartile) 51.0 ± 8.7 52.7 ± 6.6 17 Balls Received 75% (Upper Quartile) 16.9 ± 5.8 20.6 ± 8.9 18 19 Crosses (Median) 0.1 ± 0.3 0.1 ± 0.3 20 % First Time Passes Successful 25% (Lower 59.3 ± 13.0 59.9 ± 12.7 21 Quartile) 22 Total Shots on Target (Mean) 0.2 ± 0.2 0.3 ± 0.3 23 24 Passes Successful Opp Half (Minimum) 0.2 ± 0.5 0.3 ± 1.0 25 U21 Caps 0.3 ± 0.9 1.3 ± 3.2 26 27 Shooting Accuracy 25% (Lower Quartile) 0.0 ± 0.0 1.9 ± 11.4 28 Medium Passes (Mean) 7.9 ± 2.9 9.6 ± 5.1 29 30 Forward Passes Successful (Minimum) 1.5 ± 1.3 1.6 ± 2.5 31 Total Shots on Tgt (Excluding Blocked) (Mean) 0.1 ± 0.1 0.2 ± 0.2 32 33 Goals (Mean) 0.0 ± 0.1 0.1 ± 0.1 34 % Unsuccessful Headers 25% (Lower Quartile) 49.0 ± 8.7 47.2 ± 6.7 35 36 Long Passes (Median) 5.5 ± 2.1 6.3 ± 2.5 37 38 39 40 41

43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 Supplementary Information 8 9 The learning rate (the rate at which weights are updated as a proportion of the error) was set at 10 11 0.1 while the momentum (the proportion of the previous change in weights applied back to the 12 13 current change in weights) was 0.5 and two hidden nodes (feature detectors) were used as part 14 15 of the artificial neural network architecture in a single hidden layer. The maximum number of 16 epochs (updates of the network) used was three hundred while the maximum number of epochs 17 18 without improvement on the test was one hundred. This was used to prevent over fitting of the 19 20 model. 21 22 23 24 List of Initial 340 Variables Included in the Study 25 26 Number Variable 27 1 % Backwards Passes Successful Lower Quartile 28 2 % Backwards Passes Successful Mean 29 3 % Backwards Passes Successful Min 30 4 % Backwards Passes Unsuccessful Max 31 5 % Backwards Passes Unsuccessful Mean 32 6 % First Time Passes Successful Lower Quartile 33 34 7 % First Time Passes Successful Mean 35 8 % First Time Passes Successful Median 36 9 % First Time Passes Successful Min 37 10 % First Time Passes Successful Upper Quartile 38 11 % First Time Passes Unsuccessful Lower Quartile 39 12 % First Time Passes Unsuccessful Max 40 13 % First Time Passes Unsuccessful Mean 41 14 % First Time Passes Unsuccessful Median 42 15 % First Time Passes Unsuccessful Upper Quartile 43 16 % Forward Passes Unsuccessful Lower Quartile 44 17 % Forward Passes Unsuccessful Max 45 46 18 % Forward Passes Unsuccessful Mean 47 19 % Forward Passes Unsuccessful Median 48 20 % Forward Passes Unsuccessful Min 49 21 % Forward Passes Unsuccessful Upper Quartile 50 22 % Forwards Passes Successful Lower Quartile 51 23 % Forwards Passes Successful Max 52 24 % Forwards Passes Successful Mean 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 25 % Forwards Passes Successful Median 8 9 26 % Forwards Passes Successful Min 10 27 % Forwards Passes Successful Upper Quartile 11 28 % Passes Successful Opp Half Lower Quartile 12 29 % Passes Successful Opp Half Mean 13 30 % Passes Successful Opp Half Median 14 31 % Passes Successful Opp Half Min 15 32 % Passes Successful Opp Half Upper Quartile 16 33 % Passes Successful Own Half Lower Quartile 17 34 % Passes Successful Own Half Max 18 35 % Passes Successful Own Half Mean 19 20 36 % Passes Successful Own Half Median 21 37 % Passes Successful Own Half Min 22 38 % Passes Successful Own Half Upper Quartile 23 39 % Sideways Passes Successful Lower Quartile 24 40 % Sideways Passes Successful Mean 25 41 % Sideways Passes Successful Median 26 42 % Sideways Passes Successful Min 27 43 % Sideways Passes Successful Upper Quartile 28 44 % Sideways Passes Unsuccessful Lower Quartile 29 45 % Sideways Passes Unsuccessful Max 30 46 % Sideways Passes Unsuccessful Mean 31 32 47 % Sideways Passes Unsuccessful Median 33 48 % Sideways Passes Unsuccessful Upper Quartile 34 49 % Successful Headers Lower Quartile 35 50 % Successful Headers Max 36 51 % Successful Headers Mean 37 52 % Successful Headers Median 38 53 % Successful Headers Min 39 54 % Successful Headers Upper Quartile 40 55 % Successful Passes Lower Quartile 41 56 % Successful Passes Max 42 43 57 % Successful Passes Mean 44 58 % Successful Passes Median 45 59 % Successful Passes Min 46 60 % Successful Passes Upper Quartile 47 61 % Unsuccessful Headers Lower Quartile 48 62 % Unsuccessful Headers Max 49 63 % Unsuccessful Headers Mean 50 64 % Unsuccessful Headers Median 51 65 % Unsuccessful Headers Min 52 66 % Unsuccessful Headers Upper Quartile 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 67 % Unsuccessful Passes Lower Quartile 8 9 68 % Unsuccessful Passes Max 10 69 % Unsuccessful Passes Mean 11 70 % Unsuccessful Passes Median 12 71 % Unsuccessful Passes Min 13 72 % Unsuccessful Passes Upper Quartile 14 73 0-6 Assists Mean 15 74 Age 16 75 Avg Time in Possession Lower Quartile 17 76 Avg Time in Possession Max 18 77 Avg Time in Possession Mean 19 20 78 Avg Time in Possession Median 21 79 Avg Time in Possession Min 22 80 Avg Time in Possession Upper Quartile 23 81 Avg Touches Max 24 82 Backwards Passes Lower Quartile 25 83 Backwards Passes Max 26 84 Backwards Passes Mean 27 85 Backwards Passes Median 28 86 Backwards Passes Min 29 87 Backwards Passes Successful Lower Quartile 30 88 Backwards Passes Successful Max 31 32 89 Backwards Passes Successful Mean 33 90 Backwards Passes Successful Median 34 91 Backwards Passes Successful Min 35 92 Backwards Passes Successful Upper Quartile 36 93 Backwards Passes Unsuccessful Max 37 94 Backwards Passes Unsuccessful Mean 38 95 Backwards Passes Upper Quartile 39 96 Balls Received Lower Quartile 40 97 Balls Received Max 41 98 Balls Received Mean 42 43 99 Balls Received Median 44 100 Balls Received Min 45 101 Balls Received Upper Quartile 46 102 Blocks Max 47 103 Blocks Mean 48 104 Blocks Median 49 105 Blocks Upper Quartile 50 106 Clearances Lower Quartile 51 107 Clearances Max 52 108 Clearances Mean 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 109 Clearances Median 8 9 110 Clearances Upper Quartile 10 111 Corners Conceded Max 11 112 Corners Conceded Mean 12 113 Crosses Lower Quartile 13 114 Crosses Max 14 115 Crosses Mean 15 116 Crosses Median 16 117 Crosses Upper Quartile 17 118 Dribbles Max 18 119 Dribbles Mean 19 20 120 Dribbles Upper Quartile 21 121 Final Third Entries Lower Quartile 22 122 Final Third Entries Max 23 123 Final Third Entries Mean 24 124 Final Third Entries Median 25 125 Final Third Entries Min 26 126 Final Third Entries Upper Quartile 27 127 First Time Passes Lower Quartile 28 128 First Time Passes Max 29 129 First Time Passes Mean 30 130 First Time Passes Median 31 32 131 First Time Passes Min 33 132 First Time Passes Successful Lower Quartile 34 133 First Time Passes Successful Max 35 134 First Time Passes Successful Mean 36 135 First Time Passes Successful Median 37 136 First Time Passes Successful Min 38 137 First Time Passes Successful Upper Quartile 39 138 First Time Passes Unsuccessful Max 40 139 First Time Passes Unsuccessful Mean 41 140 First Time Passes Unsuccessful Upper Quartile 42 43 141 First Time Passes Upper Quartile 44 142 Forward Passes Successful Lower Quartile 45 143 Forward Passes Successful Max 46 144 Forward Passes Successful Mean 47 145 Forward Passes Successful Median 48 146 Forward Passes Successful Min 49 147 Forward Passes Successful Upper Quartile 50 148 Forwards Passes Lower Quartile 51 149 Forwards Passes Max 52 150 Forwards Passes Mean 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 151 Forwards Passes Median 8 9 152 Forwards Passes Min 10 153 Forwards Passes Unsuccessful Lower Quartile 11 154 Forwards Passes Unsuccessful Max 12 155 Forwards Passes Unsuccessful Mean 13 156 Forwards Passes Unsuccessful Median 14 157 Forwards Passes Unsuccessful Min 15 158 Forwards Passes Unsuccessful Upper Quartile 16 159 Forwards Passes Upper Quartile 17 160 Fouled Max 18 161 Fouled Mean 19 20 162 Fouled Upper Quartile 21 163 Fouls 22 164 Fouls in Defensive 3rd Mean 23 165 Fouls Max 24 166 Fouls Mean 25 167 Goals Mean 26 168 Headers Lower Quartile 27 169 Headers Max 28 170 Headers Mean 29 171 Headers Median 30 172 Headers Min 31 32 173 Headers Upper Quartile 33 174 Height 34 175 Interceptions Lower Quartile 35 176 Interceptions Max 36 177 Interceptions Mean 37 178 Interceptions Median 38 179 Interceptions Min 39 180 Interceptions Upper Quartile 40 181 International Caps 41 182 Long Passes Lower Quartile 42 43 183 Long Passes Max 44 184 Long Passes Mean 45 185 Long Passes Median 46 186 Long Passes Min 47 187 Long Passes Upper Quartile 48 188 Medium Passes Lower Quartile 49 189 Medium Passes Max 50 190 Medium Passes Mean 51 191 Medium Passes Median 52 192 Medium Passes Min 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 193 Medium Passes Upper Quartile 8 9 194 No. of 90 Mins App. 10 195 No. Of Starts 11 196 Number of Possessions Lower Quartile 12 197 Number of Possessions Max 13 198 Number of Possessions Mean 14 199 Number of Possessions Median 15 200 Number of Possessions Min 16 201 Number of Possessions Upper Quartile 17 202 Number of Touches Lower Quartile 18 203 Number of Touches Max 19 20 204 Number of Touches Mean 21 205 Number of Touches Median 22 206 Number of Touches Min 23 207 Number of Touches Upper Quartile 24 208 Offsides Mean 25 209 Passes Attempted Opp Half Lower Quartile 26 210 Passes Attempted Opp Half Max 27 211 Passes Attempted Opp Half Mean 28 212 Passes Attempted Opp Half Median 29 213 Passes Attempted Opp Half Min 30 214 Passes Attempted Opp Half Upper Quartile 31 32 215 Passes Lower Quartile 33 216 Passes Max 34 217 Passes Mean 35 218 Passes Median 36 219 Passes Min 37 220 Passes Own Half Lower Quartile 38 221 Passes Own Half Max 39 222 Passes Own Half Mean 40 223 Passes Own Half Median 41 224 Passes Own Half Min 42 43 225 Passes Own Half Upper Quartile 44 226 Passes Successful Opp Half Lower Quartile 45 227 Passes Successful Opp Half Max 46 228 Passes Successful Opp Half Mean 47 229 Passes Successful Opp Half Median 48 230 Passes Successful Opp Half Min 49 231 Passes Successful Opp Half Upper Quartile 50 232 Passes Successful Own Half Lower Quartile 51 233 Passes Successful Own Half Max 52 234 Passes Successful Own Half Mean 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 235 Passes Successful Own Half Median 8 9 236 Passes Successful Own Half Min 10 237 Passes Successful Own Half Upper Quartile 11 238 Passes Upper Quartile 12 239 Penalty Area Entries Lower Quartile 13 240 Penalty Area Entries Max 14 241 Penalty Area Entries Mean 15 242 Penalty Area Entries Median 16 243 Penalty Area Entries Min 17 244 Penalty Area Entries Upper Quartile 18 245 Playing % 19 20 246 Possession Gained Lower Quartile 21 247 Possession Gained Max 22 248 Possession Gained Mean 23 249 Possession Gained Median 24 250 Possession Gained Min 25 251 Possession Gained Upper Quartile 26 252 Possession Lost Lower Quartile 27 253 Possession Lost Max 28 254 Possession Lost Mean 29 255 Possession Lost Median 30 256 Possession Lost Min 31 32 257 Possession Lost Upper Quartile 33 258 Possession Won Lower Quartile 34 259 Possession Won Max 35 260 Possession Won Mean 36 261 Possession Won Median 37 262 Possession Won Min 38 263 Possession Won Upper Quartile 39 264 Shooting Accuracy Lower Quartile 40 265 Shooting Accuracy Mean 41 266 Shooting Accuracy Median 42 43 267 Shooting Accuracy Upper Quartile 44 268 Short Passes Lower Quartile 45 269 Short Passes Max 46 270 Short Passes Mean 47 271 Short Passes Median 48 272 Short Passes Min 49 273 Short Passes Upper Quartile 50 274 Shots Inside the Box Lower Quartile 51 275 Shots Inside the Box Max 52 276 Shots Inside the Box Mean 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 277 Shots Inside the Box Upper Quartile 8 9 278 Shots Off Target (Exc. Blocked) Max 10 279 Shots Off Target (Exc. Blocked) Mean 11 280 Shots on Target Inside the Box Max 12 281 Shots on Target Inside the Box Mean 13 282 Shots On Tgt Outside the Box Max 14 283 Shots On Tgt Outside the Box Mean 15 284 Shots Outside the Box Max 16 285 Shots Outside the Box Mean 17 286 Shots Outside the Box Median 18 287 Sideways Passes Lower Quartile 19 20 288 Sideways Passes Max 21 289 Sideways Passes Mean 22 290 Sideways Passes Median 23 291 Sideways Passes Min 24 292 Sideways Passes Successful Lower Quartile 25 293 Sideways Passes Successful Max 26 294 Sideways Passes Successful Mean 27 295 Sideways Passes Successful Median 28 296 Sideways Passes Successful Min 29 297 Sideways Passes Successful Upper Quartile 30 298 Sideways Passes Unsuccessful Max 31 32 299 Sideways Passes Unsuccessful Mean 33 300 Sideways Passes Upper Quartile 34 301 Successful Passes Lower Quartile 35 302 Successful Passes Max 36 303 Successful Passes Mean 37 304 Successful Passes Median 38 305 Successful Passes Min 39 306 Successful Passes Upper Quartile 40 307 Tackled Lower Quartile 41 308 Tackled Max 42 43 309 Tackled Mean 44 310 Tackled Median 45 311 Tackled Min 46 312 Tackled Upper Quartile 47 313 Tackles Lower Quartile 48 314 Tackles Max 49 315 Tackles Mean 50 316 Tackles Median 51 317 Tackles Upper Quartile 52 318 Total Appearances 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 319 Total Assists 8 9 320 Total Blocked Shots Max 10 321 Total Blocked Shots Mean 11 322 Total Goals 12 323 Total Minutes 13 324 Total Shots Lower Quartile 14 325 Total Shots Max 15 326 Total Shots Mean 16 327 Total Shots Median 17 328 Total Shots on Target Max 18 329 Total Shots on Target Mean 19 20 330 Total Shots on Tgt (Excluding Blocked) Max 21 331 Total Shots on Tgt (Excluding Blocked) Mean 22 332 Total Shots Upper Quartile 23 333 U21 Caps 24 334 Unsuccessful Passes Lower Quartile 25 335 Unsuccessful Passes Max 26 336 Unsuccessful Passes Mean 27 337 Unsuccessful Passes Median 28 338 Unsuccessful Passes Min 29 339 Unsuccessful Passes Upper Quartile 30 340 Yellow Cards 31

