Year: 2025
Type: article
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.
Source: Journal Of Big Data
Authors: Michał Zarȩba, Tomasz Piłka, Tomasz Górecki, Bartłomiej Grzelak, Krzysztof Dyczkowski
Institution: Adam Mickiewicz University in Poznań
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,290
OpenAlex ID: W4417423020