Leveraging Machine Learning to Predict National Basketball Association Player Injuries

In the high-intensity world of the National Basketball Association(NBA), injuries are a pervasive and complex issue that significantly impacts team dynamics, player careers, and organizational strategies. NBA players face a variety of injuries, including muscle strains, ligament tears, bone fractures, sprains, and tendonitis, among others. Factors contributing to injury risk encompass a wide range of variables, including player physical characteristics, biomechanics, conditioning, fatigue, playing style, and history of previous injuries. Height, weight, wingspan, agility, sprint speed, and body fat percentage, which are the focus features of this study, can play pivotal roles in injury susceptibility. To address the multifaceted challenge of predicting injuries among NBA players based on various features, this project employs a range of machine learning models. Random Forest is utilized for its ability to handle high-dimensional data and capture complex relationships, K-Nearest Neighbors (KNN) is applied for its instance-based learning that can classify players based on similar attributes, and Gradient Boosting is used to iteratively improve the model's predictions by focusing on the errors of previous iterations. These algorithms provide a robust framework for analyzing player data, enhancing the accuracy of injury predictions, and ultimately aiding in injury prevention and optimizing player performance. These models were evaluated using multiple metrics: accuracy, F1 score, recall, and precision. Random Forest achieved the highest accuracy at 92.47%, followed by Gradient Boosting at 91.94%, and KNN at 88.17%. By analyzing historical injury data alongside these features, we aim to illuminate the complex interplay between player characteristics and injury risk and estimate injury probabilities. Through this approach, we provide valuable insight into injury prediction for NBA organizations, enhancing injury prevention and optimizing player performance.