Injury Patterns and Impact on Performance in the NBA League Using Sports Analytics

Abstract

This research paper examines Sports Analytics, focusing on injury patterns in the National Basketball Association (NBA) and their impact on players’ performance. It employs a unique dataset to identify common NBA injuries, determine the most affected anatomical areas, and analyze how these injuries influence players’ post-recovery performance. This study’s novelty lies in its integrative approach that combines injury data with performance metrics and salary data, providing new insights into the relationship between injuries and economic and on-court performance. It investigates the periodicity and seasonality of injuries, seeking patterns related to time and external factors. Additionally, it examines the effect of specific injuries on players’ per-match analytics and performance, offering perspectives on the implications of injury rehabilitation for player performance. This paper contributes significantly to sports analytics, assisting coaches, sports medicine professionals, and team management in developing injury prevention strategies, optimizing player rotations, and creating targeted rehabilitation plans. Its findings illuminate the interplay between injuries, salaries, and performance in the NBA, aiming to enhance player welfare and the league’s overall competitiveness. With a comprehensive and sophisticated analysis, this research offers unprecedented insights into the dynamics of injuries and their long-term effects on athletes.

Leveraging Sports Analytics and Association Rule Mining to Uncover Recovery and Economic Impacts in NBA Basketball

“…Financial aspects are equally critical; unplanned absences due to injuries can lead to significant financial losses for NBA franchises. This study explores how Data Science and Sports Analytics can mitigate these risks by providing deeper insights into player health and strategic game management [7][8][9][10][11][12][13].…”