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

Ian G. McHale and Benjamin Holmes

European Journal of Operational Research, 2023, vol. 306, issue 1, 389-399

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.

Keywords: OR in sports; Analytics; Machine learning; Moneyball; xgboost (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:306:y:2023:i:1:p:389-399

DOI: 10.1016/j.ejor.2022.06.033

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