League of Legends: Real-Time Result Prediction

TL;DR

This study evaluates machine learning models, especially LightGBM, for real-time outcome prediction in League of Legends matches, achieving over 81% accuracy in mid-game stages.

Contribution

It introduces a novel application of machine learning to real-time LoL match outcome prediction, utilizing unpublished data and comparing multiple models.

Findings

LightGBM achieved 81.62% accuracy in 60-80% match stages.

Logistic Regression and Gradient Boosting performed well in early game stages.

Models demonstrated promising results across different match stages.

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ability to predict real-time results, considering different variables and stages of the match, we highlight the use of unpublished data as a fundamental part of this process.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 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.