## 3 Results  
### Predictive Performance  
In this section, results are displayed and analysed in terms of the predictive performance. Figures 2 and 3 represent boxplots of the predictive performance of all models according to the different feature sets described in Table 3. Table 2 contains a reminder of the notions of accuracy, precision, recall and AUC. In this study, the accuracy is not given priority since it assumes equal weight for different labels whilst on the one hand injuries are highly more sensitive than non-injuries and our dataset is naturally unbalanced since injury is relatively rare. AUC is a standard metric for the evaluation of predictive models given unbalanced dataset but its interpretation is not easy. Therefore precision and recall have the highest priority in some part of this study, recall being slightly prioritised since missing an injury prediction has more severe consequence than falsely predicting one (in terms of health and career). The best performances were obtained with the KNN, tree, forest and XGB classifiers. The logit and GNB classifiers do not seem to be significantly more accurate than the baseline \(B\) for all considered feature sets and time-horizons. The same observation can be done for the Ridge classifier and MLP but only for 1-week horizon predictions. For all models, performances were better when personal features are included as inputs than when only the number of past injuries is used as predictor of future ones. The addition of GPS and/or questionnaires data in features enabled much higher performances most of the time except for some models (e.g., see Figure 3 the recall of the LDA model for one-month prediction decreases when questionnaire data are used, or in Figure 2 as it is the case of the precision of MLP for one-week prediction). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Set Name** & **Past Injuries** & **Personal Features** & \(GPS\)**Data** & **Questionnaires Data** \\ \hline only injuries & x & & & \\ personal features & x & x & & \\ \(GPS\) & x & x & x & \\ questionnaires & x & x & & x \\ all & x & x & x & x \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Attributes sets. According to those remarks, Figures 4 and 5 represent the results obtained for KNN and tree-based classifiers with features including past injuries, personal features and GPS or/and questionnaires data. The terms 'GPS' and 'questionnaire' 'features sets' will implicitly include past injuries and personal features in the following of this manuscript. It appears clearly that the choice of features has a higher impact on short terms (1 week) predictions than on mid term (1 month) ones. It is also noticeable that higher performance can be obtained for 1 month predictions with maximum values around 97% for all metrics, probably due to less important labels imbalance (considering 1-week horizon, injury is a much more rare event than for 1-month time windows). In the latter configuration best performances were always obtained with XGB closely followed by random forest. For 1 week predictions, the best accuracy and recall were obtained by random forest with GPS data, the highest precision with classification tree and the best AUC was achieved by XGB. It is remarkable that for 1 week horizon, best predictions are always obtained with questionnaires data with a significant difference comparing to GPS data for the same models. In that time window, GPS data even seem to worsen injury prediction quality (e.g., for tree we have \(performance(questionnaire)>performance(GPS+questionnaire)\)). This could be explained by the fact that internal load has a more \"readable\" impact on short-term injury risk through the expressiveness of questionnaires contrary to  
Figure 2: One-week predictive performance of ten machine learning methods according to five types (in color) of data sets. Predictive results are presented as box plots, in which the box indicates quartile values; the whiskers indicate the upper and lower values; circle indicates outlier.