## 4 Results and Discussion
### Critical Analysis and Discussion  
#### 4.4.1 Combined Model Performance Against Existing Literature  
Figure 13 shows that the combined models outperformed a sample of pre-game-only/in-game-only studies in terms of accuracy. [PERSON] [41] has the highest accuracy, although this is to be expected as they trained and tested exclusively on professional match data, while this study's models are derived from a dataset containing a wider range of ranks and skills.  
Even the weakest performer, the Gradient Boost combined model, exhibits superior performance in comparison with most other methods, yet it does not surpass the results of [PERSON]'s pre-game study [17], an outcome that can likely be attributed to differences in methodology. [PERSON]'s study employs a bespoke-architected Deep Neural Network, identifying a player's win rate on a chosen champion as a critical feature, a variable not accounted for in the present analysis owing to it not being available as a direct endpoint in the API. Engineering the same feature would have required additional API calls that would have limited the feature space elsewhere or reduced the sample size. More recent studies have attempted to branch out from [PERSON]'s approach, most notably [PERSON] [80], who enhanced the pre-game feature space from [PERSON]'s study with additional information on individual players' win rates in their specific roles on the team, achieving a 97% accuracy with logistic regression. [PERSON] [79] also highlights a pre-game-focused approach, modeling specifically on the match a player has between their chosen character and that character's suitability for their role, achieving a 75.32% accuracy based on this feature alone. In contrast, [PERSON] [78] focuses mainly on in-game features and achieves an accuracy of 73.2%. This variability highlights the untapped potential in both pre-game and in-game feature spaces, outlining how advanced feature engineering combined with higher volume data (to enable more sophisticated modeling approaches) is crucial to unlocking that potential. The limitations inherent to the feature space in this study undoubtedly affect accuracy, although the findings still show the importance of combining both pre-game and in-game features for prediction.  
Future work should conduct this comparison with an expanded set of features. In summary, the integration of pre-game and in-game predictors yields a noticeable enhancement  
Figure 13: Performance comparison of this study combining pre-game/in-game models with existing methods from [PERSON] [41], [PERSON] [17], [PERSON] [24], [PERSON] [77], [PERSON] [78], [PERSON] [79].  
in predictive accuracy, even when trained on (and predicting across) a diverse spectrum of player abilities. While some loss in accuracy is seen compared with papers such as [PERSON] et al. [41], this is to be expected due to the representative nature of this study's dataset, which should, in theory, result in more generalizable predictions across the player base and not be specific to professional players. The accuracy and generalizability of models trained on specific ranks but tested against different ranks constitute a future research avenue that could highlight important differences between players at different skill levels and should be explored.