Features importance

Unlike classical statistical modelling methods, XGBoost is a black-box model based on gradient boosting, and its internal working mechanism is challenging to understand. However, the interpretability of the model is very important in training practice. An injury risk model must be understandable and interpretable. Ideally, it should be able to explain the complete logic that provides the corresponding decision to all parties involved. This can help coaches and team doctors develop good training programs and adopt targeted interventions (Ruddy et al., 2019). Therefore, this study used shapley additive explanations (SHAP) for attribution analysis of the prediction model (Lundberg et al., 2020), calculating the absolute weight of each variable according to Eq. 15. We calculate the relative weight of each variable (i.e. the ratio of the absolute weight of a single variable to the sum of the absolute weights of all variables) to facilitate cross-sectional comparisons. We performed model construction, training, validation and analysis of important variables in the Python 3.6 programming environment.