Artificial Intelligence in Team Sports: Methods for Data Collection and Performance Analysis — A Narrative Review

AI is now central in sports analytics, helping to improve player monitoring, identification and assessment of their performance. This paper looks closely at AI strategies for football, with maj or emphasis on machine learning, neural networks and computer vision approaches. By reviewing articles, we study how AI contributes to ongoing player monitoring, prediction of events and decision-making during matches. The results indicate progress in automated data handling, but certain problems persist, including the need for understandable algorithms, the question of who owns the data, biased results and adjustment to changing situations. Working on these limitations will give sports the best chance to use AI. Future research should emphasize the development of adaptive AI models, the integration of physiological and psychological factors, and ethical considerations related to data security and transparency. By overcoming these obstacles, AI can drive a new era in sports analytics, providing deeper insights and optimizing team performance in an increasingly data-driven competitive landscape.