The effect of injuries on football players' market values - the role of player positions

Abstract

Football players’ market values are intensively researched as the focus gradually shifts from qualitative approaches and subjective perception to data-driven estimation. Previous work drew on player characteristics and top-level performance metrics but only few included injuries. This thesis resorts to data from Opta and Transfermarkt and leverages MLR and machine learning approaches to derive market values. The LightGBM and MLR models predict market values accurately and identify direct impacts of injuries. Additionally, I found that player positions matter for injury incidence and severity.