Improving The Evaluation of Defensive Player Values with Advanced Machine Learning Techniques

Quantifying defensive actions, which offensive indicators have historically overshadowed, is challenging in football analysis. This study presents a novel approach using XGBoost and neural networks to evaluate defensive play using On-Ball Value (OBV), Valuing Actions by Estimating Probabilities (VAEP), and eXpected Threat (xT) indicators. The proposed evaluation of Defensive Player Value using machine learning techniques is presented. A comparative assessment of expert ratings and market values in a Polish PKO BP Ekstraklasa case study highlights the method's effectiveness. The research contributes to the development of sports analytics by addressing the long-term challenge of evaluating the defensive play of football players.