Artificial Intelligence in Performance Analysis of Football Matches and Players

Football is a widely followed sport and has developed into a major industry in which performance analysis is important for improving team tactics and player development. Artificial intelligence (AI), computer vision [1], [2], [3], machine learning, wearable sensors, and tracking technologies provide new ways to analyse matches and player performance. This review examines the use of these technologies for football performance analysis, including player and ball tracking, tactical analysis [3], [4], [5], [8], match prediction, player valuation, and health monitoring. The reviewed literature includes studies published between 2016 and 2023 and identified through Scopus, Google Scholar, and ScienceDirect. The review covers methods such as YOLO, SORT, Deep SORT, convolutional neural networks, support vector machines, random forests, Naive Bayes, reinforcement learning, and wearable sensor systems. The reported results show that AI-based approaches can support real-time tracking, tactical analysis, match prediction, and health monitoring. The review also indicates that accuracy, computational requirements, data quality, and system reliability remain important challenges.

Artificial intelligence; football analytics; computer vision; player tracking; ball tracking; machine learning; wearable sensors

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