Cinder: A fast and fair matchmaking system
Saurav Pal

TL;DR
Cinder is a two-stage matchmaking system that quickly filters and precisely evaluates lobby fairness using advanced similarity and distribution metrics to improve match quality in multiplayer games.
Contribution
The paper introduces Cinder, a novel matchmaking system combining rapid filtering with detailed fairness assessment using non-linear skill buckets and Kantorovich distance.
Findings
Analyzed 140 million simulated lobby pairings for system validation.
Demonstrated improved fairness and speed over traditional methods.
Provided a robust foundation for setting fair matchmaking thresholds.
Abstract
A fair and fast matchmaking system is an important component of modern multiplayer online games, directly impacting player retention and satisfaction. However, creating fair matches between lobbies (pre-made teams) of heterogeneous skill levels presents a significant challenge. Matching based simply on average team skill metrics, such as mean or median rating or rank, often results in unbalanced and one-sided games, particularly when skill distributions are wide or skewed. This paper introduces Cinder, a two-stage matchmaking system designed to provide fast and fair matches. Cinder first employs a rapid preliminary filter by comparing the "non-outlier" skill range of lobbies using the Ruzicka similarity index. Lobbies that pass this initial check are then evaluated using a more precise fairness metric. This second stage involves mapping player ranks to a non-linear set of skill buckets,…
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Taxonomy
TopicsArtificial Intelligence in Games · Digital Games and Media · Peer-to-Peer Network Technologies
