Identifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis
Chiu-Chou Lin, Yu-Wei Shih, Kuei-Ting Kuo, Yu-Cheng Chen, Chien-Hua, Chen, Wei-Chen Chiu, I-Chen Wu

TL;DR
This paper introduces new measures for quantifying balance in PvP games by analyzing team compositions and their counter relationships, using efficient models validated across multiple popular online games.
Contribution
It develops a novel framework employing vector quantization and strength rating approximations to identify and cluster team counters, improving balance analysis efficiency.
Findings
Accurately models strength relations in PvP games
Reduces computational complexity of balance analysis
Validated across multiple popular online games
Abstract
How can balance be quantified in game settings? This question is crucial for game designers, especially in player-versus-player (PvP) games, where analyzing the strength relations among predefined team compositions-such as hero combinations in multiplayer online battle arena (MOBA) games or decks in card games-is essential for enhancing gameplay and achieving balance. We have developed two advanced measures that extend beyond the simplistic win rate to quantify balance in zero-sum competitive scenarios. These measures are derived from win value estimations, which employ strength rating approximations via the Bradley-Terry model and counter relationship approximations via vector quantization, significantly reducing the computational complexity associated with traditional win value estimations. Throughout the learning process of these models, we identify useful categories of compositions…
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Taxonomy
TopicsArtificial Intelligence in Games · Sports Analytics and Performance · Multi-Agent Systems and Negotiation
