Action2Score: An Embedding Approach To Score Player Action
Junho Jang, Ji Young Woo, Huy Kang Kim

TL;DR
This paper introduces Action2Score, a deep learning embedding model that quantifies individual player actions in MOBA games to fairly evaluate performance and contribution, independent of match outcomes.
Contribution
It presents a novel sequence-based deep learning approach with a custom loss function to accurately score player actions based on their impact on team success.
Findings
The model effectively evaluates individual player performance.
It accurately correlates actions with team victory.
The approach reduces bias in player ranking systems.
Abstract
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.…
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Taxonomy
TopicsArtificial Intelligence in Games · Sports Analytics and Performance · Video Analysis and Summarization
MethodsGated Recurrent Unit
