## 4 Discussion  
In view of the overall results of this study, some notable facts should be noted. First, for 1-week injury prediction, questionnaire (internal load features) data are more accurate than GPS (external load features) ones, which even tend to deteriorate injury prediction when included in the learning data. For 1-month injury prediction, the classifiers learnt from GPS or questionnaire data show roughly the same performance levels, the best one being usually reached when combining GPS and questionnaire data. In terms of interpretation, decision trees graphs and features importance weights computation have highlighted a specific player profile at high injury risk and some specific features involved in precision and recall optimisation. To the best of our knowledge, the work of [PERSON] et al. [19] is the single that used a non-linear classifier, decision tree, in a multi-dimensional context to predict injuries in elite soccer. Thus, we decided to focus part of our discussion to this study. For comparison, the decision trees used in the study by [PERSON] et al. [19] detected about 80% of the injuries in the sample analyzed with an accuracy of approximately 50% (with external load features). As a result, the algorithm used in our machine learning approach would be able to classify more accurately the so-called at-risk players regarding the past occurrence of injuries and thus be able to continue to perform without being disturbed by \"false alarms\". The accuracy of this tree, particularly at 1 week, which differs from [PERSON] et al. [19], is made possible by linking GPS data and subjective questionnaires throughout the classifiers, which justifies the contribution of this work to the current literature linking data science and sport science [17; 19; 36]. In the present study, we showed that subjective variables have a very high predictive/explanatory potential (compared to objective variables) but they are more expensive, i.e., having all players completing questionnaires before and after training can be complicated given their tight schedules and their willingness. Nevertheless, professional teams that can not outfit players with GPS sensors for practical or economic reasons should consider use questionnaires in order to detect players at high injury risk [37; 38]. Another point that validates the choice of tree-based classifiers is that those models naturally provide feature importance weights that can help coaches to monitor some specific indicators and be used as useful decision support tools for training optimization. It should be noted that in this case, subjective questionnaires are very valuable especially for short-term prediction even when they are completed by only some players at some training sessions. Except for 1-week injury risk precision, ensemble models seem preferable compared to single trees even if they do not provide single tree graphs. In addition, the interest of this study lies in the coupling of the machine learning methods and the variations of the training load (internal and external). It can be noticed that even when both types of features (GPS and questionnaires) are used as inputs, the most important and sensitive features are almost always associated with subjective variables. It can therefore be hypothesized that with these data and this sample in this particular situation, internal load would be a determining factor in the prediction of injury. In other words, it would be essential for each coach to pay particular attention to the athletes' feelings before and after training sessions in order to prevent injuries from occurring. To conclude, the fact that questionnaires features can replace GPS ones and even increase predictive performance by doing so suggests that a part of the information related to external load is included in the internal load's one. While an individual may perform the same external load, their ability to respond to this output (internal load) may differ [17; 39].