Аңдатпа
The explosive growth of sports data, including event logs, tracking streams, video records, wearable signals, and biomechanics-related measurements, has fundamentally changed the way athletic performance is studied, interpreted, and optimized. What previously relied on manual observation and conventional statistics is now increasingly supported by artificial intelligence capable of extracting patterns from large, heterogeneous, and high-frequency datasets. This review aims to systematize the principal machine learning and deep learning approaches used in contemporary sports analytics, with a particular focus on three major application domains: match outcome prediction, tactical and spatiotemporal analysis, and injury risk assessment and prevention. The article synthesizes the use of widely adopted methods, ranging from regression, decision trees, random forests, support vector machines, and gradient boosting to artificial neural networks, convolutional neural networks, recurrent architectures, and graph-based models. Special attention is given to the relationship between data modality and model selection, especially in studies using tracking data, event data, physiological inputs, and biomechanical indicators. In addition to summarizing methodological trends, the review examines how AI-driven systems contribute to coaching support, player evaluation, workload monitoring and tactical decision-making. The evidence suggests that AI and neural networks have moved beyond experimental use and are becoming a central analytical infrastructure in modern sport. At the same time, persistent limitations remain, including data heterogeneity, limited interpretability, inconsistent validation protocols, and difficulties in transferring results from controlled settings to real competitive environments. Overall, this review positions AI-based sports analytics as a rapidly evolving field with strong practical value and clear relevance for the development of next-generation intelligent systems in sport.