Position-Specific Lower Limb Performance and AI-Based Injury Risk Analysis in Young Male Basketball Players

This observational study aims to evaluate lower extremity performance characteristics in young male basketball players according to their playing positions and to explore injury risk patterns using artificial intelligence (AI)-based analysis. Basketball requires frequent jumping, sprinting, rapid changes of direction, acceleration, deceleration, and single-leg movements, all of which place significant demands on lower extremity function. Participants will undergo a single assessment session including anthropometric measurements and performance tests such as countermovement jump, reactive strength testing, single-leg performance tests, and agility assessments. Information regarding previous lower extremity injuries, training history, and playing position will also be collected. The study will compare performance characteristics among different basketball positions, including guards, forwards, and centers. In addition, AI and machine learning techniques will be used to analyze the collected performance data and identify patterns associated with injury risk. The purpose of the AI analysis is not to diagnose injuries but to investigate whether combinations of performance variables can help identify athletes who may demonstrate higher-risk movement or performance profiles. The findings may contribute to the development of position-specific training strategies, individualized performance monitoring, and evidence-based injury prevention approaches in youth basketball players.