Predicting player skills and optimizing tactical decisions in football data analysis using machine learning methods
Kassymova A. Aibatullin T. Yelezhanova S. Konyrkhanova A. Mukhanbetkaliyeva A. Tynykulova A. Makhazhanova U. Azieva G.
December 2025Institute of Advanced Engineering and Science
Bulletin of Electrical Engineering and Informatics
2025#14Issue 65057 - 5072 pp.
This study investigates the integration of machine learning (ML) techniques into football analytics to predict player skills and optimize tactical decisions. A dataset of over 150,000 professional match actions from various leagues and seasons was analyzed using deep neural networks, convolutional neural networks (CNNs), and gradient boosting machines (GBM) algorithms on biometric, contextual, and match data. The valuing actions by estimating probabilities (VAEP) metric indicated scores from +1.8 to +3.0 for key players, enabling detailed performance evaluation. CNN models achieved up to 91% precision, 88% recall, and a receiver operating characteristic – area under the curve (ROC-AUC) of 0.94, confirming their effectiveness in predicting player actions and contributions. Injury risk prediction using eXtreme gradient boosting (XGBoost) reached an F1-score of 0.87 and a ROC-AUC of 0.92, offering actionable insights for injury prevention and optimal player rotation. The findings highlight artificial intelligences (AI)’s capacity to support individualized preparation, tactical adjustments, and cost-effective recruitment strategies. While computational demands and data quality remain challenges, the results demonstrate the transformative potential of AI in modern football, providing a practical framework for data-driven decision-making to enhance team performance and strategic planning.
Artificial intelligence in sports , Football analytics , Gradient boosting machines , Machine learning , Player skill prediction
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Institute of Digital Economy and Sustainable Development, Higher School of Information Technology, Zhangir Khan University, Uralsk, Kazakhstan
Department of Software Engineering, Kh. Dosmukhamedov Atyrau University, Atyrau, Kazakhstan
Department of Information Security, Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Higher School of Information Technology and Engineering, Astana International University, Astana, Kazakhstan
Department of Information Systems, Eurasian National University named L.N. Gumilyov, Astana, Kazakhstan
Institute of Digital Economy and Sustainable Development
Department of Software Engineering
Department of Information Security
Higher School of Information Technology and Engineering
Department of Information Systems
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