## 4 Results and Discussion
### Effect of Streak  
In examining the importance of streaks, exponentiated coefficients and test statistics for the logistic regression model (Table 4) were computed to allow like-for-like comparison of the effect size of statistically significant features. It should be noted that the Odds Ratios listed in Table 4 show the effect on odds of losing (as winning was set as the reference level for this statistic). 'streakDiff' emerged as one of the few predictors identified as statistically significant, others being 'rankDiff' and 'goldAtFifteenDiff'. Its exponentiated coefficient indicated a marginally larger effect size than the other two coefficients did. However, it is important to note that the utility of statistical significance diminishes with increasing sample size because the relative portion of variance attributable to noise decreases, increasing the likelihood of identifying significant associations [75]. Streak was also the only feature used to contain imputed data. Consequently, additional evidence is necessary to substantiate these findings. Specifically, an expanded data engineering methodology over a longer timeframe that allows comprehensive collection of streak data would greatly benefit this space. Figure 8 above suggests that a streak is important in isolation as a pre-game predictor, being the second most important pre-game predictor behind rankDiff; however, overall, the difference in ranks between players is a more important pre-game predictor, and in-game predictors overall carry far more weight in determining match outcomes. This is further corroborated when the variable importance in the XGBoost (the third most accurate) model is examined, as shown in Figure 11 below. Streak is considered the 6 th most important predictor only, far less important than rankDiff and goldAtFifteenDiff at the top. This agrees with the findings from the logistic regression coefficients as well (since they show the impact on the odds ratio for losing streaks and therefore have the lowest effect size of the three). In practical terms within a game context, we can interpret this as for each unit increase in \"streakDiff\", the odds of Team 1 losing are multiplied by 0.880 (or a decrease of 12%), all else being equal. This is a very modest effect size in real terms compared to the far larger impact of unit increases in rank (a unit increase decreases odds of losing by 50%) and goldAtFifteen (a unit increase decreases odds of losing by 83%). To analyze model sensitivity to streak, comparator models with streakDiff removed and streakDiff imputed using median values (rather than the mean) were created, again only for LR and RF as the two best-performing models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Predictor** & **Odds Ratio** & \(\boldsymbol{p}\)**Value** \\ \hline rankDiff & 0.513 & 2.76 \(\times\) 10\({}^{-25}\) \\ streakDiff & 0.880 & 1.95 \(\times\) 10\({}^{-2}\) \\ goldAtFifteenDiff & 0.176 & 2.79 \(\times\) 10\({}^{-28}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Logistic regression parameter statistics. Figure 12a shows a sensitivity analysis for streak imputation by comparing performance between LR and RF models trained using the base (mean-imputed) streak feature against the same models trained with the median-imputed streak feature, all else being equal. Accuracy showed no difference in performance between the two imputation methodologies, and AUC only showed a difference of 0.001 for the RF model. AUC, specificity, precision and recall are excluded for visual clarity, but they all also showed no difference between the two imputation methods and, importantly, did not influence the most performant model either positively or negatively. Figure 12b shows model performance with the streak removed, comparing both LR and RF versions to the base LR combined model. Eliminating'streak' from the features seemingly exerts minimal influence.