League of Legends: Uma aplicação de algoritmos de classificação para prever a vitória em partidas competitivas

Abstract

The e-sport scenario has been consolidating more and more nowadays. League of Legends is one of the most popular sports in the category due to its popularity and the structure of its competitive scenario consisting of regional and international tournaments. The present monography, had the finality to predict the result of competitive matches using information about games played between the years 2016 and 2020 in the most well-known regional and international leagues, such as the Brazilian League of Legends Championship and League of Legends Pro League. We used four different approaches to train our models based on different variable categories. First, we used all economic variables registered. Secondly, we considered variables that change only from the beginning to the end of the game and do not suffer interference before and after the game. Then, we used economic variables registered in the first 10 and 15 minutes of each match. The accuracy of classifiers such as K-Nearest Neighbors, Random Forest, and Decision Tree varied from 68.33% to 85.17%, depending on which variables were used to train the models.