## 3 Proposed Research Methodology
### Feature Engineering
As such, mean value imputation for streak was utilized as the base because of its low probability of biasing the feature space [52]; however, in Section 4.3, we also performed sensitivity analysis by comparing the effect on model performance when using median imputation instead. Both mean and median values were calculated separately per response class (mean: 0.046--win, 0.029--loss; median: 1--win, \(-1\)--loss). Figure 4: _Cont_. Sample representativeness was verified across ranks by counting matches in each tier (Table 1) and ensuring that class balance was appropriate (Table 2). The rank profile is similar to published statistics [53], making the sample reasonably representative. The prediction labels were also reasonably balanced (Table 2), so rebalancing techniques such as oversampling were not necessary [54]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Records** & **\%** \\ \hline Iron & 2118 & 7\% \\ Bronze & 5505 & 18\% \\ Silver & 5797 & 19\% \\ Gold & 5569 & 18\% \\ Platinum & 5643 & 18\% \\ Diamond & 4905 & 16\% \\ Master & 980 & 3\% \\ Grandmaster & 17 & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample representativeness. Figure 4: (a) QQ plot for non-missing streak data. (b) Collinearity analysis of raw features. (c) Collinearity plot of the final aggregated dataset. In this research, emphasis is placed on assessing the performance of teams as a whole rather than individual players, given that the outcome of a match ('T1_win') is applicable only to team-based results. To facilitate this analysis, player-specific features were aggregated and averaged per team to represent each team's overall average features. To further streamline the feature set, Team 1 was designated the reference point, and a 'difference' metric was created for each numeric predictor by calculating the disparity between the average values of Teams 1 and 2 (T1-T2) features. These newly crafted 'Diff' predictors constituted the primary set of features employed in the study. The refined dataset encompasses data from 3054 matches, featuring 15 predictors--7 pre-game and 8 in-game--with a single binary label to predict (T1_win). While an initial collinearity analysis was performed on the raw features (i.e., non-\"diff\"-ed), the analysis was not useful as it could not show any negative correlation--since features such as damage dealt, gold taken, and damage taken in their raw forms are always positive for both teams. Collinearity analysis of the diff predictors proved more insightful, revealing that greater damageTakenDiff (indicating that Team 1 sustained more damage) is inversely associated with goldDiffs and damageDealtDiffs. This finding implies that teams failing to effectively manage their health are at a disadvantage in terms of gathering resources and launching counterattacks. The analysis also revealed a slight negative correlation between rankDiff and damageTaken, suggesting that teams with higher rankings exhibit better control over their health status. In a game context, this means a higher-ranked team will on average be able to both deal more damage over time to their opponents while negating counterattacks or damage to themselves (i.e., they do not 'trade' blows). The final engineered feature set (Table 3) contains seven pre-game predictors and eight in-game predictors along with the binary label of whether Team 1 won.