## 4 Results and Discussion
### Critical Analysis and Discussion
#### 4.4.4 Limitations and Future Research  
This research focused exclusively on data from European players to streamline the data-gathering process, given that distinct geographic areas have separate API endpoints. [PERSON] [37] reported that variations in styles across regions diminish the predictive ability when findings are applied universally. Importantly, however, [PERSON]'s analysis was based on professional esports competitions, whereas gameplay at competitive levels may adhere to tacit strategies that are not prevalent in lower tiers. This observation does not negate the possibility of regional distinctions among lower-ranking players, suggesting an area ripe for future investigation. Generalizability of the model in this study on data from other regions would be a useful research avenue to assess how feature significance varies by geography.  
An additional potential limitation identified in this study pertains to the method of data consolidation at the match level, which involves averaging the metrics of individual players within a team to create a team-wide feature set, aggregating individual contributions within the team. [PERSON] et al. [55] demonstrated the variable impact of players occupying distinct positions on the outcome of a match. Specifically, their findings suggest that the performance of individuals in the 'Mid' lane offers the most reliable forecast of match results. However, the field presents a lack of consensus on this matter, as evidenced by [PERSON]'s [81] research, which attributes the greatest influence to the 'Carry' role. This discrepancy may stem from the utilization of differing sets of features in their analyses or the temporal gap between the studies (2018 vs. 2016), a period during which modifications by the developers could have altered the relative influence of each role [30]. More recent research by [PERSON] et al. [82] revealed no difference in importance between various roles when attempting to optimize models for individual player contributions.  
Finally, while the sensitivity analysis of streak suggested imputation had a low impact (due to streak's relative unimportance overall), future studies should apply the data engineering framework from this study over a longer timeframe to account for the Riot API rate limitations while still collecting sufficient, real data on player streaks--and explore the various methods of construction for this feature to accurately capture its impact.  
In general, there are opportunities to increase the scope of studies on data available via the Riot API through longer-term data collection, allowing a much richer dataset. This would allow more nuanced exploration of inter-feature relationships (especially in-game features), which more recent studies are attempting to do [78], and this would also open the door to applying more sophisticated machine learning methods, such as those demonstrated in [83] (ensemble-of-ensembles models, pipeline-based feature selection and hyperparameter optimization), methods that may otherwise be computationally efficient and risk overfitting on small datasets with fewer features.