A deep learning approach to injury forecasting in NBA basketball

Abstract

Predicting athlete injury risk has been a holy grail in sports medicine with little progress to date due to a variety of factors such as small sample sizes, significantly imbalanced data, and inadequate statistical approaches. Modeling approaches which are not able to account for the multiple interactions across factors can be misleading. We address the small sample size by collecting longitudinal data of NBA player injuries using publicly available data sources and develop a state of the art deep learning model, METIC, to predict future injuries based on past injuries, game activity, and player statistics. We evaluate model performance using metrics appropriate for imbalanced data and find that METIC performs significantly better than other traditional machine learning approaches. METIC uses feature learning to create interactive features which become meaningful in combination with each other. METIC can be used by practitioners and front offices to improve athlete management and reduce injury incidence, potentially saving sports teams millions in revenue due to reduced athlete injuries.

Development of an algorithm-based approach using neuromuscular test results to indicate an increased risk for non-contact lower limb injuries in elite football players

“…Nevertheless, screening can still highlight key risk factors for sports injuries 22. The current literature comprises similar proofs of concept in the NBA11 12 or applied to a specific injury—ACL injury 23. The study of Cohan et al predicted injury based on the injury mechanism, player’s characteristics and game statistics,11 while Lu et al focused mostly on injury history and past concussions 12.…”

“… 22 The current literature comprises similar proofs of concept in the NBA 11 12 or applied to a specific injury—ACL injury. 23 The study of Cohan et al predicted injury based on the injury mechanism, player’s characteristics and game statistics, 11 while Lu et al focused mostly on injury history and past concussions. 12 Jauhiainen et al included an extensive screening protocol comprising neuromuscular and functional tests.…”

“…In sports medicine, AI methods have already been proven beneficial to tackle this challenging multifaceted task of identifying injury risk factors. 11 12 For example, ML methods can be used to improve injury prediction and allow proper approaches to injury prevention. Regarding the popularity of the different ML techniques, tree-based models are currently the most popular.…”

Unsupervised Learning in NBA Injury Recovery: Advanced Data Mining to Decode Recovery Durations and Economic Impacts

“…The identification and assessment of risk factors for ACL injuries is an important step in reducing the risk of injury in athletes. By taking a proactive approach and addressing risk factors, athletes can not only reduce their risk of injury but also improve their overall performance [10][11][12][13][14][15][16].…”

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

“…This study emphasizes the importance of correctly splitting data to avoid overfitting and proposes a novel model, METIC (Multiple bidirectional Encoder Transformers for Injury Classification), for assessing injury classification. The model is designed to process sequences of data related to past injuries and games, offering insights into risk factors and the potential for predicting future injuries based on player history and game loads [18].…”