Decoding defensive performance: a machine learning approach to football player valuation

Abstract Analyzing defensive actions, which have traditionally received less attention than offensive metrics, is a significant challenge in football analytics. This research presents an innovative methodology that utilizes XGBoost and deep neural networks to evaluate defensive performance using metrics such as On-Ball Value (OBV), Valuing Actions by Estimating Probabilities (VAEP), and eXpected Threat (xT). The study proposes a machine learning-based framework for evaluating the value of defensive players. A case study using expert ratings and market values from the Polish PKO BP Ekstraklasa demonstrates the method’s effectiveness. The results advance the field of sports analytics by addressing the persistent problem of accurately valuing the defensive contributions of football players.