Artificial neural networks and player recruitment in professional soccer

Barron, D., Ball, G. ORCID: 0000-0001-5828-7129, Robins, M. and Sunderland, C. ORCID: 0000-0001-7484-1345, 2018. Artificial neural networks and player recruitment in professional soccer. PLoS ONE, 13 (10): e0205818. ISSN 1932-6203

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Abstract

The aim was to objectively identify key performance indicators in professional soccer that influence outfield players’ league status using an artificial neural network. Mean technical performance data were collected from 966 outfield players’ (mean SD; age: 25 ± 4 yr, 1.81 ±) 90-minute performances in the English Football League. ProZone’s MatchViewer system and online databases were used to collect data on 347 indicators assessing the total number, accuracy and consistency of passes, tackles, possessions regained, clearances and shots. Players were assigned to one of three categories based on where they went on to complete most of their match time in the following season: group 0 (n = 209 players) went on to play in a lower soccer league, group 1 (n = 637 players) remained in the Football League Championship, and group 2 (n = 120 players) consisted of players who moved up to the English Premier League. The models created correctly predicted between 61.5% and 78.8% of the players’ league status. The model with the highest average test performance was for group 0 v 2 (U21 international caps, international caps, median tackles, percentage of first time passes unsuccessful upper quartile, maximum dribbles and possessions gained minimum) which correctly predicted 78.8% of the players’ league status with a test error of 8.3%. To date, there has not been a published example of an objective method of predicting career trajectory in soccer. This is a significant development as it highlights the potential for machine learning to be used in the scouting and recruitment process in a professional soccer environment.

Item Type: Journal article
Publication Title: PLoS ONE
Creators: Barron, D., Ball, G., Robins, M. and Sunderland, C.
Publisher: Public Library of Science
Date: 31 October 2018
Volume: 13
Number: 10
ISSN: 1932-6203
Identifiers:
NumberType
10.1371/journal.pone.0205818DOI
Rights: © 2018 Barron et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 08 Nov 2018 16:44
Last Modified: 09 Nov 2018 16:14
URI: http://irep.ntu.ac.uk/id/eprint/34875

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