82 2025 TSMA AI in sports events The application of AI in sports events has revolutionized the landscape of sports. Firstly, AI plays a crucial role in data collection and processing. Through video and sensors, AI can instantly gather various data from athletes, such as speed, position, and heart rate. This data, processed and analyzed by AI, can provide precise match statistics, helping coaches and athletes understand every detail of the game. For example, in football matches, AI can track each player’s running trajectory, ball possession time, and pass success rate. This data can be used to analyze player performance and develop tactics. Secondly, AI is key in formulating match strategies. By analyzing historical match data and opponents’ game footage, AI can simulate opponents’ tactics and movements, aiding coaches in devising more effective game plans. Additionally, AI can be used for real-time match analysis and feedback. Through real-time data analysis, AI can provide immediate feedback during the game, helping coaches and athletes make quick decisions. For instance, in basketball games, AI can analyze the effectiveness of each offense and defense, offering instant improvement suggestions, which are crucial for winning the game. AI’s application in sports events also includes predicting match outcomes. Using machine learning and deep learning techniques, AI can analyze vast amounts of historical data to predict the results of matches. In summary, the application of AI in sports data analysis not only enhances the scientific and precise nature of competitions but also provides comprehensive support in strategy formulation and real-time feedback. AI in injury prevention The application of AI in the sports field is becoming increasingly widespread, especially in the area of injury prevention, where it shows significant potential. Firstly, various data from athletes can be collected through wearable devices, such as performance metrics, training loads, injury history, fitness tests, and sleep patterns. This data is used to build AI models that assess the likelihood of an athlete getting injured in the near future and issue warnings. For example, AI can analyze an athlete’s running posture to identify abnormal movements that might lead to knee or ankle injuries, thereby suggesting posture adjustments
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