###### Abstract  
Over the last decade, advancements in machine learning and easier model deployment have led to increased commercial applications. One such use case is esports, where machine learning (ML) is used to understand predictors of success. League of Legends, one of the most popular esports, has been a particular academic focus. Investigations into League are divided into two areas: qualitative analyses of factors such as perseverance and group dynamics and quantitative research to create models to predict match outcomes via either pre-game player information or in-game match data. Few studies have integrated both pre-game and in-game data to improve modeling, often using datasets that may not represent the broader player community. This study investigates the factors influencing the accuracy of match prediction models in League of Legends. Evaluating the effects of training on data that are representative of the actual player on the basis of accuracy and determining whether models that amalgamate pre-game and in-game features yield superior results. By utilizing a dataset derived from the Riot API, this research work introduces a novel \"streak\" feature and constructs models using logistic regression, random forest, C5.0 Gradient Boost and XGBoost, evaluating model performance against recent literature. The results indicate that employing a dataset that more accurately reflects the general player population leads to a slight decrease in the efficacy of the models compared with those using professional datasets only; however, the models demonstrate potential for greater generalizability across a wider range of ranks. The models that incorporate both pre-game and in-game data outperformed most existing studies that focus solely on one type of data, achieving a peak accuracy of 76.8% for the best-performing model. These findings guide future work on feature engineering via the Riot API and model application for broader player populations in esports. Additionally, these insights can be applied to build and improve tools that provide real-time predictions of match results. p 2025 15 241 20  
growth rate of 13% [9], has seen a shift towards competitive, online, live-service games that have evolved over time. This shift has contributed to the establishment of professional esports communities around the most popular leading competitive titles [10]. One of these titles is League of Legends, a large online game attracting 150 million players [11], generating USD 1.75 bn in 2020, with an eSports prize pool of USD 2 m in recent years [12]. As such, there is an increasing focus on applying machines to decipher drivers of team victories and predict game outcomes. The Riot Games public API also provides a large set of historical training data on matches and individual players [13], further enabling research on the applications of ML in this space. Two distinct types of studies dominate this research domain. The first type encompasses experimental research that examines external determinants linked to triumph or aspects that are not inherently documented by the API's data. This includes examining the impact of team communication and cooperation [14], the direction of attention of players at differing skill levels [15], or the influence of winning/losing streaks and mental tilt [16]. Owing to their experimental nature, sample sizes are usually small and are focused on important factors in the experiment. The second type of studies are large-scale studies attempting to apply quantitative techniques to high-volume, high-dimensional data extracted from the Riot API or professional match datasets. These latter studies can be further categorized as prediction using 'pre-game' features--the characteristics of individual players prior to a match, such as experience [17] and role [18]--and 'in-game' match event data, such as resources acquired, opponents eliminated, or objectives captured [19; 20; 21] at various points in the match.