Advances in artificial intelligence present new opportunities for enhancing sports performance analysis, injury prevention, and strategic decision-making. This research proposes the development of an AI-powered sports analytics framework leveraging the PySport library for data acquisition, processing, and model integration.
The system applies machine learning techniques to extract actionable insights from player statistics, game events, and performance metrics. Predictive models are designed for talent scouting, match outcome forecasting, and personalized training recommendations.
Framework
By combining AI-driven analytics with the flexible data handling capabilities of PySport, the project aims to deliver a versatile and scalable solution that supports coaches, analysts, and athletes in data-informed decision-making. The framework integrates computer vision for tracking, natural language processing for commentary analysis, and reinforcement learning for tactical optimization.