Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Document Type
Event
Start Date
24-11-2025 4:00 PM
Description
This project examines whether player performance data can signal injury risk before an absence occurs. Using game-by-game net rating trends, I applied an exponentially weighted control-chart approach to detect early shifts in performance that might indicate a rising risk of re-injury. The method successfully identified 71% of re-injury cases with an average 20-game lead, suggesting that performance declines can serve as an early warning signal. While the false-alarm rate was high, the results show that performance-based monitoring has potential value for teams seeking proactive player-health insights.
Included in
UC-0223 Predicting NBA Player Re-Injury Using Net Rating
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
This project examines whether player performance data can signal injury risk before an absence occurs. Using game-by-game net rating trends, I applied an exponentially weighted control-chart approach to detect early shifts in performance that might indicate a rising risk of re-injury. The method successfully identified 71% of re-injury cases with an average 20-game lead, suggesting that performance declines can serve as an early warning signal. While the false-alarm rate was high, the results show that performance-based monitoring has potential value for teams seeking proactive player-health insights.