Journal of Sports Sciences
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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. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation Manuscript - with author details 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.