32 33 List of 196 Variables Excluded from the Study 34 35 Number Variable 36 1 % Backwards Passes Successful Max 37 38 2 % Backwards Passes Successful Median 39 3 % Backwards Passes Successful Upper Quartile 40 % Backwards Passes Unsuccessful Lower 4 Quartile 41 42 5 % Backwards Passes Unsuccessful Median 43 6 % Backwards Passes Unsuccessful Min 44 % Backwards Passes Unsuccessful Upper 7 Quartile 45 46 8 % First Time Passes Successful Max 47 9 % First Time Passes Unsuccessful Min 48 10 % Passes Successful Opp Half Max 49 11 % Sideways Passes Successful Max 50 12 % Sideways Passes Unsuccessful Min 51 13 0-6 Assists Lower Quartile 52 14 0-6 Assists Max 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 15 0-6 Assists Median 8 9 16 0-6 Assists Min 10 17 0-6 Assists Upper Quartile 11 18 1st Assist Lower Quartile 12 19 1st Assist Max 13 20 1st Assist Mean 14 21 1st Assist Median 15 22 1st Assist Min 16 23 1st Assist Upper Quartile 17 24 2nd Assist Lower Quartile 18 25 2nd Assist Max 19 20 26 2nd Assist Mean 21 27 2nd Assist Median 22 28 2nd Assist Min 23 29 2nd Assist Upper Quartile 24 30 3rd Assist Lower Quartile 25 31 3rd Assist Max 26 32 3rd Assist Mean 27 33 3rd Assist Median 28 34 3rd Assist Min 29 35 3rd Assist Upper Quartile 30 36 4th Assist Lower Quartile 31 32 37 4th Assist Max 33 38 4th Assist Mean 34 39 4th Assist Median 35 40 4th Assist Min 36 41 4th Assist Upper Quartile 37 42 5th Assist Lower Quartile 38 43 5th Assist Max 39 44 5th Assist Mean 40 45 5th Assist Median 41 46 5th Assist Min 42 43 47 5th Assist Upper Quartile 44 48 6th Assist Lower Quartile 45 49 6th Assist Max 46 50 6th Assist Mean 47 51 6th Assist Median 48 52 6th Assist Min 49 53 6th Assist Upper Quartile 50 54 Avg Touches Lower Quartile 51 55 Avg Touches Mean 52 56 Avg Touches Median 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 57 Avg Touches Min 8 9 58 Avg Touches Upper Quartile 10 59 Backwards Passes Unsuccessful Lower Quartile 11 60 Backwards Passes Unsuccessful Median 12 61 Backwards Passes Unsuccessful Min 13 62 Backwards Passes Unsuccessful Upper Quartile 14 63 Blocks Lower Quartile 15 64 Blocks Min 16 65 Clearances Min 17 66 Corners Conceded Lower Quartile 18 67 Corners Conceded Median 19 20 68 Corners Conceded Min 21 69 Corners Conceded Upper Quartile 22 70 Corners from LEFT Lower Quartile 23 71 Corners from LEFT Max 24 72 Corners from LEFT Mean 25 73 Corners from LEFT Median 26 74 Corners from LEFT Min 27 75 Corners from LEFT Upper Quartile 28 76 Corners from RIGHT Lower Quartile 29 77 Corners from RIGHT Max 30 78 Corners from RIGHT Mean 31 32 79 Corners from RIGHT Median 33 80 Corners from RIGHT Min 34 81 Corners from RIGHT Upper Quartile 35 82 Corners Taken Lower Quartile 36 83 Corners Taken Max 37 84 Corners Taken Mean 38 85 Corners Taken Median 39 86 Corners Taken Min 40 87 Corners Taken Upper Quartile 41 88 Crosses from LEFT Lower Quartile 42 43 89 Crosses from LEFT Max 44 90 Crosses from LEFT Mean 45 91 Crosses from LEFT Median 46 92 Crosses from LEFT Min 47 93 Crosses from LEFT Upper Quartile 48 94 Crosses from RIGHT Lower Quartile 49 95 Crosses from RIGHT Max 50 96 Crosses from RIGHT Mean 51 97 Crosses from RIGHT Median 52 98 Crosses from RIGHT Min 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 99 Crosses from RIGHT Upper Quartile 8 9 100 Crosses Min 10 101 Dribbles Lower Quartile 11 102 Dribbles Median 12 103 Dribbles Min 13 104 First Time Passes Unsuccessful Lower Quartile 14 105 First Time Passes Unsuccessful Median 15 106 First Time Passes Unsuccessful Min 16 107 Fouled Lower Quartile 17 108 Fouled Median 18 109 Fouled Min 19 20 110 Fouls in Defensive 3rd Lower Quartile 21 111 Fouls in Defensive 3rd Max 22 112 Fouls in Defensive 3rd Median 23 113 Fouls in Defensive 3rd Min 24 114 Fouls in Defensive 3rd Upper Quartile 25 115 Fouls Lower Quartile 26 116 Fouls Median 27 117 Fouls Min 28 118 Fouls Upper Quartile 29 119 Free Kicks Taken Lower Quartile 30 120 Free Kicks Taken Max 31 32 121 Free Kicks Taken Mean 33 122 Free Kicks Taken Median 34 123 Free Kicks Taken Min 35 124 Free Kicks Taken Upper Quartile 36 125 Goals Lower Quartile 37 126 Goals Max 38 127 Goals Median 39 128 Goals Min 40 129 Goals Upper Quartile 41 130 Offsides Lower Quartile 42 43 131 Offsides Max 44 132 Offsides Median 45 133 Offsides Min 46 134 Offsides Upper Quartile 47 135 Own Goals Lower Quartile 48 136 Own Goals Max 49 137 Own Goals Mean 50 138 Own Goals Median 51 139 Own Goals Min 52 140 Own Goals Upper Quartile 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 141 Playing Time Lower Quartile 8 9 142 Playing Time Max 10 143 Playing Time Mean 11 144 Playing Time Median 12 145 Playing Time Min 13 146 Playing Time Upper Quartile 14 147 Red Cards 15 148 Red Cards Lower Quartile 16 149 Red Cards Max 17 150 Red Cards Mean 18 151 Red Cards Median 19 20 152 Red Cards Min 21 153 Red Cards Upper Quartile 22 154 Shooting Accuracy Max 23 155 Shooting Accuracy Min 24 156 Shots Inside the Box Median 25 157 Shots Inside the Box Min 26 158 Shots Off Target (Exc. Blocked) Lower Quartile 27 159 Shots Off Target (Exc. Blocked) Median 28 160 Shots Off Target (Exc. Blocked) Min 29 161 Shots Off Target (Exc. Blocked) Upper Quartile 30 162 Shots on Target Inside the Box Lower Quartile 31 32 163 Shots on Target Inside the Box Median 33 164 Shots on Target Inside the Box Min 34 165 Shots on Target Inside the Box Upper Quartile 35 166 Shots On Tgt Outside the Box Lower Quartile 36 167 Shots On Tgt Outside the Box Median 37 168 Shots On Tgt Outside the Box Min 38 169 Shots On Tgt Outside the Box Upper Quartile 39 170 Shots Outside the Box Lower Quartile 40 171 Shots Outside the Box Min 41 172 Shots Outside the Box Upper Quartile 42 43 173 Sideways Passes Unsuccessful Lower Quartile 44 174 Sideways Passes Unsuccessful Median 45 175 Sideways Passes Unsuccessful Min 46 176 Sideways Passes Unsuccessful Upper Quartile 47 177 Tackles Min 48 178 Total Blocked Shots Lower Quartile 49 179 Total Blocked Shots Median 50 180 Total Blocked Shots Min 51 181 Total Blocked Shots Upper Quartile 52 182 Total Shots Min 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 183 Total Shots on Target Lower Quartile 8 9 184 Total Shots on Target Median 10 185 Total Shots on Target Min 11 186 Total Shots on Target Upper Quartile 12 Total Shots on Tgt (Excluding Blocked) Lower 13 187 Quartile 14 188 Total Shots on Tgt (Excluding Blocked) Median 15 189 Total Shots on Tgt (Excluding Blocked) Min 16 Total Shots on Tgt (Excluding Blocked) Upper 17 190 Quartile 18 191 Yellow Cards Lower Quartile 19 192 Yellow Cards Max 20 193 Yellow Cards Mean 21 194 Yellow Cards Median 22 195 Yellow Cards Min 23 196 Yellow Cards Upper Quartile 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Manuscript - anonymous

Identifying playing talent in professional football using artificial

neural networks

Keywords: Soccer, Talent Identification, Premier League, Championship, Artificial

Intelligence

Abstract

The aim of the current study was to objectively identify position-specific key performance

indicators in professional football that predict out-field players league status. The sample

consisted of 966 out-field players who completed the full 90 minutes in a match during the

2008/09 or 2009/10 season in the Football League Championship. Players were assigned to

one of three categories (group 0, 1 and 2) based on where they completed most of their match

time in the following season, and then split based on five positions including full backs (n =

205), centre backs (n = 193), centre midfielders (n = 205), wide midfielders (n = 168) and

forwards (n = 195). 340 performance, biographical and esteem variables were analysed using

a Stepwise Artificial Neural Network approach. The models correctly predicted between 72.7%

and 100% of test cases (Mean prediction of models = 85.9%), the test error ranged from 1.0%

to 9.8% (Mean test error of models = 6.3%). Variables related to passing, shooting, regaining

possession and international appearances were key factors in the predictive models. This is

highly significant as objective position-specific predictors of players league status have not

previously been published. The method could be used to aid the identification and comparison

of transfer targets as part of the due diligence process in professional football.

