Brinca, PedroKüpper, Tim2023-12-122023-12-122023-01-272023-01-27http://hdl.handle.net/10362/161116Football 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. Our LightGBM and MLR models predict market values accurately and identify direct impacts of injuries. Additionally, we found that recurring injuries affect especially young athletes.engFootballMarket valuesInjuriesMachine learningLightGBMMLROptaTransfermarktmaster thesis203317653