FALL 2025

AUTHOR

Ethan Wang, St. Margaret’s Episcopal School

ABSTRACT

Injuries in the NBA have become consequential not only for team success but for the financial costs those teams suffer. This study develops a machine learning framework that predicts next-game injury risk using publicly available box-score data, player attributes, and injury history, then translates these probabilities into expected financial costs. Combining five datasets from 2010-2022, I derived sixteen workload and recency features and trained a Random Forest model optimized with five-fold cross-validation. At a 2% threshold for classification, the model predicts out-of-sample 69% of injuries while correctly ruling out 62% of healthy games, indicating better-than-chance predictive power is possible using solely public data. Feature-importance analysis identified workload shifts and rest as primary predictors.

Extending beyond prediction, this study gives a new way to interpret the financial implications of injuries, looking at how strategic rest decisions can minimize financial loss. This study offers NBA organizations a data-driven tool linking injury prevention with financial optimization, bridging injury forecasting with economic decision-making.