Introduction

Coaches and decision makers in professional football have traditionally used subjective

observations to assess the performance of their team, to review the strengths and weaknesses of future opponents and to identify potential signings (Carling, Williams and Reilly, 2005).

Match analysis research into the individual’s performance in football has focused heavily on the physical demands of the sport (Carling, 2013). Research led by sport scientists with a heavy focus upon the physical aspects of performance in football has not managed to identify key predictors of match outcome or team success (Bradley et al., 2016; Carling, 2013).

However, studies investigating physical performance during matches have also incorporated technical elements and provided some insights into the successful performance of players and teams (Bradley et al., 2013; Bradley et al., 2016; Dellal et al., 2010; Dellal et al., 2011).

Technical factors have been identified that are prominent predictors of team success and match outcome. Shots, shots on target and ball possession are the most commonly reported predictors

(Castellano, Casamachina and Lago, 2012; Lagos-Penas, Lago-Ballesteros, Dellal and Gomez,

2010; Liu, Gomez, Lago-Penas and Sampaio, 2015). There has been a heavy emphasis on the attacking aspects of play linked to success and more detailed analysis is required into the defensive aspects of play to gain a greater understanding of the game.

Following on from the research into team success and physical profiles, there has been an increasing interest in the technical profiles of players. Studies have found positional differences in Ligue 1 in France, the Premier League in England and in Spain’s La Liga (Dellal, Wong,

Moalla and Chamari 2010; Dellal et al., 2011). The development of advanced computer systems has supported a greater understanding of position profiles in football. However, most of the research to date has used subjective methods to select variables for analysis (Taylor,

Mellalieu and James, 2004) or they have replicated indicators used in other studies

(Andrzejewski, Konefal, Chmura, Kowalczuk and Chmura, 2016). Using subjective criteria selection rather than exploring a broad spectrum of the data points has meant that many variables have yet to be assessed. Therefore, the impact of these variables upon playing success and career progression is unknown.

A broader analysis of player performance and career progression has been provided by using artificial neural networks to assess a wide range of variables (Barron, Ball, Robins and

Sunderland, 2018). Artificial neural networks have been shown to be better at identifying patterns in complex non-linear data sets than forms of regression analysis and they are capable of generalizing results to solve real world problems (Basheer and Hajmeer, 2000; Lancashire,

Lemetre and Ball, 2009; Tu, 1996). In a football context, artificial neural networks have been shown to be capable of creating models that can differentiate between specific groups and identify key variables that predict career progression (Barron et al, 2018). Previous studies though have been limited by assessing players regardless of position and their accuracy could be improved by making assessments of each position and the creation of position-specific career progression models.

To the authors’ knowledge there has not been an objective study carried out to develop a position-specific predictive model that could support the scouting and recruitment process in professional football. The efficient and effective identification and assessment of transfer targets is a key aspect of any professional football club and requires a thorough due diligence process. Therefore, the aim of the current study was to develop an objective model to identify position-specific key performance indicators in professional football that predict out-field players league status using an artificial neural network.

Methods

Players and Match Data

The basis of the current study followed Barron et al’s (2018) method but looked to build on it and focus on position-specific assessments of players. The sample consisted of 966 out-field players (mean ± SD age and height: 25 ± 4 yr, 1.81 ± 0.06 m) who had completed a full 90 minutes in the English Football League Championship during the 2008/09 and 2009/10 seasons

(Table 1). Technical performance data and biographical data was collected using ProZone’s

MatchViewer software (ProZone Sports Ltd., Leeds, UK), the official Football League website

(www.efl.com) and Scout7 Ltd’s (Birmingham, UK) site. The Prozone MatchViewer system was used to collect performance data due to its accurate inter-observer agreement for the number and type of events (Bradley, O’Donoghue, Wooster and Tordoff, 2007). The data collected from the Prozone MatchViewer software was made available by STATS LLC

(Chicago, USA). Institutional ethical approval was attained from the Non-Invasive Human

Ethics Committee at Nottingham Trent University.

In total, 536 variables were collected in including the total number, accuracy (% success), means, medians and upper and lower quartiles of passes, tackles, possessions regained, clearances and shots. Additional data on total appearances, playing percentage, total goals and assists, international appearances and heights was also collected. The data set originally included 536 variables but low variance statistics were removed. After removing low variance data points, the data set included 340 variables for comparison. Each player’s data was converted into mean 90-minute performance data before they were assigned to one of three categories (group 0, group 1 and group 2).

Player Grouping

Players were allocated to one of five positions (full back, centre back, wide midfielder, central midfielder or attacker) based on where they spent most of their playing time during the season

(See Table 1). They were then assigned to one of three categories (group 0, group 1 and group

2) based on where they went on to complete most of their match time during the following season. The first category (group 0) included the players who completed most of their match time in a lower league during the following season. The second group (group 1) included those players who completed most their match time in the English Football League Championship during the following season and the final category (group 2) contained the players who progressed to complete most their match time in the English Premier League during the following season.

Sample sizes for each comparison were balanced to have an equal number of cases using a random number selector (i.e. 24 full backs were selected from group 0 to have an equal number of cases for comparisons to group 2). Players who played on loan during the 2008/09 and

2009/10 seasons were included in the study but players who moved to a club outside England were excluded due to the complications in assessing the merits of foreign competitions against those in England. The five positions for each category of playing status were subsequently analysed using a Stepwise Artificial Neural Network approach to identify the optimal collection of variables for predicting playing status.

Artificial Neural Network Model

The artificial neural network modelling was based on the approach previously used in gene profiling with breast cancer data (Lancashire et al., 2009) and used in assessing player performances in the Football League Championship (Barron et al., 2018). It used in house code written in Microsoft visual basic 6 to call Statistica 10.0 (Statsoft Inc., Tulsa, USA) artificial neural network model at each loop of the stepwise procedure and output the results in a text format.

Before training the artificial neural network, the data was randomly split (60% for training purposes, 20% for validation and 20% blind test cases). A Monte-Carlo cross validation procedure was used to avoid over-fitting of the data. The artificial neural network modelling involved a multi-layer perceptron architecture with a feed-forward back-propagation algorithm. This algorithm used a sigmoidal transfer function and weights were updated by feedback from errors. Results were provided for the average test performance and the average test error. The average test performance indicates the percentage of test cases that are correctly predicted. The average test error is the root mean square error for the test data set, this indicates the difference between the values predicted by the model and the actual values of the test data set (Salkind, 2010). Further information on the artificial neural network model can be viewed in the supplementary information.

Results

Analysis using the artificial neural network created fifteen position-specific models to predict out-field player’s league status. The models correctly predicted between 72.7% and 100% of test cases (Mean prediction of models = 85.9%), the test error ranged from 1.0% to 9.8% (Mean test error of models = 6.3%). Fourteen models correctly predicted 75% or more of the test players league status with an error of 9.6% or less (Table 2). The fifteen models, created in total, contained between five and twenty variables to predict the players league status with 134 variables in total being required to make the position models. The most prominent set of variables were those related to the players passing ability, with 48 of the 134 variables (35.8%) being passing statistics. The next most prominent type of variable was related to players shooting. In total, twenty variables (14.9%) related to shooting were selected in the models.

Statistics related to regaining possession accounted for eleven of the variables (8.2%) selected.

Variables related to international appearances were selected nine times (6.7%). A full outline of the categories of variables selected can be viewed in full (Table 3).

Full Back Models

The performance of the full back models as a group were the lowest of the five positions

(Average test performance = 78.4% ± 8.0% and average test error = 8.6% ± 1.7%) (Table 4).

The group 0 v1 comparison had the lowest average test performance and highest test error out of all the models created (Average test performance = 72.7% and average test error = 9.8%).

Total appearances and mean percentage of backwards passes successful were key variables in the model (Table 5). The group 1 v 2 comparison had an average test performance of 75% and a test error of 9.3%. The percentage of sideways passes successful (upper quartile) and median total shots were the most prominent variables in the model (Table 6). The best full back model was for group 0 v 2 which had an average test performance of 87.5% and a test error of 6.6%.

The mean goals scored and minimum headers were the two most prominent factors in the model

(Table 7).

Centre Back Models

The performance of the centre back models as a group had an average test performance of

94.4% ± 5.1% and an average test error of 3.5% ± 2.3%. The group 0 v 1 model had an average test performance of 93.3% and an average test error of 4.1% using twenty variables. The percentage of successful passes in the opposition half (upper quartile) and shooting accuracy

(upper quartile) were the most prominent variables in the model (Table 8). The group 1 v 2 model had the lowest average test performance and highest test error of the three centre back models (average test performance = 90.0% and average test error = 5.5%). Backwards passes

(lower quartile) and maximum short passes were the top two factors in the model (Table 9).

The group 0 v 2 model had the highest average test performance of any model and the lowest test error of any model (average test performance = 100% and test error = 1.0%). The group 0 v 2 centre back model contained eighteen variables with 0-6 assists mean (group 0 = 0.1 ± 0.1, group 2 = 0.2 ± 0.1), mean shots on target inside the box (group 0 = 0.2 ± 0.2, group 2 = 0.3 ±

0.2) and minimum penalty area entries (Group 0 = 0.2 ± 0.4, Group 2 = 0 ± 0) being key variables (Table 10).

Wide Midfielder Models

The wide midfield models group average test performance was 84.8% ± 13.2% with an average test error of 6.3% ± 2.5%. The group 0 v 1 model had an average test performance of 79.4% and a test error of 8.2%. The maximum percentage of unsuccessful headers and forward passes successful (upper quartile) were the biggest predictors in the model (Table 11). The group 1 v

2 model had an average test performance of 77.8% and a test error of 7.4%. U21 international caps and median forward passes unsuccessful were the most prominent factors in the model

(Table 12). The group 0 v 2 model had the second highest average test performance and third lowest test error of all the models created (average test performance = 100% and a test error of

3.4%). The group 0 v 2 wide midfielder model contained six variables including: total goals

(group 0 = 1.4 ± 1.9, group 2 = 5.5 ± 3.8), passes attempted opposition half upper quartile

(group 0 = 16.2 ± 6.3, group 2 = 21.4 ± 5.8), fouls in the defensive third mean (group 0 = 0.2

± 0.2, group 2 = 0.3 ± 0.3), total shots on target (excluding blocked) maximum (group 0 = 1.0

± 0.8, group 2 = 2.6 ± 1.1), % forward passes successful mean (group 0 = 53.4% ± 14.8%, group 2 = 55.2% ± 9.7%) and forward passes successful median (group 0 = 5.0 ± 3.2, group 2

= 6.1 ± 2.2) (Table 13).

Centre Midfielder Models

The best overall average was for the centre midfielder’s models as a group (Average test performance = 86.1% ± 6.6 and average test error = 6.8% ± 2.5). The group 0 v 1 model had the lowest average test performance of the centre midfield models and had the second highest test error across all models (Average test performance = 78.6% and average test error = 9.6%).

Fouls and maximum first time passes were the most prominent variables in the model (Table

14). The group 1 v 2 model had an average test performance of 88.9% and a test error of 5.9%.

Successful passes (lower quartile) and penalty area entries (lower quartile) were two key variables in the model (Table 15). The group 0 v 2 model had an average test performance of

90.9% and a test error of 4.8%. The number of starts and maximum shots on target outside the box were the highest predictors in the model (Table 16).

