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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月17日

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  • Michał Zarȩba,
  • Tomasz Piłka,
  • Tomasz Górecki,
  • Bartłomiej Grzelak,
  • Krzysztof Dyczkowski

摘要

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.