###### Abstract
The source for these studies is a mixture of public game data from the API and esports datasets from sources such as Kaggle (which are also partially derived from the API) [22]. These large-scale quantitative studies tend to focus primarily on exploring game-specific factors influencing match success to build better models for predicting victory in games. Studies focusing on pre-game features in isolation are less common, although they have shown reasonable accuracy when focusing on factors such as player experience [17]. Typically, studies combine pre-game and in-game factors by extracting in-game data from an arbitrary time point in matches (e.g., [23]), taking data from the end of a match [24], or building real-time prediction models [25]. This study attempts to fill a specific set of gaps in the current literature, which are the lack of studies that combine pre-game/in-game factors together while exploring their relative importance on a representatively sampled dataset (i.e., ensuring a split of skill ranks representative of the wider player base), which also attempt to include some element of engineering features that can act as proxies for factors deemed important in offline qualitative studies (in this case, the effect of winning/losing streaks) [26]). This study aims to explore whether representative datasets on match performance can be used to build better predictive models of match outcomes. The objectives of this study are to engineer a dataset using the Riot API, which is representative of the player base and contains data across all ranks of players, and to train a suite of models (logistic regression, random forest, Gradient Boost and XGBoost) to predict the outcome of the match, optimizing for accuracy. A new feature from the API,'streak', will be engineered to quantify a player's current win/loss streak going into the game and assess the importance of this predictor in determining the match outcome. The methodology is focused on improving prediction accuracy for matches in the general player population. The hypothesis is that a combined model has superior predictive performance compared with individual pre-game/in-game models and that a streak is an important pre-game predictor of match outcome, as offline experimental studies have shown it to be an important psychological factor in success [26]. The significance of these contributions is multifaceted. In a world where the competitive landscape is increasingly fierce and many games are being developed as live services [27], ensuring player contentment is essential for their continued engagement. It is vital to offer an equitable and well-balanced gaming experience to all participants to maintain their involvement [28]. The proposed method has the following contributions:  
* The findings from this research have the potential to enhance matchmaking algorithms, which could positively affect players' perceptions of match fairness [26], thereby increasing both player retention and revenue. * Furthermore, the broader infrastructure that contributes to the game's success, including esports, benefits from the incorporation of machine learning (ML) techniques. These techniques could facilitate player development, enable talent scouts to identify emerging players more effectively, and aid coaches in formulating strategic plans, similar to their applications in traditional sports [5]. In particular, the influence of winning/losing streaks on match prediction is statistically significant (\(p<0.05\)), but its impact on prediction is relatively small. * This study provides a data engineering framework to allow easy representative sampling from the Riot API, which can be extended to incorporate further features easily. * It explores the relative importance of pre-game vs. in-game in the context of predicting match outcomes while also ensuring that the results are generalizable across all different skill levels in the player base, as opposed to professional matches, which are the focus of much of the literature.