Attacker Models

The performance of the attacker models as a group had an average test performance of 84.7%

± 6.6% and an average test error of 6.2% ± 3.2%. The group 0 v 1 model had an average test performance of 80% and an average test error of 8.7%. The most prominent variables in the model were international caps and the number of touches (lower quartile) (Table 17). The group

1 v 2 model had an average test performance of 81.8% and a test error of 7.2%. U21 international caps and international caps were the two most important factors in the model

(Table 18). The best average test performance for an attacker model was recorded for the group

0 v 2 model and it had the lowest overall test error of all models (average test performance =

92.3% and test error = 2.6%). The group 0 v 2 attacker model contained ten variables with total goals (group 0 = 2.7 ± 3.0, group 2 = 10.0 ± 6.2), blocks upper quartile (group 0 = 1.0 ± 0.5, group 2 = 1.5 ± 0.7) and short passes minimum (group 0 = 4.9 ± 2.5, group 2 = 4.3 ± 2.4) being key variables (Table 19).

Model Comparisons

The models produced comparing positions for group 0 v 1 had the lowest overall average test performance and highest test error (mean test performance = 80.8% ± 7.6% and average test error = 8.1% ± 2.3%). The overall average test performance across all five positions for group

1 v 2 comparisons was 82.7% ± 6.6% and the average test error was 7.1% ± 1.5. The highest overall average test performance across the five positions was for group 0 v 2 (mean test performance = 94.1% ± 5.6% and average test error = 3.7% ± 2.1%) (Table 20). The top three models produced by the neural network were for 0 v 2 centre back (average test performance

100% and 1.0% test error), group 0 v 2 wide midfielder (average test performance 100% and

3.4% test error) and group 0 v 1 centre back (average test performance 93.3% and 4.1% test error). The means and standard deviations for key variables for the top three models can be reviewed in full (Tables 21-23).

Discussion

The aim of the current study was to develop objective models that identified position-specific key performance indicators that predict out-field players league status. The artificial neural network created fifteen position-specific models to predict out-field players league status. The artificial neural network’s ability to correctly classify more than 75% of the players league status for fourteen different position comparisons is a key result. The models were able to accurately predict the league status of players being transferred between different levels of competition and those who were promoted or demoted with their team. Therefore, they did not simply predict subjective scouting decisions.

The results surpass the previous prediction rates reported using artificial neural networks in other team sports, such as those undertaken in cricket (Iyer and Sharda, 2009; Saikia,

Bhattacharjee and Lemmer, 2012). Their studies could predict classification of batsmen and bowlers with accuracy levels ranging from 49% to 77%. In individual sports, artificial neural networks have been able to predict 80.2% of gymnast’s future classifications based on a multi- dimensional testing process (Pion, Hohmann, Liu, Lenoir and Segers, 2017). Therefore, the current artificial neural network prediction rates are among the highest reported to date in an athlete classification study.

Passing Variables

The most prominent set of variables were those related to the players passing ability, with 48 of the 134 total variables included in models (35.8%) being passing statistics. Many passing variables have been highlighted previously as key indicators when differentiating between players of various playing levels and linked to team success (Bradley et al., 2013; Rampinini,

Impellizzerie, Castagna, Coutts and Wisloff, 2009). Comparisons between players within the

English football pyramid showed that players in the Premier League performed a greater number of total passes, successful passes and forward passes (Bradley et al., 2013). Out of the

48 passing variables identified in the models, 29 were related to the success of the passing variables. The passing variables related to their success were a mixture of 27 different statistics accounting for the direction (forwards, sideways and backwards) of the pass, the origin of the pass (own half or opposition half) and the mean, median, minimum, maximum and upper and lower quartile figure for different variables. In further agreement with Bradley and colleagues (2013) findings, thirteen of the passing variables were related to forward passing. Forward passes have been shown to have the lowest chance of success when compared to sideways or backwards passes (Szczepanski and McHale,

2016). Yet, to create scoring opportunities and in turn score goals players are required to progress the play with forward passing. Variables relating to forward passes appeared in models for full backs (group 0 v 1 and group 0 v 2), centre backs (group 0 v 1), wide midfield

(group 0 v 1, group 1 v 2 and group 0 v 2), centre midfield (group 0 v 1 and group 0 v 2) but did not feature prominently in any models for attackers. This would appear logical as attackers play in more advanced areas and have fewer opportunities to perform forward passes. The prevalence of forward passing variables for a number of positions and different comparisons highlights its importance in playing success.

The current study also highlighted two variables related to short passing with the maximum and minimum variables being selected in two models (group 1 v 2 centre back and group 0 v 2 attacker). Research into factors that distinguish between top four and bottom four English

Premier League teams highlighted short passes as a key variable (Adams, Morgans,

Sacramento, Morgan and Williams, 2013). Specifically, the mean frequency of successful short passes played by centre backs and full backs was the biggest factor differentiating between the two groups.

Using the artificial neural network methodology has highlighted some overlap between factors previously identified by research articles. The current study has also identified novel findings for variables that have not previously been analysed or identified as key variables. Eight passing variables were related to those in the opposition half and they appeared in six different position models (group 0 v 1 centre back, group 0 v 1 and 0 v 2 centre midfield, group 0 v 1 and 0 v 2 wide midfield and 0 v 2 attacker models). Six of the variables were also related to first time passes played and they appeared in the group 0 v 1 and 0 v 2 centre back, group 1 v

2 full back, group 0 v 1 and 1 v 2 centre midfield and group 0 v 1 attacker models. Passes in the opposition half indicate possession taking place in more offensive pitch locations and could indicate the involvement of players in attacking moves. The ability to pass the ball accurately over a range of distances and directions is a key factor in performance and for differentiating between players of varying ability. This is accepted knowledge amongst coaches but the models have accurately identified specific key variables and provided an objective assessment of their impact on league status.

Shooting Variables

The next most prominent type of variable was related to players shooting ability. In total, twenty variables (14.9%) related to shooting were selected in the models. This agrees with previous research into team success in football, with total shots and shooting accuracy being the most commonly reported predictors in matches (Castellano et al., 2012; Lagos-Penas et al.,

2010; Liu et al., 2015). Surprisingly, all positions except attacker included shooting variables in the models created in the current study. However, one of the attacker models (group 0 v 2) did include total goals as a key variable. Many teams now prefer to play with one lone attacker in their line-up that spreads the need for scoring goals throughout the team and the requirements of the centre forward position could be changing as a result (Adams et al., 2013).

Attacking Entries

Other attacking variables selected as part of the models were related to crossing and entries into the final third and penalty area. Final third and penalty area entries were selected three times and in three different models. Crosses are a factor that have been repeatedly identified as being key to differentiating between successful and unsuccessful teams (Lagos-Penas et al.,

2010; Lagos-Penas et al., 2011). They have not been identified as key when differentiating between players of different performance levels previously, they were only selected twice in the current study meaning they did not play a prominent role in the position models. The mean number of crosses were selected in the group 0 v 2 attacker model (crosses mean group 0 1.0

± 0.8, group 2 1.75 ± 1.23). The inclusion of the number of crosses in the attacker model and the higher values reported for group 2 may offer more evidence for the evolving role of the attacker.

As well as crosses, final third and penalty area entries were selected three times and in three different models. Previous research has indicated that penalty area entries differentiate between winning and losing teams (Ruiz-Ruiz, Fradua, Fernandez-Garcia and Zubillaga, 2013).

However, in the current study they were selected in one model for centre backs (group 0 v 2), the centre backs from players dropping down to a lower playing level reported higher values

(minimum penalty area entries group 0 0.2 ± 0.4, group 2 0.0 ± 0.0). The identification of minimum penalty area entries in the centre back model and group 0 having a higher value is a novel finding. It may appear counter intuitive but centre backs who drop down to a lower level may play in teams who use a more direct style of play and play longer passes from their centre backs as opposed to building the play with shorter passing combinations.

Defensive Variables

The models also highlighted several defensive variables as key predictors of league status.

Statistics related to regaining possession accounted for eleven of the variables (8.2%). Previous research into match outcomes and players technical and tactical ability has heavily focused on the attacking aspects of play (Mackenzie and Cushion, 2013), passing (Adams et al., 2013; Szczepanski and McHale, 2016) and possession (Castellano et al., 2012; Collett, 2013; Lagos-

Penas et al., 2010; Liu et al., 2015). A limited number of defensive variables have been researched or identified that are linked to success. A balanced defensive shape (Tenga, Holme,

Ronglan and Bahr, 2010), defensive reaction after losing possession (Vogelbein, Nopp and

Hokelmann, 2014) and regaining possession in the final third have been identified previously

(Almeida, Ferreira and Volossovitch, 2014).

The current study highlighted possession won based on the minimum, median, maximum and upper quartile variables as being key predictors of league status. Possession gained upper quartile and interceptions median and maximum were also selected as key variables in models.

The defensive variables were not selected as part of any of the full back models. They were commonly selected as part of the wide midfield (group 0 v 1 and group 1 v 2) and attacker models (group 1 v 2 and group 0 v 2). This may appear counter intuitive and these factors would not normally be assessed when profiling more attacking positions within the team.

Modern playing philosophies valuing high pressing tactics from forward players to regain possession in more advanced areas of the pitch, this may explain the importance of these factors in wide midfield and attacker models within the current study (Perarnau, 2014).

International Recognition

Other key variables selected throughout several models relate to international appearances, international caps and U21 international caps were selected nine times (6.7%) in total. This is a novel finding as previous assessments of player’s performances have limited themselves to match performance and season totals of performance data. Previous research into international recognition and team or playing success has not been undertaken to the author’s knowledge. However, international recognition has been found to be linked with player salary allocation, particularly at the higher levels of the game (Frick, 2011).

Position-Specific Models

The current study created a number of strong predictive models for player’s league status, there were also some key findings relating to the prediction rates of specific positions. Three of the five positions had very similar levels of classification accuracy (centre midfield 86.1%, wide midfield 85.7% and attacker 84.7%) but the full back position’s overall accuracy was only

78.4% and the centre back position’s overall accuracy was 94.4%. The full back results are still an important finding but below the levels reported for other positions. The group 0 v 1 full back model had the lowest classification accuracy of all the models and the group 1 v 2 full back model had the second lowest classification accuracy. The full back position is one that requires a complex set of technical and tactical skills as it requires a wide array of attacking and defensive qualities (Bush, Archer, Hogg and Bradley, 2015).

Recent evaluations of the changes within performance data for playing positions has shown extensive changes over time in the Premier League (Bush et al., 2015). Pronounced increases were found for the levels of high-speed running and the distances covered while sprinting, with full backs showing the largest increases between 2006-07 and 2012-13 (Bush et al., 2015).

Therefore, the full back position may be influenced more by the physical aspects of performance. This could explain the lower prediction rates for full backs due to the lack of physical tracking data being available.

Study Limitations

Strong models were identified for fourteen out of the fifteen position comparisons assessed but there are some limitations to the present study that should be addressed in future research. The match running performance data for players was not available for the current study. There is an acceptance amongst the sports science community that running performance is not a predictor of team success or match outcome (Bradley et al., 2016; Carling, 2013). However, including match running performance data could provide a higher level of classification accuracy for some of the positions assessed. Another limitation of the study is the lack of contextual data available and the inability of the data to provide a detailed assessment for off the ball parameters. The final limitation of the study relates to the sample size for players progressing to play in the Premier League. The samples for the players progressing from the five positions to play in the Premier League were the smallest of all the groupings. Statistical power tests on similar sample sizes have reached the required levels (Lancashire et al., 2009).

However, future studies should look to increase the sample available to increase confidence that the results are repeatable to new cases.

Conclusions

The current study has shown that artificial neural networks are a valid and highly effective tool to classify and predict players league status. Fourteen models across all five positions were created that provided strong prediction accuracy levels for players league status. This is an important result as it outlines an objective methodology that can aid the scouting and recruitment process in professional football. The process of identifying and recruiting players in professional football has largely been a subjective process in the past. Further research should look to combine assessments of physical and technical performance data to provide a more accurate prediction of league status. Studies should also look to create models to predict the career progression of players from multiple leagues to provide a better practical tool for scouting and recruitment purposes. The combination of subjective assessments and more objective tools could lead to a more effective overall process in the highly competitive football transfer market.

References

Adams, D., Morgans, R., Sacramento, J., Morgan, S., and Williams, M. D., 2013. Successful short passing frequency of defenders differentiates between top and bottom four English Premier League teams. International Journal of Performance Analysis in Sport, 13, pp. 653- 668.

Almeida, C. H., Ferreira, A. P., and Volossovitch, A., 2014. Effects of match location, match status and quality of opposition on regaining possession in UEFA Champions League. Journal of Human Kinetics, 41 (2014), pp. 2013-214.

