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[2602.17015] Cinder: A fast and fair matchmaking system
Summary
The paper introduces Cinder, a two-stage matchmaking system designed to enhance fairness and speed in multiplayer online games by utilizing advanced skill metrics.
Why It Matters
As online gaming continues to grow, ensuring fair matchmaking is crucial for player satisfaction and retention. Cinder addresses the common pitfalls of traditional matchmaking systems, which often lead to unbalanced games, thus providing a solution that could significantly improve player experiences.
Key Takeaways
- Cinder employs a two-stage matchmaking process for improved fairness.
- The system uses the Ruzicka similarity index for initial filtering of lobbies.
- A non-linear skill bucket mapping enhances granularity in matchmaking.
- Fairness is quantified using a unique 'Sanction Score' based on Kantorovich distance.
- Analysis of 140 million simulated lobby pairings demonstrates the system's effectiveness.
Computer Science > Artificial Intelligence arXiv:2602.17015 (cs) [Submitted on 4 Nov 2025] Title:Cinder: A fast and fair matchmaking system Authors:Saurav Pal View a PDF of the paper titled Cinder: A fast and fair matchmaking system, by Saurav Pal View PDF HTML (experimental) 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, generated from an inverted normal distribution, to provide higher granularity at average skill levels. The fairness of a potential match is then quantified using the Kantorovich distance on the lobbies' sorted bucket indices, producing a "Sanction Score." We demonstrate the system's viability by analyzing the ...