## 4 Results and Discussion
### Variable Importance Groups  
To delve deeper into the relative significance of pre-game and in-game features within a unified model, feature importance differences between the pre-game and in-game variables were determined. Feature importance is quantified on the basis of the average  
Figure 7: Comparison of accuracy and AUC for each model.  
decrease in Gini impurity resulting from a predictor's involvement in node splits across all decision trees within the ensemble [74]. Predictors that exhibit higher scores are more important. According to Figure 8, in-game variables are generally more influential predictors, with four of the top five most pivotal predictors belonging to the in-game category, with 'gold' emerging as a particularly potent predictor. This observation is corroborated by existing research that acknowledges the disproportionately significant role of 'gold' in determining match outcomes [40]. Nevertheless, pre-game variables retain their relevance, with 'rankDiff' being identified as the second most influential predictor. Beyond the top six predictors, it is interesting to note that pre-game variables demonstrate superior strength compared with their in-game counterparts, underscoring that while in-game factors are predominantly more influential, pre-game factors still hold considerable predictive power.  
Comparing the best-performing model (LR) against itself and the second-best-performing model (RF) with only pre-game and in-game feature sets in Figures 9 and 10 below, there is a noticeable decline in performance across all the metrics when only pre-game features are employed. Specifically, the accuracy was reduced by approximately 20% in both the pre-game-only LR and RF models, as shown in Figure 9, relative to the accuracy of the combined LR model. An interesting observation from Figure 9 is the increase in recall for the RF model, which managed to correctly identify a higher percentage of wins, thus achieving a superior F1 score, albeit the LR model surpassed it in all other aspects. Consequently, it is evident that predictor sets exclusively based on pre-game variables are suboptimal compared with those that amalgamate both pre-game and in-game variables for this feature set.  
Figure 8: Variable importance by predictor type—scaled to 100 maximum.  
Compared with those based solely on pre-game variables, models utilizing only in-game variables demonstrated superior performance, which aligns with expectations due to their elevated variable importance. This finding agrees with the literature [24; 41]. Specifically, the in-game RF model exceeded the performance of the in-game LR model across all metrics except for the area under the curve (AUC) in Figure 10, implying that the combined LR model attributed more significance to pre-game factors. Nonetheless, both in-game-focused models outperformed the LR model, which integrated both the pre-game and in-game variables. This underscores the importance of pre-game factors, as  
Figure 10: The base LR model was compared with the in-game (IG) only LR and RF models.  
Figure 9: The base LR model was compared with the pre-game (PG) only LR and RF models.  
the comprehensive LR model achieves better results than the combined RF model does, reinforcing the notion that pre-game variables, though not solely determinative, contribute meaningfully to prediction accuracy.