## 4 Discussion  
This study aimed to identify the strongest predictive variables of winning and losing in Belgian professional soccer. A broad spectrum of variables was used to build a predictive model. The results showed that more than 89% of the games resulting in a win or loss could be correctly classified. Total shots on target from the attacking penalty box showed to be the strongest predictor. Interestingly, a broad range of variables, including physical indicators such as the distance in several speed zones, the number of accelerations (\(>\)2 m/s\({}^{2}\)) and number of actions \(>\)15 km/h showed to be among the most important variables related to game results, as well as contextual variables such as ELO-rating, the total added transfer value of the benched players and match location. There are different purposes for predicting game outcome. Previous studies relating to the prediction of game outcome in soccer often focused on betting [27]. These studies used historic games to construct a model, which was then evaluated by predicting future soccer games. In our study however, the aim was to identify the strongest predictive variables, so instead of a division between historic and future games, a cross-validation approach was used to evaluate model performance. The prediction accuracy was considerably higher compared to a similar study conducted by [28], who reported classification accuracies of 72.7% and 83.3% when predicting respectively losing and winning in professional soccer using artificial neural networks. A classification accuracy of amply 89% can be considered as high, as it has been shown that chance plays a major role in goal-scoring [29]. The results show that the majority of the misclassifications occur when the final goal difference between two teams is small. Given these small goal differences between teams, and the impact that each goal has on game outcome [29], the occurrence of \"lucky winners\" or \"unlucky losers\" is frequent in low-scoring sports [30], and classification accuracies of close to 100% seem improbable. It was hypothesized that shot-related variables were among the strongest predictors, as previous studies already showed that shot-related variables closely relate to game outcome in soccer [6; 8; 10; 12]. In this study, the total shots on target from the attacking penalty box showed to be the best predictor of winning and losing. Of all shot-related variables, total shots on target from the attacking penalty box was the only variable which was not rejected by VIF or BorutaShap, showing that shot-related variables are closely interrelated. It should be noted that this does not directly indicate that other shot-related variables can be deemed as unimportant, but that the information entailed by those measures is already captured, or better captured, by other metrics in relation to game outcome. The number of shots on or off target are often reported by sports data providers, as opposed to location, which is usually not reported. It may therefore be useful to either use both total shots on target from the penalty box, or use a metric such as Expected Goals, which also takes shot location into account. Other often-reported metrics such as the total number of passes and the number of successful passes are not among the best important predictors of game outcome, which may also be explained by the informative value to the model in comparison to other variables. This may also explain why Playing Styles-related possessions, such as Direct Play and Counter Attack, are amongst the best predictors, as they may provide more information to the model than total ball possession as an isolated metric. Physical fitness is deemed as an important factor relating to performance in soccer, however, the role of physical game output in relation to other performance indicators remained to be elucidated [31]. In our study, several physical indicators are shown to be among the best predictors of game outcome in soccer.