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

arXiv: https://arxiv.org/abs/1705.08079 · HTML: https://mlsanity.com/paper/1705.08079/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2017.

## Built on by (most cited first, 4 of 4)

- [Finding Pre-Injury Patterns in Triathletes from Lifestyle, Recovery and Load Dynamics Features](https://mlsanity.com/paper/2511.17610/) (2025)
- [Autoencoder Architectures for Athlete Performance Scoring from Wearable Telemetry](https://mlsanity.com/paper/2606.28145/) (2026)
- [Learning Latent Memory States from Longitudinal Athlete Monitoring Data](https://mlsanity.com/paper/2608.06290/) (2026)
- [Multimodal Injury Risk and Performance Prediction in Tennis Using Weighted Ensemble Learning](https://mlsanity.com/paper/2608.21530/) (2026)

## Abstract (arXiv)

Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.