Evaluating Team Skill Aggregation in Online Competitive Games
Arman Dehpanah, Muheeb Faizan Ghori, Jonathan Gemmell, Bamshad, Mobasher

TL;DR
This study evaluates methods for aggregating individual player skills into team skill ratings in online competitive games, finding that the MAX method often best predicts team performance across various rating systems and datasets.
Contribution
The paper introduces two new team skill aggregation methods and compares them with a standard approach across multiple rating systems and real-world datasets.
Findings
MAX method outperforms other aggregation methods in most cases
Team performance is best predicted by the most skilled member
Results suggest need for more comprehensive team performance metrics
Abstract
One of the main goals of online competitive games is increasing player engagement by ensuring fair matches. These games use rating systems for creating balanced match-ups. Rating systems leverage statistical estimation to rate players' skills and use skill ratings to predict rank before matching players. Skill ratings of individual players can be aggregated to compute the skill level of a team. While research often aims to improve the accuracy of skill estimation and fairness of match-ups, less attention has been given to how the skill level of a team is calculated from the skill level of its members. In this paper, we propose two new aggregation methods and compare them with a standard approach extensively used in the research literature. We present an exhaustive analysis of the impact of these methods on the predictive performance of rating systems. We perform our experiments using…
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Taxonomy
TopicsSports Analytics and Performance · Digital Games and Media · Gambling Behavior and Treatments
