التفاصيل البيبلوغرافية
| العنوان: |
Estimating transfer fees of professional footballers using advanced performance metrics and machine learning. |
| المؤلفون: |
McHale, Ian G.1 (AUTHOR) ian.mchale@liverpool.ac.uk, Holmes, Benjamin1,2 (AUTHOR) |
| المصدر: |
European Journal of Operational Research. Apr2023, Vol. 306 Issue 1, p389-399. 11p. |
| مصطلحات موضوعية: |
*PROFESSIONAL fees, MACHINE performance, MACHINE learning, PREDICTION models |
| الشركة/الكيان: |
ATLETICO de Madrid (Soccer team) |
| مستخلص: |
• Advanced performance metrics used to predict transfer fees. • The metrics improve prediction accuracy. • Machine learning models outperform linear models in out-of-sample predictions. • Value-for-money assessed for individual transfers and for clubs. 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. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Business Source Index |