# League of Legends: Real-Time Result Prediction

arXiv: https://arxiv.org/abs/2309.02449 · HTML: https://mlsanity.com/paper/2309.02449/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 2 Sep 2023.

## Builds on (most cited first, 3 of 3)

- [Using Machine Learning to Predict Game Outcomes Based on Player-Champion Experience in League of Legends](https://mlsanity.com/paper/2108.02799/) (2021)
- [Machine learning models for DOTA 2 outcomes prediction](https://mlsanity.com/paper/2106.01782/) (2021)
- [The Evolution of eSports: An Analysis of its origin and a look at its prospective future growth as enhanced by Information Technology Management tools](https://mlsanity.com/paper/1509.08795/) (2015)

## Built on by (most cited first, 2 of 2)

- [Voice Communication Analysis in Esports](https://mlsanity.com/paper/2411.19793/) (2024)
- [Round Outcome Prediction in VALORANT Using Tactical Features from Video Analysis](https://mlsanity.com/paper/2510.17199/) (2025)

## Abstract (arXiv)

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ability to predict real-time results, considering different variables and stages of the match, we highlight the use of unpublished data as a fundamental part of this process.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