Time-to-Injury Forecasting in Elite Female Football: A DeepHit Survival Approach

TL;DR

Overall, this study provides a novel proof of concept: survival modelling with DeepHit shows strong potential to advance injury forecasting in football, offering accurate, explainable, and actionable insights for injury prevention across competitive levels.

Abstract

Injury occurrence in football poses significant challenges for athletes and teams, carrying personal, competitive, and financial consequences. While machine learning has been applied to injury prediction before, existing approaches often rely on static pre-season data and binary outcomes, limiting their real-world utility. This study investigates the feasibility of using a DeepHit neural network to forecast time-to-injury from longitudinal athlete monitoring data, while providing interpretable predictions. The analysis utilised the publicly available SoccerMon dataset, containing two seasons of training, match, and wellness records from elite female footballers. Data was pre-processed through cleaning, feature engineering, and the application of three imputation strategies. Baseline models (Random Forest, XGBoost, Logistic Regression) were optimised via grid search for benchmarking, while the DeepHit model, implemented with a multilayer perceptron backbone, was evaluated using chronological and leave-one-player-out (LOPO) validation. DeepHit achieved a concordance index of 0.762, outperforming baseline models and delivering individualised, time-varying risk estimates. Shapley Additive Explanations (SHAP) identified clinically relevant predictors consistent with established risk factors, enhancing interpretability. Overall, this study provides a novel proof of concept: survival modelling with DeepHit shows strong potential to advance injury forecasting in football, offering accurate, explainable, and actionable insights for injury prevention across competitive levels.

Figures (4)

• Figure 1: Grid Search method used, including custom evaluation formula and different feature selection techniques.
• Figure 2: Distribution of data points in the cleaned dataset across 37 players over two football seasons (322 days recorded). Red circles indicate injury occurrences (n = 43).
• Figure 3: Predicted injury risk over time by a DeepHit model trained using a LOPO method. Red dotted lines indicate injury occurrences.
• Figure 4: Results of SHAP analysis determining the features that most influenced a prediction of elevated injury risk for one player on one day.