## 4 Discussion
Developments in the area of machine learning, such as TreeExplainer [25], which is not only a strong theoretically grounded method to calculate feature importance [26], but also allows to build visualisations that indicate the direction of the relation of a variable with performance, can be helpful in the translation from science to practice. Illustrations such as those displayed in Figures 3 and 4 can be useful for analysts to show how features impact game outcome. Future studies should attempt to add more detailed information for several features, for example, total distance in and out of possession or detailed information on the position of crosses. This information can be informative to the model and aid the explanation of results. As data is often provided by sports data companies, these companies should be encouraged to add more detail to the provided data to allow more thorough analyses. The use of \"new\" features should also be encouraged, to test their added value in relation to other, more established features. It should also be noted that the results from this study cannot fully distinguish whether a variable is the cause of a (un)favourable scoreline, or the effect. To illustrate, losing teams attempt to turn the game and therefore may engage in more high-intensity efforts [34]. The winning side on the other hand, may fall back,allowing less space for losing side to play and perform sprints, while the losing team may apply a more risk-taking strategy, which could result in more counter-attacking play of the winning side [39; 52]. Therefore, more research is necessary to gain more insight into the cause-effect relation between performance indicators and game outcome. The results from our study show which variables can be considered as the best predictors of an accurate model predicting winning and losing in professional Belgian soccer. It provides the direction of the relationship of these variables with winning and losing, also in relation to the other predictors. It was shown that not only shot-related variables, but a broad range of variables are amongst the strongest predictors of winning and losing. As the workflow from dataset to predictive modelling was also described in detail, similar approaches can be used to evaluate the current performance indicators provided to the coaching staff and other stakeholders connected to the team. It seems particularly interesting to look at physical parameters of the second half, given that they are amongst the best predictors of game outcome in soccer. Variables such as ELO-ratings, transfer values, match location and Playing Styles can be useful additions to current approaches used to evaluate game performances. Conceptualization, Y.G., J.B., S.V. and J.G.B. ; methodology, Y.G., J.B., S.V. and J.G.B. ; software, Y.G., and S.V. ; validation, Y.G., J.B., S.V. and J.G.B. ; formal analysis, Y.G., and S.V. ; investigation, Y.G. ; resources, Y.G. ; data curation, Y.G. and S.V. ; writing--original draft preparation, Y.G. ; writing--review and editing, Y.G., J.B., S.V. and J.G.B. ; visualization, Y.G., and S.V. ; supervision, Y.G., J.B., S.V. and J.G.B. ; project administration, Y.G. and J.G.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Restrictions apply to the availability of these data. Part of the data was obtained from STATS Perform. Please contact [PERSON] ([EMAIL_ADDRESS]) to inform about the data availability. The authors would like to thank team of [PERSON]. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: