Action2Score: An Embedding Approach to Score Player Action

Junho Jang, Ji Young Woo, Huy Kang Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Multiplayer Online Battle Arena (MOBA) is one of the most successful game genres. MOBA games such as League of Legends have competitive environments where players race for their rank. In most MOBA games, a player's rank is determined by the match result (win or lose). It seems natural because of the nature of team play, but in some sense, it is unfair because the players who put a lot of effort lose their rank just in case of loss and some players even get free-ride on teammates' efforts in case of a win. To reduce the side-effects of the team-based ranking system and evaluate a player's performance impartially, we propose a novel embedding model that converts a player's actions into quantitative scores based on the actions' respective contribution to the team's victory. Our model is built using a sequence-based deep learning model with a novel loss function working on the team match. We showed that our model can evaluate a player's individual performance fairly and analyze the contributions of the player's respective actions.

Original languageEnglish
Article number220
JournalProceedings of the ACM on Human-Computer Interaction
Publication statusPublished - 2022 Oct 29

Bibliographical note

Funding Information:
This was supported by Korea University Grant.

Publisher Copyright:
© 2022 ACM.


  • deep learning
  • esports analysis
  • individual performance evaluation
  • player contribution
  • sequence model

ASJC Scopus subject areas

  • Social Sciences (miscellaneous)
  • Human-Computer Interaction
  • Computer Networks and Communications


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