Abstract
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.
Paper Type
Poster
DOI
10.62036/ISD.2024.67
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.
Recommended Citation
Zaręba, M., Piłka, T., Górecki, T., Grzelak, B. & Dyczkowski, K. (2024). Improving The Evaluation of Defensive Player Values with Advanced Machine Learning Techniques. In B. Marcinkowski, A. Przybylek, A. Jarzębowicz, N. Iivari, E. Insfran, M. Lang, H. Linger, & C. Schneider (Eds.), Harnessing Opportunities: Reshaping ISD in the post-COVID-19 and Generative AI Era (ISD2024 Proceedings). Gdańsk, Poland: University of Gdańsk. ISBN: 978-83-972632-0-8. https://doi.org/10.62036/ISD.2024.67