We explore factors associated with transfer fees in European professional football by building upon advances in machine learning, which allow to depart from linear functional forms. Next to fully flexible estimators we employ generalized and quantile additive models to analyze smooth (non-linear) effects across different quantiles. 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.

Research Line: Athletes, Teams and Performance.

Funding / Support: Intramural Funding.

Principal Investigators: Yanxiang Yang, Jörg Königstorfer, Tim Pawlowski

Publication:

• Predicting transfer fees in professional European football before and during COVID-19 using machine learning. Published in the European Sport Management Quarterly.