Estimating transfer fees of professional footballers using advanced performance metrics and machine learning

McHale, Ian G ORCID: 0000-0002-7686-3879 and Holmes, Benjamin (2022) Estimating transfer fees of professional footballers using advanced performance metrics and machine learning. European Journal of Operational Research, 306 (1). pp. 389-399.

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Official URL: http://dx.doi.org/10.1016/j.ejor.2022.06.033

Abstract

The paper presents a model for estimating the transfer fees of professional footballers. We seek to improve on the literature in two dimensions. First, we utilise advanced player performance metrics to better capture the playing ability of footballers. Second, we adopt machine learning algorithms to improve out-of-sample prediction accuracy. The model proves to be a considerable improvement on linear regression, and the advanced performance metrics further improve the predictions. We use the model to identify value-for-money transfers, before assessing the past records of clubs in identifying value-for-money and find that, Liverpool and Atlético Madrid, for example, are successful at identifying value-for-money, whilst Manchester United and Barcelona are not.

Item Type:	Article
Uncontrolled Keywords:	OR in sports, Analytics, Machine learning, Moneyball, xgboost
Divisions:	Faculty of Humanities and Social Sciences > School of Management
Depositing User:	Symplectic Admin
Date Deposited:	13 Jul 2022 10:48
Last Modified:	25 Jan 2023 15:41
DOI:	10.1016/j.ejor.2022.06.033
Open Access URL:	https://doi.org/10.1016/j.ejor.2022.06.033
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3158381