## 3 Proposed Research Methodology
### Feature Engineering  
During the data extraction process, several player features were captured to characterize their in-game performance and pregame statistics and champion selection tendencies. These features included a player's champion experience, current ranking tier, and overall seasonal win-loss record. To assess the diversity of a player's champion pool, which can indicate adaptability [49], metrics such as champion points for the chosen character (\"championPoints\"), total champion master (\"totalMastery\"), and the number of unique champions played (\"nchamps\") were collected. Wins and losses were utilized to calculate the total number of games played and the win rate. However, because existing research  
Figure 3: Sampling logic diagram (the yellow text specifies the API endpoints used). Figure 2: Relational model of the Riot API endpoints used in the study. suggests that the overall win rate is not a strong predictor of future success [44], it was excluded from further analysis. A streak metric was computed, obtained by recursively querying past games for each player-match combination. This was calculated by querying the player_matches endpoint for the last 200 games an individual played, identifying the matches preceding the given match. The matches endpoint was then used to loop through these previous matches and calculate an integer value for the number of matches a player had won or lost in a row prior to the given match. A negative streak value signifies a series of recent losses, whereas a positive value indicates a winning streak. Due to rate limits on the API restricting the ability to arbitrarily query all player history for all matches, some streak data could not be computed--in instances where a player's current match was not in the last 200 matches they played. This was dealt with at the data cleaning stage, outlined below. Compared with pre-game player profile data, in-game predictors offer a richer feature space. The match timeline endpoint provides over 100 features capturing match events at 60 s intervals. However, prior research has highlighted the issue of high correlation within in-game feature sets [21], as many features exhibit a dependency or direct influence on each other. This was addressed by a correlation analysis postextraction, as detailed below. In-game predictors were categorized into player-specific and match-specific groups. Match-specific features included the team securing the first elimination and the first objective (tower), along with the outcome variable (win/loss). Player-specific predictors included gold acquired, damage dealt, and damage taken at both 10 and 15 min. These specific timestamps were chosen because of their prevalence in past studies [50] and because they represent relatively early phases in the game before a match outcome can be easily determined. To ensure data quality, several cleaning steps were implemented. Duplicate matches and instances of games ending prematurely (i.e., less than 15 min) through surrender were eliminated from the dataset. Additionally, any errors identified within tier or rank data were rectified. The prevalence of missing data points across features was calculated, as this phenomenon can introduce bias into machine learning models [51]. Most features contained less than 3% missing values, and these records were excluded. The'streak' feature contained approximately 20% missing values, which would have resulted in a sizable reduction in sample size if excluded. Multiple avenues of imputation were considered. Firstly, sampling from a normal distribution. The normality of the streak was assessed using a QQ plot (Figure 4a) and a D'Agostino test of normality. Streak proved to be significantly different from normal (\(p<2.2\times 10^{-16}\)), discontinuing this approach. Secondly, extrapolating streak from other feature values was considered. A collinearity analysis on the raw features (Figure 4b) showed streak has no strong correlation with other features that could be used to compute a statistically sound estimated value.