Hodge, Victoria J. orcid.org/0000-0002-2469-0224, Devlin, Sam Michael orcid.org/0000-0002-7769-3090, Sephton, Nicholas John et al. (3 more authors) (2017) Win Prediction in Esports:Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games. [Preprint]
Abstract
Esports has emerged as a popular genre for players as well as spectators, supporting a global entertainment industry. Esports analytics has evolved to address the requirement for data-driven feedback, and is focused on cyber-athlete evaluation, strategy and prediction. Towards the latter, previous work has used match data from a variety of player ranks from hobbyist to professional players. However, professional players have been shown to behave differently than lower ranked players. Given the comparatively limited supply of professional data, a key question is thus whether mixed-rank match datasets can be used to create data-driven models which predict winners in professional matches and provide a simple in-game statistic for viewers and broadcasters. Here we show that, although there is a slightly reduced accuracy, mixed-rank datasets can be used to predict the outcome of professional matches, with suitably optimized configurations.
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York > Faculty of Arts and Humanities (York) > Theatre, Film, TV and Interactive Media (York) |
Funding Information: | Funder Grant number EPSRC EP/M023265/1 |
Depositing User: | Pure (York) |
Date Deposited: | 08 Jun 2023 23:14 |
Last Modified: | 08 Feb 2025 00:04 |
Status: | Published |
Publisher: | arXiv |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:200177 |