  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unpublished data as a fundamental part of this process.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 LightGBM showed the best performance, achieving an average accuracy of
81.62\% 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.
