Effective injury forecasting in soccer with GPS training data and machine learning

Area of Science:

• Sports Medicine
• Data Science in Athletics

Background:

• Soccer injuries significantly impact team performance and incur high rehabilitation costs.
• Current research offers limited insight into injury risk factors and lacks injury forecasting models.

Purpose of the Study:

• To develop and evaluate a multi-dimensional approach for forecasting injuries in professional soccer.
• To assess the accuracy and interpretability of machine learning models for injury prediction.

Main Methods:

• Utilized GPS tracking technology to collect player training workload data from a professional soccer club over a season.
• Developed and implemented machine learning algorithms to construct an injury forecasting model.

Main Results:

• The proposed injury forecaster demonstrates high accuracy in predicting player injuries.
• The model provides interpretable insights, offering practical rules for practitioners.

Conclusions:

• This novel approach enhances injury prevention strategies in professional soccer.
• It clarifies the complex relationship between training performance and injury risk, aiding practitioners.