Identifying playing talent in professional football using artificial neural networks
| Autor: | Caroline Sunderland, Graham Ball, Donald Barron, Matthew T. Robins |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Competitive Behavior
GV557 Artificial neural network Decision Making Applied psychology Football Aptitude 030209 endocrinology & metabolism Physical Therapy Sports Therapy and Rehabilitation Sample (statistics) 030229 sport sciences Athletic Performance League Q1 QA76 03 medical and health sciences 0302 clinical medicine Humans Orthopedics and Sports Medicine Neural Networks Computer Performance indicator Championship Psychology |
| ISSN: | 0264-0414 |
| Popis: | 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 backpropagation 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. |
| Databáze: | OpenAIRE |
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