Andrzejewski, M., Konefal, M., Chmura, P., Kowalczuk, E., and Chmura, J., 2016. Match outcome and distances covered at various speeds in match play by elite German soccer players. International Journal of Performance Analysis in Sport, 16 (3), pp. 817-828.

Basheer, I.A., and Hajmeer, M., 2000. Artificial neural networks: fundamentals, computing, design and application. Journal of Microbiological Methods, 43 (2000), pp. 3-31.

Bradley, P. S., Archer, D., Hogg, B., Schuth, G., Bush, M., Carling, C., and Barnes, C., 2016. Tier-specific evolution of match performance characteristics in the English Premier League: It’s getting tougher at the top. Journal of Sports Sciences, 34 (10), pp. 980-987.

Bradley, P. S., Carling, C., Diaz, A. G., Hood, P., Barnes, C., Ade, J., Boddy, M., Krustrup, P., and Mohr, M., 2013. Match performance and physical capacity of players in the top three competitive standards of English professional soccer. Human Movement Science, 32 (2013), pp. 808-821.

Bradley, P. S., O’Donaghue, P., Wooster, B., and Tordoff, P., 2007. The reliability of ProZone MatchViewer: a video-based technical performance analysis system. International Journal of Performance Analysis in Sport, 7 (3), pp. 117-129.

Bush, M. D., Archer, D. T., Hogg, R., and Bradley, P. S., 2015. Factors influencing physical and technical variability in the English Premier League. International Journal of Sports Physiology and Performance, 10, pp. 865-872.

Carling, C., 2013. Interpreting physical performance in professional soccer match-play: Should we be more pragmatic in our approach. Sports Medicine, 43 (2013), pp. 655-663.

Carling, C., Williams, A. M., and Reilly, T. R., 2005. Handbook of soccer match analysis: A systematic approach to improving performance. London: Routledge.

Castellano, J., Casamichana, D., and Lago, C., 2012. The use of match statistics that discriminate between successful and unsuccessful soccer teams. Journal of Human Kinetics, 31 (2012), pp. 139-147.

Collett, C., 2013. The possession game? A comparative analysis of ball retention and team success in European and international football, 2007-2010. Journal of Sports Sciences, 31 (2), pp. 123-136.

Dellal, A., Chamari, K., Wong, D. P., Ahmaidi, S., Keller, D., Barros, R., Bisciotti, G. N., and Carling, C., 2011. Comparison of physical and technical performance in European soccer match-play: FA Premier League and La Liga. European Journal of Sport Science, 11 (1), pp. 51-59.

Dellal, A., Wong, D. P., Moalla, W., and Chamari, K., 2010. Physical and technical activity of soccer players in the French First League – with special reference to their playing position. International SportMed Journal, 11 (2), pp. 278-290.

Frick, B., 2011. Performance, salaries, and contract length: Empirical evidence from German soccer. International Journal of Sport Finance, 6, pp. 87-118.

Iyer, S., R., and Sharda, R., 2009. Prediction of athletes performance using neural networks: An application in cricket team selection. Expert systems with applications, 36 (2009), pp. 5510- 5522.

Lancashire, L. J., Lemetre, C., and Ball, G. R., 2009. An introduction to artificial neural networks in bioinformatics – application to complex microarray and mass spectrometry datasets in cancer studies. Briefings in Bioinformatics, 10 (3), pp. 315-329.

Lago-Penas, C., Lago-Ballesteros, J., Dellal, A., and Gomez, M., 2010. Game-related statistics that discriminated winning, drawing and losing teams from the Spanish soccer league. Journal of Sports Science and Medicine, 9, pp. 288-293.

Liu, H., Gomez, M. A., Lago-Penas, C., and Sampaio, J., 2015. Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup. Journal of Sports Sciences, 33 (12), pp. 1205-1213.

Mackenzie, R., and Cushion, C., 2013. Performance analysis in football: A critical review and implications for future research. Journal of Sports Sciences, 31 (6), pp. 639-676.

Perarnau, M., 2014. Pep confidential: The inside story of Pep Guardiola’s first season at Bayern Munich. Edinburgh: Arena Sport.

Pion, J., Hohmann, A., Liu, T., Lenoir, M., & Segers, V. (2017). Predictive models reduce talent development costs in female gymnastics. Journal of Sports Sciences, 35(8), 806-811.

Rampinini, E., Impellizzeri, F. M., Castagna, C., Coutts, A. J., and Wisloff, U., 2009. Technical performance during soccer matches of the Italian Serie A league: Effect of fatigue and competitive level. Journal of Science and Medicine in Sport, 12, pp. 227-233.

Ruiz-Ruiz, C., Fradua, L., Fernandez-Garcia, A., and Zubillaga, A., 2013. Analysis of entries into the penalty area as a performance indicator in soccer. European Journal of Sport Science, 13 (3), pp. 241-248.

Saikia, H., Bhattacharjee, D., and Lemmer, H., H., 2012. Predicting the performance of bowlers in IPL: An application of artificial neural network. International Journal of Performance Analysis in Sport, 12 (1), pp. 75-89.

Salkind, N., J., 2010. Encyclopedia of research design. California: Sage.

Taylor, J., B., Mellalieu, S., and James, N., 2004. Behavioural comparisons of positional demands in professional soccer. International Journal of Performance Analysis in Sport, 4 (1), pp. 81-97.

Tenga, A., Holme, I., Ronglan, L. T., and Bahr, R., 2010. Effects of playing tactics on goal scoring in Norwegian professional soccer. Journal of Sports Sciences, 28 (3), pp. 237-244.

Tu, J. V., 1996. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 49 (11), pp. 1225-1231.

Vogelbein, M., Nopp, S., and Hokelmann, A., 2014. Defensive transition in soccer – are prompt possession regains a measure of success? A quantitative analysis of German Fusball- Bundesliga 2010/11. Journal of Sports Sciences, 32 (11), pp. 1076-1083.

Formatted: Normal, Space After: 0 pt, Line spacing: 1.5 lines Table 1. Biographical data represented as means and standard deviations for player groupings.

Group Players (n) Age (years) Height (cm) 90 Minute Appearances Total Minutes

Group 0 Full Back 56 24.2 ± 4.3 180.5 ± 4.4 10.1 ± 10.7 1112 ± 1040

Group 1 Full Back 125 24.9 ± 4.2 180.2 ± 4.3 20.0 ± 12.1 2603 ± 1107

Group 2 Full Back 24 25.4 ± 3.3 179.7 ± 3.6 18.5 ± 12.5 1919 ± 1200

Group 0 Centre Back 37 27.5 ± 5.1 187.2 ± 5.1 15.9 ± 10.9 15901 ± 1023

Group 1 Centre Back 131 25.6 ± 3.7 186.7 ± 4.2 22.5 ± 12.4 2186 ± 1116

Group 2 Centre Back 25 25.6 ± 3.4 187.4 ± 3.7 22.8 ± 12.0 2173 ± 1141

Group 0 Wide Midfield 42 24.4 ± 4.3 179.1 ± 5.5 6.6 ± 7.0 1119 ± 858

Group 1 Wide Midfield 103 24.6 ± 3.7 177.2 ± 5.6 12.6 ± 9.6 1840 ± 1000

Group 2 Wide Midfield 23 24.8 ± 3.7 179.2 ± 4.8 19.4 ± 11.5 2425 ± 1109

Group 0 Centre Midfield 36 25.6 ± 4.8 179.7 ± 5.1 12.4 ± 11.9 1505 ± 1147

Group 1 Centre Midfield 148 25.6 ± 3.9 178.8 ± 5.8 19.5 ± 11.1 2238 ± 1006

Group 2 Centre Midfield 21 26.3 ± 4.5 178.5 ± 4.5 25.6 ± 13.6 2693 ± 1253

Group 0 Attacker 38 26.6 ± 4.8 182.2 ± 6.5 6.2 ± 6.9 1096 ± 920

Group 1 Attacker 130 26.0 ± 3.9 181.6 ± 5.9 11.8 ± 9.3 1845 ± 931

Group 2 Attacker 27 26.2 ± 4.5 181.7 ± 5.8 13.2 ± 9.3 2081 ± 930

Table 2. Results for all models with balanced data sets. The best average test performance = 100.0% and the best average test error = 1.0% (Using a combination of eighteen variables) – Centre Back Group 0 v 2. The worst average test performance = 72.7% and the worst average test error = 9.8% (Using a combination of five variables) – Full Back Group 0 v 1.

Position Groups Average Test Performance (%) Average Test Error (%) Number of Variables Full Back 0 v 1 72.7 9.8 5

Full Back 0 v 2 87.5 6.5 10

Full Back 1 v 2 75 9.3 6

Centre Back 0 v 1 93.3 4.1 20

Centre Back 0 v 2 100 1.0 18

Centre Back 1 v 2 90 5.5 6

Wide Midfield 0 v 1 76.5 8 10

Wide Midfield 0 v 2 100 3.4 6

Wide Midfield 1 v 2 77.8 7.4 9

Centre Midfield 0 v 1 78.6 9.6 9

Centre Midfield 0 v 2 90.9 4.8 10

Centre Midfield 1 v 2 88.9 5.9 5

Attacker 0 v 1 80 8.7 5

Attacker 0 v 2 92.3 2.6 10

Attacker 1 v 2 81.8 7.2 6

Average NA 85.7 6.3 9.0

Table 3. Summary of the variables in all position models by grouping.

Variable Grouping Times Selected Selected (%)

Passing 48 35.8 Shooting 20 14.9 Regains 11 8.2 International Appearances 9 6.7 Heading 8 6.0 Fouls 5 3.7 Goals 5 3.7 Appearances 4 3.0 Entries 3 2.2 Possession Lost 4 3.0 Tackled 3 2.2 Time in Possession 3 2.2 Assists 2 1.5 Blocks 2 1.5 Clearances 2 1.5 Crossing 2 1.5 Touches 2 1.5 Balls Received 1 0.7 Possessions 1 0.7

Table 4. Comparison of overall average test performance scores from position models as means and standard deviations.

Position Comparison Overall Average Test Performance (%) Overall Average Test Error (%)

Full Back 78.4 ± 8.0 8.6 ± 1.7

Centre Back 94.5 ± 5.1 3.5 ± 2.3

Wide Midfield 84.8 ± 13.2 6.3 ± 2.5

Centre Midfield 86.1 ± 6.6 6.8 ± 2.5

Attacker 84.7 ± 6.6 6.2 ± 3.2

Table 5. Results for Group 0 v Group 1 Full Back balanced data set comparison. The best average test performance = 72.7% and the best average test error = 9.8% (Using a combination of five variables).

Rank Variable Average Test Performance (%) Average Test Error (%)

1 Total Appearances 63.6 11.2

2 % Backwards Passes Successful (Mean) 72.7 10.6

3 Total Minutes 72.7 9.8

4 % Forwards Passes Successful (Mean) 72.7 9.8

5 Forwards Passes (Maximum) 72.7 9.8

6 Blocks (Mean) 70.5 9.9

7 % Unsuccessful Headers (Median) 68.2 10.0

8 Forward Passes Successful (Median) 68.2 10.0

9 % Passes Successful Own Half (Mean) 72.7 9.9

10 Passes Own Half 25% (Lower Quartile) 72.7 10.0

Table 6. Results for Group 1 v Group 2 Full Back balanced data set comparison. The best average test performance = 75.0% and the best average test error = 9.3% (Using a combination of six variables).

Rank Variable Average Test Average Test Error Performance (%) (%)

1 % Sideways Passes Successful 75% (Upper Quartile) 60.0 11.3

2 Total Shots (Median) 60.0 10.9

3 International Caps 70.0 9.7

4 Tackled (Mean) 70.0 9.3

5 First Time Passes (Maximum) 70.0 9.1

6 Number of Possessions (Median) 75.0 9.3

7 Tackled (Minimum) 70.0 9.4

8 % Sideways Passes Successful 25% (Lower Quartile) 70.0 9.4

9 Total Assists 70.0 9.8

10 % First Time Passes Unsuccessful 25% (Lower Quartile) 70.0 9.8

Table 7. Results for Group 0 v Group 2 Full Back balanced data set comparison. The best average test performance = 87.5% and the best average test error = 6.6% (Using a combination of ten variables).

