Machine Learning Methods for Predicting <i>League of Legends</i> Game Outcome
Juan Agustín Hitar-García, Laura Morán‐Fernández, Verónica Bolón‐Canedo
Abstract
The video game <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">League of Legends</i> has several professional leagues and tournaments that offer prizes reaching several million dollars, making it one of the most followed games in the Esports scene. This article addresses the prediction of the winning team in professional matches of the game, using only pregame data. We propose to improve the accuracy of the models trained with the features offered by the game application programming interface (API). To this end, new features are built to collect interesting information, such as the skills of a player handling a certain champion, the synergies between players of the same team or the ability of a player to beat another player. Then, we perform feature selection and train different classification algorithms aiming at obtaining the best model. Experimental results show classification accuracy above 0.70, which is comparable to the results of other proposals presented in the literature, but with the added benefit of using few samples and not requiring the use of external sources to collect additional statistics.