This analysis shows how a machine learning framework improves evaluations of defensive actions in football, indicating its importance in player valuation.
Abstract Analyzing defensive actions, which have traditionally received less attention thanoffensive metrics, is a significant challenge in football analytics. This researchpresents an innovative methodology that utilizes XGBoost and deep neuralnetworks to evaluate defensive performance using metrics such as On-Ball Value(OBV), Valuing Actions by Estimating Probabilities (VAEP), and eXpectedThreat (xT). The study proposes a machine learning-based framework forevaluating defensive player value. A case study using expert ratings and marketvalues from the Polish PKO BP Ekstraklasa demonstrates the method’seffectiveness. The results advance the field of sports analytics by addressing thepersistent problem of accurately valuing the defensive contributions of footballplayers.
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Zareba et al. (2025) studied this question.
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