SILVA JUNIOR, J. B.; SILVA JUNIOR, Jailson Barros da.
Abstract:
This paper presents a study on the prediction of outcomes in matches of the electronic game League
of Legends (LoL) using machine learning techniques. With the aim of exploring the ability to predict
real-time results, considering different variables and stages of the match, we highlight the use of
unpublished data as a fundamental part of this process. With the increasing popularity of LoL and the
emergence of tournaments, betting related to the game has also emerged, making the investigation
in this area even more relevant. A variety of models were evaluated and the results were
encouraging. A model based on Random Forest showed the best performance, achieving an average
accuracy of 81.57% in intermediate stages of the match when the percentage of elapsed time was
between 60% and 80%. On the other hand, the Logistic Regression and Gradient Boosting models
proved to be more effective in early stages of the game, with promising results. This study contributes
to the field of machine learning applied to electronic games, providing valuable insights into real-time
prediction in League of Legends. The results obtained may be relevant for both players seeking to
improve their strategies and the betting industry related to the game.