Research Article
Statistical Models for Predicting Results in Professional League of Legends
@INPROCEEDINGS{10.1007/978-3-030-95531-1_10, author={Robbie Jadowski and Stuart Cunningham}, title={Statistical Models for Predicting Results in Professional League of Legends}, proceedings={ArtsIT, Interactivity and Game Creation. Creative Heritage. New Perspectives from Media Arts and Artificial Intelligence. 10th EAI International Conference, ArtsIT 2021, Virtual Event, December 2-3, 2021, Proceedings}, proceedings_a={ARTSIT}, year={2022}, month={2}, keywords={Esports League of Legends Machine learning Regression}, doi={10.1007/978-3-030-95531-1_10} }- Robbie Jadowski
Stuart Cunningham
Year: 2022
Statistical Models for Predicting Results in Professional League of Legends
ARTSIT
Springer
DOI: 10.1007/978-3-030-95531-1_10
Abstract
The esports industry has seen enormous growth in popularity. With increased viewership and revenue, further investment has been made to improve professional players’ competitive strength. The modern esports team is a hierarchical business fuelled by investors and sponsorship. This paper is focused on the professional competitions in League of Legends esports. In existing real-world sports such as football or baseball, there is great attention paid to statistic driven analysis of the competition, and these stats are used to quantify player and team performance. These statistics hold significant value for competitive improvement, the gambling industry, and market influence within the esports industry.
This paper presents an analysis of data and metrics gathered from professional games during 2020 in several League of Legends international competitions. The objective was to build a predictive model through the combination of existing data analysis and machine learning that can rate team and player performance. The best performing model was able to correctly predict 67% of 306 games. Results indicate that while it is possible to predict the outcome of a competitive League of Legends game, to do so with a higher degree of accuracy would require substantially more data and contextual information.
