AI and large language models hold the potential to revolutionize sports science by leveraging vast amounts of data to improve athlete performance and prevent injuries. By analyzing data from wearable devices, game footage, and training sessions, AI can analyze biomechanics, identify risky patterns, provide real-time feedback, and design personalized training regimens tailored to an athlete’s unique physiology and performance goals. Large language models can further support coaches and athletes by synthesizing insights from sports science research, offering performance optimization strategies, and/or creating tailored intervention and recovery plans. These tools can simplify complex scientific findings into actionable advice, promoting smarter, data-driven decision-making in sports science.

Sports Science AI supports both performance optimization and injury risk mitigation, providing quantitative assessment alongside automated natural-language explanation. This project operates as a unified, expandable ecosystem comprising fitness biomechanics, baseball skill assessment, and badminton technique analysis. The fitness module evaluates exercise execution in terms of joint loading, movement efficiency, and compensatory patterns during strength and conditioning tasks. The baseball module quantifies batting motion in adolescent athletes through comparative biomechanical modeling against expert swing templates, enabling age-appropriate skill correction. The badminton module applies pose-based inference to characterize stroke mechanics, footwork timing, and tactical movement quality. In combination, these capabilities demonstrate its potential as a comprehensive AI framework capable of translating professional sports science into accessible, evidence-based digital coaching for diverse athletic populations.