Identifying playing talent in professional football using artificial neural networks

Barron, Donald, Ball, Graham, Robins, Matthew T. and Sunderland, Caroline (2019) Identifying playing talent in professional football using artificial neural networks. Journal of Sports Sciences. ISSN 0264-0414 (In Press)

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Abstract

The aim of the current study was to identify key performance indicators in professional
football that predict out-field players league status. The sample consisted of 966 players who
completed the full 90 minutes during the 2008/09 or 2009/10 season in the Football League
Championship. Players were assigned to one of three categories based on where they
completed most of their match time in the following season, then split based on five positions
including full backs (n = 205), centre backs (n = 193), centre midfielders (n = 205), wide
midfielders (n = 168) and attackers (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, regains and international appearances were key factors
in the predictive models. This is highly significant as objective position-specific predictors of
league status have not previously been published. The method could be used to aid the
identification and comparison of players as part of the due diligence process in professional
football.

Item Type: Article
Subjects: G Geography. Anthropology. Recreation > GV Recreation Leisure > GV557 Sports
Q Science > Q Science (General)
Q Science > QA Mathematics > QA76 Computer software
Divisions: Departments > Sport and Exercise Sciences
Depositing User: Matthew Robins
Date Deposited: 12 Sep 2019 08:56
Last Modified: 12 Sep 2019 08:56
URI: http://eprints.chi.ac.uk/id/eprint/4814

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