Rank Variable Average Test Average Test Error Performance (%) (%)

1 Goals (Mean) 75.0 9.1

2 Headers (Minimum) 75.0 8.6

3 % Forward Passes Unsuccessful (Mean) 81.3 8.2

4 Shots Off Target (Exc. Blocked) (Maximum) 78.1 8.1

5 % Forward Passes Unsuccessful 75% (Upper Quartile) 75.0 8.2

6 U21 Caps 75.0 8.0

7 Shots Inside the Box (Mean) 81.3 7.7

8 Possession Lost (Mean) 81.3 7.0

9 Shots On Tgt Outside the Box (Maximum) 81.3 7.2

10 Total Assists 87.5 6.6

Table 8. Results for Group 0 v Group 1 Centre Back balanced data set comparison. The best average test performance = 93.3% and the best average test error = 4.1% (Using a combination of twenty variables).

Rank Variable Average Test Average Test Error Performance (%) (%)

1 % Passes Successful Opp Half 75% (Upper Quartile) 66.7 10.9

2 Shooting Accuracy 75% (Upper Quartile) 73.3 9.3

3 % Successful Headers 75% (Upper Quartile) 80.0 7.6

4 Balls Received 75% (Upper Quartile) 80.0 7.6

5 Crosses (Median) 80.0 7.9

6 % First Time Passes Successful 25% (Lower Quartile) 80.0 6.8

7 Total Shots on Target (Mean) 86.7 6.4

8 Passes Successful Opp Half (Minimum) 86.7 6.0

9 U21 Caps 86.7 6.1

10 Shooting Accuracy 25% (Lower Quartile) 86.7 5.2

11 Medium Passes (Mean) 86.7 5.2

12 Forward Passes Successful (Minimum) 93.3 4.5

13 Total Shots on Tgt (Excluding Blocked) (Mean) 86.7 5.0

14 Goals (Mean) 86.7 4.5

15 % Unsuccessful Headers 25% (Lower Quartile) 90.0 4.7

16 Long Passes (Median) 93.3 4.5

17 % Passes Successful Opp Half (Minimum) 93.3 4.2

18 Avg Time in Possession (Mean) 86.7 4.8

19 % Forwards Passes Successful (Minimum) 86.7 4.7

20 Shooting Accuracy (Median) 93.3 4.1

Table 9. Results for Group 1 v Group 2 Centre Back balanced data set comparison. The best average test performance = 90.0% and the best average test error = 5.5% (Using a combination of six variables).

Rank Variable Average Test Average Test Error Performance (%) (%)

1 Backwards Passes 25% (Lower Quartile) 70.0 10.7

2 Short Passes (Maximum) 70.0 9.4

3 Interceptions (Maximum) 80.0 8.1

4 Shots on Target Inside the Box (Mean) 80.0 6.8

5 Sideways Passes Unsuccessful (Mean) 80.0 6.6

6 Sideways Passes Successful 75% (Upper Quartile) 90.0 5.5

7 Passes Successful Own Half (Mean) 90.0 5.5

8 % Passes Successful Opp Half (Minimum) 80.0 6.3

9 % Sideways Passes Successful (Median) 90.0 6.4

10 Shots On Tgt Outside the Box (Mean) 85.0 6.6

Table 10. Results for Group 0 v Group 2 Centre Back balanced data set comparison. The best average test performance = 100% and the best average test error = 1.0% (Using a combination of eighteen variables).

Rank Variable Average Test Performance (%) Average Test Error (%)

1 0-6 Assists (Mean) 80.0 8.1

2 Shots on Target Inside the Box (Mean) 80.0 5.8

3 Penalty Area Entries (Minimum) 90.0 4.4

4 International Caps 90.0 3.7

5 Long Passes 25% (Lower Quartile) 90.0 3.2

6 Shots Outside the Box (Mean) 90.0 2.9

7 U21 Caps 100.0 2.4

8 Possession Gained 75% (Upper Quartile) 100.0 1.5

9 Avg Time in Possession (Median) 100.0 1.5

10 Clearances (Maximum) 100.0 1.2

11 Shots Outside the Box (Median) 100.0 1.1

12 First Time Passes (Mean) 100.0 1.3

13 Unsuccessful Passes (Minimum) 100.0 1.4

14 Interceptions 75% (Upper Quartile) 100.0 1.3

15 Possession Gained (Minimum) 100.0 1.3

16 Shots Inside the Box 25% (Lower Quartile) 100.0 1.1

17 Total Shots on Target (Mean) 100.0 1.2

18 Tackled (Minimum) 100.0 1.0

19 Final Third Entries (Mean) 100.0 1.0

20 Medium Passes 25% (Lower Quartile) 100.0 1.3

Table 11. Results for Group 0 v Group 1 Wide Midfield balanced data set comparison. The best average test performance = 79.4% and the best average test error = 8.2% (Using a combination of nine variables).

Rank Variable Average Test Performance Average Test Error (%) (%)

1 % Unsuccessful Headers (Maximum) 70.6 10.8

2 Forward Passes Successful 75% (Upper Quartile) 73.5 10.0

3 Possession Won 75% (Upper Quartile) 70.6 9.8

4 Shooting Accuracy 25% (Lower Quartile) 76.5 8.9

5 % Unsuccessful Headers 75% (Upper Quartile) 79.4 8.5

6 % Successful Headers (Median) 76.5 8.4

7 Sideways Passes Successful 75% (Upper Quartile) 76.5 8.2

8 Fouls (Mean) 76.5 8.1

9 Tackled (Maximum) 79.4 8.2

10 8.0 Passes Attempted Opp Half (Mean) 76.5

Table 12. Results for Group 1 v Group 2 Wide Midfield balanced data set comparison. The best average test performance = 77.8% and the best average test error = 7.4% (Using a combination of nine variables).

Rank Variable Average Test Performance Average Test Error (%) (%)

1 U21 International Caps 66.7 10.3

2 Forwards Passes Unsuccessful (Median) 77.8 9.3

3 % Sideways Passes Unsuccessful (Median) 77.8 9.1

4 Fouls (Mean) 77.8 8.9

5 Possession Won (Maximum) 77.8 8.6

6 % Unsuccessful Headers (Maximum) 77.8 8.5

7 Backwards Passes Unsuccessful (Maximum) 77.8 8.7

8 Possession Lost (Maximum) 77.8 7.9

9 Possession Won (Minimum) 77.8 7.4

10 % Unsuccessful Headers 25% (Lower Quartile) 77.8 7.6

Table 13. Results for Group 0 v Group 2 Wide Midfield balanced data set comparison. The best average test performance = 100% and the best average test error = 3.4% (Using a combination of six variables).

Rank Variable Average Test Performance (%) Average Test Error (%)

1 Total Goals 84.6 7.2

2 Passes Attempted Opp Half 75% (Upper Quartile) 84.6 6.3

3 Fouls in Defensive 3rd (Mean) 84.6 6.1

4 Total Shots on Tgt (Excluding Blocked) (Maximum) 92.3 4.5

5 % Forwards Passes Successful (Mean) 92.3 3.3

6 Forward Passes Successful (Median) 100.0 3.4

7 Tackled 75% (Upper Quartile) 92.3 3.7

8 % Unsuccessful Passes 75% (Upper Quartile) 92.3 3.6

9 Backwards Passes Unsuccessful (Mean) 92.3 3.5

10 Possession Lost (Median) 92.3 3.1

Table 14. Results for Group 0 v Group 1 Centre Midfield balanced data set comparison. The best average test performance = 78.6% and the best average test error = 9.6% (Using a combination of nine variables).

Rank Variable Average Test Performance Average Test Error (%) (%)

1 Fouls 57.1 11.5

2 First Time Passes (Maximum) 64.3 10.9

3 Backwards Passes 75% (Upper Quartile) 64.3 10.6

4 Number of Touches (Median) 64.3 10.6

5 Fouls (Maximum) 64.3 10.5

6 Total Minutes 71.4 9.9

7 % Forward Passes Unsuccessful 25% (Lower 71.4 9.6 Quartile)

8 Sideways Passes (Median) 71.4 9.6

9 Passes Attempted Opp Half (Minimum) 78.6 9.6

10 Height 71.4 9.7

Table 15. Results for Group 1 v Group 2 Centre Midfield balanced data set comparison. The best average test performance = 88.9% and the best average test error = 5.9% (Using a combination of five variables).

Rank Variable Average Test Performance Average Test Error (%) (%)

1 Successful Passes 25% (Lower Quartile) 66.7 10.2

2 Penalty Area Entries 25% (Lower Quartile) 66.7 9.6

3 Goals (Mean) 77.8 8.4

4 Backwards Passes Unsuccessful (Mean) 88.9 6.2

5 First Time Passes Successful (Maximum) 88.9 5.9

6 Backwards Passes (Median) 88.9 6.2

7 % Sideways Passes Successful 25% (Lower Quartile) 88.9 6.4

8 Total Shots 25% (Lower Quartile) 88.9 6.4

9 Passes Own Half (Mean) 88.9 6.9

10 Dribbles 75% (Upper Quartile) 83.3 7.2

Table 16. Results for Group 0 v Group 2 Centre Midfield balanced data set comparison. The best average test performance = 90.9% and the best average test error = 4.8% (Using a combination of ten variables).

Rank Variable Average Test Performance Average Test Error (%) (%)

1 No. Of Starts 72.7 9.6

2 Shots On Tgt Outside the Box (Maximum) 81.8 8.6

3 Possession Lost (Maximum) 77.3 8.0

4 Forwards Passes (Mean) 81.8 7.2

5 Possession Won (Median) 81.8 6.0

6 Clearances 25% (Lower Quartile) 81.8 5.5

7 Total Shots on Target (Mean) 90.9 5.2

8 Total Blocked Shots (Maximum) 90.9 5.2

9 Forwards Passes (Median) 90.9 4.9

10 % Passes Successful Opp Half 75% (Upper Quartile) 90.9 4.8 Table 17. Results for Group 0 v Group 1 Attacker balanced data set comparison. The best average test performance = 80.0% and the best average test error = 8.7% (Using a combination of five variables).

Rank Variable Average Test Performance (%) Average Test Error (%)

1 International Caps 73.3 10.4

2 Number of Touches 25% (Lower Quartile) 73.3 9.2

3 First Time Passes (Maximum) 73.3 9.1

4 Blocks (Maximum) 73.3 8.9

5 Final Third Entries (Mean) 80.0 8.7

6 Passes Successful Own Half (Median) 73.3 8.9

7 % Successful Passes (Maximum) 73.3 9.2

8 Tackled 25% (Lower Quartile) 73.3 9.0

9 % Forwards Passes Successful (Minimum) 73.3 9.1

10 % Passes Successful Opp Half (Minimum) 73.3 9.1

Table 18. Results for Group 1 v Group 2 Attacker balanced data set comparison. The best average test performance = 81.8% and the best average test error = 7.2% (Using a combination of six variables).

Rank Variable Average Test Performance (%) Average Test Error (%)

1 U21 International Caps 63.6 11.0

2 International Caps 72.7 9.9

3 Unsuccessful Passes (Maximum) 72.7 9.6

4 Interceptions (Maximum) 72.7 8.7

5 Possession Won (Median) 81.8 7.2

6 % Unsuccessful Passes 75% (Upper Quartile) 81.8 7.2

7 Final Third Entries 25% (Lower Quartile) 81.8 7.8

8 Tackles (Maximum) 81.8 7.4

9 % Unsuccessful Passes (Minimum) 81.8 7.5

10 Penalty Area Entries (Minimum) 81.8 7.3

Table 19. Results for Group 0 v Group 2 Attacker balanced data set comparison. The best average test performance = 92.3% and the best average test error = 2.6% (Using a combination of ten variables).

Rank Variable Average Test Performance (%) Average Test Error (%)

1 Total Goals 76.9 7.6

2 Blocks 75% (Upper Quartile) 84.6 5.6

3 Short Passes (Minimum) 92.3 5.0

4 Passes Own Half 25% (Lower Quartile) 92.3 4.4

5 % Unsuccessful Headers (Maximum) 92.3 4.0

6 Crosses (Mean) 92.3 3.0

7 Avg Time in Possession 75% (Upper Quartile) 92.3 2.9

8 Interceptions (Median) 92.3 3.0

9 Passes Successful Opp Half 75% (Upper Quartile) 92.3 3.0

10 Backwards Passes 25% (Lower Quartile) 92.3 2.6

Table 20. Comparison of overall average test performance scores from position models as means and standard deviations.

