AI-Driven Performance Analysis of College-Level Inter- Collegiate Kabaddi Players

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This study critically analyzes the integration of artificial intelligence (AI)
within the performance evaluation of college-level inter-collegiate kabaddi
players, focusing on the development of an innovative framework that
leverages machine learning algorithms. By systematically identifying key
performance indicators (KPIs) that notably influence players' efficacy, we
utilize extensive performance data collected via video analytics and motion
tracking to offer actionable insights. The empirical findings reveal strong
correlations among tailored training regimens, quantifiable performance
metrics, and success rates as illuminated by AI analysis. This research not
only contributes to the academic discourse surrounding sports science but
also provides practical implications for coaches and athletes, thereby
fostering a more data-driven approach to enhancing competitive strategies in
kabaddi.