Utilizing Artificial Intelligence for Enhancing Performance and Preventing Injuries in Sports Analytics

The sports sector could see a revolution in player performance enhancement and injury prevention, especially with recent developments in artificial intelligence (AI). In order to identify areas for improvement and potential injury risks, this proposed work aims to utilize the potential of AI techniques, such as XGBoost, to evaluate extensive player data, including movement patterns, biomechanics, and physical condition. This strategy is unusual because it combines real-time feedback systems with AI-powered predictive modeling to offer athletes and coaches individualized training advice and early warnings about potential injury risks. Cleaning and normalization were done during the preparation phase to make sure the data was suitable for analysis. Utilizing XGBoost for feature extraction allowed for the identification of critical factors affecting efficiency and injury risk. In sports analytics, the suggested XGBoost-AI system was compared to other AI-based techniques for improving performance and avoiding injuries. The study’s findings show that the XGBoost classifier significantly improves performance accuracy, which may reach 96%. This is in contrast to conventional approaches that depend on subjective evaluations and human data analysis, which can result in errors and inefficiencies. Python is used to implement the suggested work. AI-driven sports analytics platform can improve game strategy, fan engagement, and data-driven decision-making for sports organizations, enhancing competitiveness and sustainability in the sports industry.