Sports Analytics for Evaluating Injury Impact on NBA Performance

This study investigates the impact of injuries on National Basketball Association (NBA) player performance over 20 seasons, using large-scale performance data and a statistical evaluation. Injury events were matched with player–game performance metrics to assess how various injury types influence short-, medium-, and long-term performance outcomes, measured across 2-, 5-, and 10-game windows. Using paired sample t-tests and Cohen’s d, we quantified both the statistical significance and effect size of changes in key performance metrics before and after injury. The analysis applies paired t-tests and Cohen’s d to quantify the statistical and practical significance of performance deviations pre- and post-injury. Our results show that while most injury types are associated with measurable performance declines, especially in offensive and defensive ratings, certain categories, such as cardiovascular injuries, demonstrate counterintuitive improvements post-recovery. These patterns suggest that not all injuries have equivalent consequences and highlight the importance of individualized recovery protocols. This work contributes to the growing field of sports injury analytics by combining statistical modeling and sports analytics to deliver actionable insights for coaches, medical staff, and performance analysts in managing player rehabilitation and optimizing return-to-play decisions.