Group Comparison Overall Average Test Overall Average Test Error (%) Performance (%)

Group 0 v 1 Comparisons 80.8 ± 7.6 8.1 ± 2.3

Group 1 v 2 Comparisons 82.7 ± 6.6 7.1 ± 1.5

Group 0 v 2 Comparisons 94.1 ± 5.6 3.7 ± 2.1

Table 21. Group 0 v 2 Centre Back model variables represented as means and standard deviations for all player groupings.

Variables Group 0 Centre Back Group 2 Centre Back

0-6 Assists (Mean) 0.1 ± 0.1 0.2 ± 0.1

Shots on Target Inside the Box (Mean) 0.2 ± 0.2 0.3 ± 0.2

Penalty Area Entries (Minimum) 0.2 ± 0.4 0.0 ± 0.0

International Caps 4.8 ± 18.3 9.2 ± 14.6

Long Passes 25% (Lower Quartile) 4.3 ± 2.2 4.9 ± 2.0

Shots Outside the Box (Mean) 0.1 ± 0.2 0.1 ± 0.1

U21 Caps 0.3 ± 0.9 3.5 ± 6.6

Possession Gained 75% (Upper Quartile) 34.2 ± 5.5 36.7 ± 5.7

Avg Time in Possession (Median) 2.4 ± 2.2 2.6 ± 0.3

Clearances (Maximum) 10.9 ± 3.2 11.4 ± 3.2

Shots Outside the Box (Median) 0.0 ± 0.2 0.0 ± 0.0

First Time Passes (Mean) 6.5 ± 1.9 7.0 ± 1.2

Unsuccessful Passes (Minimum) 1.4 ± 1.8 1.0 ± 1.2

Interceptions 75% (Upper Quartile) 29.9 ± 4.2 31.1 ± 5.3

Possession Gained (Minimum) 21.1 ± 4.9 18.5 ± 6.3

Shots Inside the Box 25% (Lower Quartile) 0.1 ± 0.4 0.0 ± 0.1

Total Shots on Target (Mean) 0.2 ± 0.2 0.3 ± 0.2

Tackled (Minimum) 0.2 ± 0.7 0.0 ± 0.2

Table 22. Group 0 v 2 Wide Midfield model variables represented as means and standard deviations for all player groupings.

Variables Group 0 Wide Midfield Group 2 Wide Midfield

Total Goals 1.4 ± 1.9 5.5 ± 3.8

Passes Attempted Opp Half 75% (Upper Quartile) 16.2 ± 6.3 21.4 ± 5.8

Fouls in Defensive 3rd (Mean) 0.2 ± 0.2 0.3 ± 0.3

Total Shots on Tgt (Excluding Blocked) 1.0 ± 0.8 2.6 ± 1.1 (Maximum)

% Forwards Passes Successful (Mean) 53.4 ± 14.8 55.2 ± 9.7

Forward Passes Successful (Median) 5.0 ± 3.2 6.1 ± 2.2

Total Goals 1.4 ± 1.9 5.5 ± 3.8

Passes Attempted Opp Half 75% (Upper Quartile) 16.2 ± 6.3 21.4 ± 5.8

Fouls in Defensive 3rd (Mean) 0.2 ± 0.2 0.3 ± 0.3

Total Shots on Tgt (Excluding Blocked) 1.0 ± 0.8 2.6 ± 1.1 (Maximum)

Table 23. Group 0 v 1 Centre Back model variables represented as means and standard deviations for all player groupings.

Variables Group 0 Centre Back Group 1 Centre Back

% Passes Successful Opp Half 75% (Upper 81.2 ± 22.3 92.4 ± 13.5 Quartile)

Shooting Accuracy 75% (Upper Quartile) 23.5 ± 35.6 20.1 ± 33.8

% Successful Headers 75% (Upper Quartile) 51.0 ± 8.7 52.7 ± 6.6

Balls Received 75% (Upper Quartile) 16.9 ± 5.8 20.6 ± 8.9

Crosses (Median) 0.1 ± 0.3 0.1 ± 0.3

% First Time Passes Successful 25% (Lower 59.3 ± 13.0 59.9 ± 12.7 Quartile)

Total Shots on Target (Mean) 0.2 ± 0.2 0.3 ± 0.3

Passes Successful Opp Half (Minimum) 0.2 ± 0.5 0.3 ± 1.0

U21 Caps 0.3 ± 0.9 1.3 ± 3.2

Shooting Accuracy 25% (Lower Quartile) 0.0 ± 0.0 1.9 ± 11.4

Medium Passes (Mean) 7.9 ± 2.9 9.6 ± 5.1

Forward Passes Successful (Minimum) 1.5 ± 1.3 1.6 ± 2.5

Total Shots on Tgt (Excluding Blocked) (Mean) 0.1 ± 0.1 0.2 ± 0.2

Goals (Mean) 0.0 ± 0.1 0.1 ± 0.1

% Unsuccessful Headers 25% (Lower Quartile) 49.0 ± 8.7 47.2 ± 6.7

Long Passes (Median) 5.5 ± 2.1 6.3 ± 2.5

Supplementary Information

The learning rate (the rate at which weights are updated as a proportion of the error) was set at

0.1 while the momentum (the proportion of the previous change in weights applied back to the current change in weights) was 0.5 and two hidden nodes (feature detectors) were used as part of the artificial neural network architecture in a single hidden layer. The maximum number of epochs (updates of the network) used was three hundred while the maximum number of epochs without improvement on the test was one hundred. This was used to prevent over fitting of the model.

List of Initial 340 Variables Included in the Study

Number Variable 1 % Backwards Passes Successful Lower Quartile 2 % Backwards Passes Successful Mean 3 % Backwards Passes Successful Min 4 % Backwards Passes Unsuccessful Max 5 % Backwards Passes Unsuccessful Mean 6 % First Time Passes Successful Lower Quartile 7 % First Time Passes Successful Mean 8 % First Time Passes Successful Median 9 % First Time Passes Successful Min 10 % First Time Passes Successful Upper Quartile 11 % First Time Passes Unsuccessful Lower Quartile 12 % First Time Passes Unsuccessful Max 13 % First Time Passes Unsuccessful Mean 14 % First Time Passes Unsuccessful Median 15 % First Time Passes Unsuccessful Upper Quartile 16 % Forward Passes Unsuccessful Lower Quartile 17 % Forward Passes Unsuccessful Max 18 % Forward Passes Unsuccessful Mean 19 % Forward Passes Unsuccessful Median 20 % Forward Passes Unsuccessful Min 21 % Forward Passes Unsuccessful Upper Quartile 22 % Forwards Passes Successful Lower Quartile 23 % Forwards Passes Successful Max 24 % Forwards Passes Successful Mean 25 % Forwards Passes Successful Median 26 % Forwards Passes Successful Min 27 % Forwards Passes Successful Upper Quartile 28 % Passes Successful Opp Half Lower Quartile 29 % Passes Successful Opp Half Mean 30 % Passes Successful Opp Half Median 31 % Passes Successful Opp Half Min 32 % Passes Successful Opp Half Upper Quartile 33 % Passes Successful Own Half Lower Quartile 34 % Passes Successful Own Half Max 35 % Passes Successful Own Half Mean 36 % Passes Successful Own Half Median 37 % Passes Successful Own Half Min 38 % Passes Successful Own Half Upper Quartile 39 % Sideways Passes Successful Lower Quartile 40 % Sideways Passes Successful Mean 41 % Sideways Passes Successful Median 42 % Sideways Passes Successful Min 43 % Sideways Passes Successful Upper Quartile 44 % Sideways Passes Unsuccessful Lower Quartile 45 % Sideways Passes Unsuccessful Max 46 % Sideways Passes Unsuccessful Mean 47 % Sideways Passes Unsuccessful Median 48 % Sideways Passes Unsuccessful Upper Quartile 49 % Successful Headers Lower Quartile 50 % Successful Headers Max 51 % Successful Headers Mean 52 % Successful Headers Median 53 % Successful Headers Min 54 % Successful Headers Upper Quartile 55 % Successful Passes Lower Quartile 56 % Successful Passes Max 57 % Successful Passes Mean 58 % Successful Passes Median 59 % Successful Passes Min 60 % Successful Passes Upper Quartile 61 % Unsuccessful Headers Lower Quartile 62 % Unsuccessful Headers Max 63 % Unsuccessful Headers Mean 64 % Unsuccessful Headers Median 65 % Unsuccessful Headers Min 66 % Unsuccessful Headers Upper Quartile 67 % Unsuccessful Passes Lower Quartile 68 % Unsuccessful Passes Max 69 % Unsuccessful Passes Mean 70 % Unsuccessful Passes Median 71 % Unsuccessful Passes Min 72 % Unsuccessful Passes Upper Quartile 73 0-6 Assists Mean 74 Age 75 Avg Time in Possession Lower Quartile 76 Avg Time in Possession Max 77 Avg Time in Possession Mean 78 Avg Time in Possession Median 79 Avg Time in Possession Min 80 Avg Time in Possession Upper Quartile 81 Avg Touches Max 82 Backwards Passes Lower Quartile 83 Backwards Passes Max 84 Backwards Passes Mean 85 Backwards Passes Median 86 Backwards Passes Min 87 Backwards Passes Successful Lower Quartile 88 Backwards Passes Successful Max 89 Backwards Passes Successful Mean 90 Backwards Passes Successful Median 91 Backwards Passes Successful Min 92 Backwards Passes Successful Upper Quartile 93 Backwards Passes Unsuccessful Max 94 Backwards Passes Unsuccessful Mean 95 Backwards Passes Upper Quartile 96 Balls Received Lower Quartile 97 Balls Received Max 98 Balls Received Mean 99 Balls Received Median 100 Balls Received Min 101 Balls Received Upper Quartile 102 Blocks Max 103 Blocks Mean 104 Blocks Median 105 Blocks Upper Quartile 106 Clearances Lower Quartile 107 Clearances Max 108 Clearances Mean 109 Clearances Median 110 Clearances Upper Quartile 111 Corners Conceded Max 112 Corners Conceded Mean 113 Crosses Lower Quartile 114 Crosses Max 115 Crosses Mean 116 Crosses Median 117 Crosses Upper Quartile 118 Dribbles Max 119 Dribbles Mean 120 Dribbles Upper Quartile 121 Final Third Entries Lower Quartile 122 Final Third Entries Max 123 Final Third Entries Mean 124 Final Third Entries Median 125 Final Third Entries Min 126 Final Third Entries Upper Quartile 127 First Time Passes Lower Quartile 128 First Time Passes Max 129 First Time Passes Mean 130 First Time Passes Median 131 First Time Passes Min 132 First Time Passes Successful Lower Quartile 133 First Time Passes Successful Max 134 First Time Passes Successful Mean 135 First Time Passes Successful Median 136 First Time Passes Successful Min 137 First Time Passes Successful Upper Quartile 138 First Time Passes Unsuccessful Max 139 First Time Passes Unsuccessful Mean 140 First Time Passes Unsuccessful Upper Quartile 141 First Time Passes Upper Quartile 142 Forward Passes Successful Lower Quartile 143 Forward Passes Successful Max 144 Forward Passes Successful Mean 145 Forward Passes Successful Median 146 Forward Passes Successful Min 147 Forward Passes Successful Upper Quartile 148 Forwards Passes Lower Quartile 149 Forwards Passes Max 150 Forwards Passes Mean 151 Forwards Passes Median 152 Forwards Passes Min 153 Forwards Passes Unsuccessful Lower Quartile 154 Forwards Passes Unsuccessful Max 155 Forwards Passes Unsuccessful Mean 156 Forwards Passes Unsuccessful Median 157 Forwards Passes Unsuccessful Min 158 Forwards Passes Unsuccessful Upper Quartile 159 Forwards Passes Upper Quartile 160 Fouled Max 161 Fouled Mean 162 Fouled Upper Quartile 163 Fouls 164 Fouls in Defensive 3rd Mean 165 Fouls Max 166 Fouls Mean 167 Goals Mean 168 Headers Lower Quartile 169 Headers Max 170 Headers Mean 171 Headers Median 172 Headers Min 173 Headers Upper Quartile 174 Height 175 Interceptions Lower Quartile 176 Interceptions Max 177 Interceptions Mean 178 Interceptions Median 179 Interceptions Min 180 Interceptions Upper Quartile 181 International Caps 182 Long Passes Lower Quartile 183 Long Passes Max 184 Long Passes Mean 185 Long Passes Median 186 Long Passes Min 187 Long Passes Upper Quartile 188 Medium Passes Lower Quartile 189 Medium Passes Max 190 Medium Passes Mean 191 Medium Passes Median 192 Medium Passes Min 193 Medium Passes Upper Quartile 194 No. of 90 Mins App. 195 No. Of Starts 196 Number of Possessions Lower Quartile 197 Number of Possessions Max 198 Number of Possessions Mean 199 Number of Possessions Median 200 Number of Possessions Min 201 Number of Possessions Upper Quartile 202 Number of Touches Lower Quartile 203 Number of Touches Max 204 Number of Touches Mean 205 Number of Touches Median 206 Number of Touches Min 207 Number of Touches Upper Quartile 208 Offsides Mean 209 Passes Attempted Opp Half Lower Quartile 210 Passes Attempted Opp Half Max 211 Passes Attempted Opp Half Mean 212 Passes Attempted Opp Half Median 213 Passes Attempted Opp Half Min 214 Passes Attempted Opp Half Upper Quartile 215 Passes Lower Quartile 216 Passes Max 217 Passes Mean 218 Passes Median 219 Passes Min 220 Passes Own Half Lower Quartile 221 Passes Own Half Max 222 Passes Own Half Mean 223 Passes Own Half Median 224 Passes Own Half Min 225 Passes Own Half Upper Quartile 226 Passes Successful Opp Half Lower Quartile 227 Passes Successful Opp Half Max 228 Passes Successful Opp Half Mean 229 Passes Successful Opp Half Median 230 Passes Successful Opp Half Min 231 Passes Successful Opp Half Upper Quartile 232 Passes Successful Own Half Lower Quartile 233 Passes Successful Own Half Max 234 Passes Successful Own Half Mean 235 Passes Successful Own Half Median 236 Passes Successful Own Half Min 237 Passes Successful Own Half Upper Quartile 238 Passes Upper Quartile 239 Penalty Area Entries Lower Quartile 240 Penalty Area Entries Max 241 Penalty Area Entries Mean 242 Penalty Area Entries Median 243 Penalty Area Entries Min 244 Penalty Area Entries Upper Quartile 245 Playing % 246 Possession Gained Lower Quartile 247 Possession Gained Max 248 Possession Gained Mean 249 Possession Gained Median 250 Possession Gained Min 251 Possession Gained Upper Quartile 252 Possession Lost Lower Quartile 253 Possession Lost Max 254 Possession Lost Mean 255 Possession Lost Median 256 Possession Lost Min 257 Possession Lost Upper Quartile 258 Possession Won Lower Quartile 259 Possession Won Max 260 Possession Won Mean 261 Possession Won Median 262 Possession Won Min 263 Possession Won Upper Quartile 264 Shooting Accuracy Lower Quartile 265 Shooting Accuracy Mean 266 Shooting Accuracy Median 267 Shooting Accuracy Upper Quartile 268 Short Passes Lower Quartile 269 Short Passes Max 270 Short Passes Mean 271 Short Passes Median 272 Short Passes Min 273 Short Passes Upper Quartile 274 Shots Inside the Box Lower Quartile 275 Shots Inside the Box Max 276 Shots Inside the Box Mean 277 Shots Inside the Box Upper Quartile 278 Shots Off Target (Exc. Blocked) Max 279 Shots Off Target (Exc. Blocked) Mean 280 Shots on Target Inside the Box Max 281 Shots on Target Inside the Box Mean 282 Shots On Tgt Outside the Box Max 283 Shots On Tgt Outside the Box Mean 284 Shots Outside the Box Max 285 Shots Outside the Box Mean 286 Shots Outside the Box Median 287 Sideways Passes Lower Quartile 288 Sideways Passes Max 289 Sideways Passes Mean 290 Sideways Passes Median 291 Sideways Passes Min 292 Sideways Passes Successful Lower Quartile 293 Sideways Passes Successful Max 294 Sideways Passes Successful Mean 295 Sideways Passes Successful Median 296 Sideways Passes Successful Min 297 Sideways Passes Successful Upper Quartile 298 Sideways Passes Unsuccessful Max 299 Sideways Passes Unsuccessful Mean 300 Sideways Passes Upper Quartile 301 Successful Passes Lower Quartile 302 Successful Passes Max 303 Successful Passes Mean 304 Successful Passes Median 305 Successful Passes Min 306 Successful Passes Upper Quartile 307 Tackled Lower Quartile 308 Tackled Max 309 Tackled Mean 310 Tackled Median 311 Tackled Min 312 Tackled Upper Quartile 313 Tackles Lower Quartile 314 Tackles Max 315 Tackles Mean 316 Tackles Median 317 Tackles Upper Quartile 318 Total Appearances 319 Total Assists 320 Total Blocked Shots Max 321 Total Blocked Shots Mean 322 Total Goals 323 Total Minutes 324 Total Shots Lower Quartile 325 Total Shots Max 326 Total Shots Mean 327 Total Shots Median 328 Total Shots on Target Max 329 Total Shots on Target Mean 330 Total Shots on Tgt (Excluding Blocked) Max 331 Total Shots on Tgt (Excluding Blocked) Mean 332 Total Shots Upper Quartile 333 U21 Caps 334 Unsuccessful Passes Lower Quartile 335 Unsuccessful Passes Max 336 Unsuccessful Passes Mean 337 Unsuccessful Passes Median 338 Unsuccessful Passes Min 339 Unsuccessful Passes Upper Quartile 340 Yellow Cards

