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This study examines the multifaceted field of injuries and their impacts on performance in the National Basketball Association (NBA), leveraging a blend of Data Science, Data Mining, and Sports Analytics. Our research is driven by three pivotal questions: Firstly, we explore how Association Rule Mining can elucidate the complex interplay between players’ salaries, physical attributes, and health conditions and their influence on team performance, including team losses and recovery times. Secondly, we investigate the relationship between players’ recovery times and their teams’ financial performance, probing interdependencies with players’ salaries and career trajectories. Lastly, we examine how insights gleaned from Data Mining and Sports Analytics on player recovery times and financial influence can inform strategic financial management and salary negotiations in basketball. Harnessing extensive datasets detailing player demographics, injuries, and contracts, we employ advanced analytic techniques to categorize injuries and transform contract data into a format conducive to deep analytical scrutiny. Our anomaly detection methodologies, an ensemble combination of DBSCAN, isolation forest, and Z-score algorithms, spotlight patterns and outliers in recovery times, unveiling the intricate dance between player health, performance, and financial outcomes. This nuanced understanding emphasizes the economic stakes of sports injuries. The findings of this study provide a rich, data-driven foundation for teams and stakeholders, advocating for more effective injury management and strategic planning. By addressing these research questions, our work not only contributes to the academic discourse in Sports Analytics but also offers practical frameworks for enhancing player welfare and team financial health, thereby shaping the future of strategic decisions in professional sports.
Sarlis et al. (Mon,) studied this question.