Predicting & Understanding Football Transfervalues based on Match Event Data

Throughout this project, I collaborated closely with a football team that provided comprehensive event data, including metrics such as the number of passes in the attacking third, for all matches spanning the past five seasons. Leveraging this dataset, I developed multiple machine learning models to predict the market value of players based on both individual performance data and team-level statistics. The project not only provided the team with market value predictions for a wide range of players in their database but also offered valuable insights into the key events that influence player value and the specific ways in which these events impact their market worth.