List of 196 Variables Excluded from the Study

Number Variable 1 % Backwards Passes Successful Max 2 % Backwards Passes Successful Median 3 % Backwards Passes Successful Upper Quartile % Backwards Passes Unsuccessful Lower 4 Quartile 5 % Backwards Passes Unsuccessful Median 6 % Backwards Passes Unsuccessful Min % Backwards Passes Unsuccessful Upper 7 Quartile 8 % First Time Passes Successful Max 9 % First Time Passes Unsuccessful Min 10 % Passes Successful Opp Half Max 11 % Sideways Passes Successful Max 12 % Sideways Passes Unsuccessful Min 13 0-6 Assists Lower Quartile 14 0-6 Assists Max 15 0-6 Assists Median 16 0-6 Assists Min 17 0-6 Assists Upper Quartile 18 1st Assist Lower Quartile 19 1st Assist Max 20 1st Assist Mean 21 1st Assist Median 22 1st Assist Min 23 1st Assist Upper Quartile 24 2nd Assist Lower Quartile 25 2nd Assist Max 26 2nd Assist Mean 27 2nd Assist Median 28 2nd Assist Min 29 2nd Assist Upper Quartile 30 3rd Assist Lower Quartile 31 3rd Assist Max 32 3rd Assist Mean 33 3rd Assist Median 34 3rd Assist Min 35 3rd Assist Upper Quartile 36 4th Assist Lower Quartile 37 4th Assist Max 38 4th Assist Mean 39 4th Assist Median 40 4th Assist Min 41 4th Assist Upper Quartile 42 5th Assist Lower Quartile 43 5th Assist Max 44 5th Assist Mean 45 5th Assist Median 46 5th Assist Min 47 5th Assist Upper Quartile 48 6th Assist Lower Quartile 49 6th Assist Max 50 6th Assist Mean 51 6th Assist Median 52 6th Assist Min 53 6th Assist Upper Quartile 54 Avg Touches Lower Quartile 55 Avg Touches Mean 56 Avg Touches Median 57 Avg Touches Min 58 Avg Touches Upper Quartile 59 Backwards Passes Unsuccessful Lower Quartile 60 Backwards Passes Unsuccessful Median 61 Backwards Passes Unsuccessful Min 62 Backwards Passes Unsuccessful Upper Quartile 63 Blocks Lower Quartile 64 Blocks Min 65 Clearances Min 66 Corners Conceded Lower Quartile 67 Corners Conceded Median 68 Corners Conceded Min 69 Corners Conceded Upper Quartile 70 Corners from LEFT Lower Quartile 71 Corners from LEFT Max 72 Corners from LEFT Mean 73 Corners from LEFT Median 74 Corners from LEFT Min 75 Corners from LEFT Upper Quartile 76 Corners from RIGHT Lower Quartile 77 Corners from RIGHT Max 78 Corners from RIGHT Mean 79 Corners from RIGHT Median 80 Corners from RIGHT Min 81 Corners from RIGHT Upper Quartile 82 Corners Taken Lower Quartile 83 Corners Taken Max 84 Corners Taken Mean 85 Corners Taken Median 86 Corners Taken Min 87 Corners Taken Upper Quartile 88 Crosses from LEFT Lower Quartile 89 Crosses from LEFT Max 90 Crosses from LEFT Mean 91 Crosses from LEFT Median 92 Crosses from LEFT Min 93 Crosses from LEFT Upper Quartile 94 Crosses from RIGHT Lower Quartile 95 Crosses from RIGHT Max 96 Crosses from RIGHT Mean 97 Crosses from RIGHT Median 98 Crosses from RIGHT Min 99 Crosses from RIGHT Upper Quartile 100 Crosses Min 101 Dribbles Lower Quartile 102 Dribbles Median 103 Dribbles Min 104 First Time Passes Unsuccessful Lower Quartile 105 First Time Passes Unsuccessful Median 106 First Time Passes Unsuccessful Min 107 Fouled Lower Quartile 108 Fouled Median 109 Fouled Min 110 Fouls in Defensive 3rd Lower Quartile 111 Fouls in Defensive 3rd Max 112 Fouls in Defensive 3rd Median 113 Fouls in Defensive 3rd Min 114 Fouls in Defensive 3rd Upper Quartile 115 Fouls Lower Quartile 116 Fouls Median 117 Fouls Min 118 Fouls Upper Quartile 119 Free Kicks Taken Lower Quartile 120 Free Kicks Taken Max 121 Free Kicks Taken Mean 122 Free Kicks Taken Median 123 Free Kicks Taken Min 124 Free Kicks Taken Upper Quartile 125 Goals Lower Quartile 126 Goals Max 127 Goals Median 128 Goals Min 129 Goals Upper Quartile 130 Offsides Lower Quartile 131 Offsides Max 132 Offsides Median 133 Offsides Min 134 Offsides Upper Quartile 135 Own Goals Lower Quartile 136 Own Goals Max 137 Own Goals Mean 138 Own Goals Median 139 Own Goals Min 140 Own Goals Upper Quartile 141 Playing Time Lower Quartile 142 Playing Time Max 143 Playing Time Mean 144 Playing Time Median 145 Playing Time Min 146 Playing Time Upper Quartile 147 Red Cards 148 Red Cards Lower Quartile 149 Red Cards Max 150 Red Cards Mean 151 Red Cards Median 152 Red Cards Min 153 Red Cards Upper Quartile 154 Shooting Accuracy Max 155 Shooting Accuracy Min 156 Shots Inside the Box Median 157 Shots Inside the Box Min 158 Shots Off Target (Exc. Blocked) Lower Quartile 159 Shots Off Target (Exc. Blocked) Median 160 Shots Off Target (Exc. Blocked) Min 161 Shots Off Target (Exc. Blocked) Upper Quartile 162 Shots on Target Inside the Box Lower Quartile 163 Shots on Target Inside the Box Median 164 Shots on Target Inside the Box Min 165 Shots on Target Inside the Box Upper Quartile 166 Shots On Tgt Outside the Box Lower Quartile 167 Shots On Tgt Outside the Box Median 168 Shots On Tgt Outside the Box Min 169 Shots On Tgt Outside the Box Upper Quartile 170 Shots Outside the Box Lower Quartile 171 Shots Outside the Box Min 172 Shots Outside the Box Upper Quartile 173 Sideways Passes Unsuccessful Lower Quartile 174 Sideways Passes Unsuccessful Median 175 Sideways Passes Unsuccessful Min 176 Sideways Passes Unsuccessful Upper Quartile 177 Tackles Min 178 Total Blocked Shots Lower Quartile 179 Total Blocked Shots Median 180 Total Blocked Shots Min 181 Total Blocked Shots Upper Quartile 182 Total Shots Min 183 Total Shots on Target Lower Quartile 184 Total Shots on Target Median 185 Total Shots on Target Min 186 Total Shots on Target Upper Quartile Total Shots on Tgt (Excluding Blocked) Lower 187 Quartile 188 Total Shots on Tgt (Excluding Blocked) Median 189 Total Shots on Tgt (Excluding Blocked) Min Total Shots on Tgt (Excluding Blocked) Upper 190 Quartile 191 Yellow Cards Lower Quartile 192 Yellow Cards Max 193 Yellow Cards Mean 194 Yellow Cards Median 195 Yellow Cards Min 196 Yellow Cards Upper Quartile