Predicting transfer fees in professional European football before and during COVID-19 using machine learning

Research question: Our study aims to extend findings from previous efforts exploring the factors associated with transfer fees to and from all big five league clubs in European football (men) by building upon advances in machine learning, which allow to depart from linear functional forms. Furthermore, we provide a simple test of whether the transfer market has changed since the beginning of the COVID-19 pandemic. Research methods: A fully flexible random forest estimator as well as generalized and quantile additive models are used to analyze smooth (non-linear) effects across different quantiles of scraped data (including remaining contract duration) from transfermarkt.de (n = 3,512). While we train our models with a randomly drawn subsample of before-COVID-19 transfers, we compare the prediction accuracy for two subsets of test data, that is, before and during COVID-19. Results and findings: Since our findings suggest several non-linear predictors of transfer fees, moving beyond linearity is insightful and relevant. Moreover, our models trained with before-COVID-19 data significantly underestimate the actual transfer fees paid during COVID-19 particularly for high- and medium-priced players, thus questioning any cooling-off effect of the transfer market. Implications: In the discussion of our findings, we showcase how moving beyond linearity and modeling quantiles can be revealing for both research and practice. We discuss limitations such as sample selection issues and provide directions for future research.