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http://dx.doi.org/10.7583/JKGS.2022.22.1.33

Improvement of online game matchmaking using machine learning  

Kim, Yongwoo (Department of Technology Management, Hanyang University)
Kim, Young‐Min (Department of Technology Management, Hanyang University)
Publication Information
Journal of Korea Game Society / v.22, no.1, 2022 , pp. 33-42 More about this Journal
Abstract
In online games, interactions with other players may threaten player satisfaction. Therefore, matching players of similar skill levels is important for players' experience. However, with the current evaluation method which is only based on the final result of the game, newbies and returning players are difficult to be matched properly. In this study, we propose a method to improve matchmaking quality. We build machine learning models to predict the MMR of players and derive the basis of the prediction. The error of the best model was 40.4% of the average MMR range, confirming that the proposed method can immediately place players in a league close to their current skill level. In addition, the basis of predictions may help players to accept the result.
Keywords
Machine Learning; StarCraft; Matchmaking;
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Times Cited By KSCI : 1  (Citation Analysis)
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