Accelerating Skill Assessment in Chess: A Drift-Diffusion-Enhanced Elo Rating System
Researchers propose DD-Elo, a skill assessment framework using drift diffusion model and move-level data, achieving faster adaptation than Elo while maintaining bounded deviation.
[2606.26267] Accelerating Skill Assessment in Chess: A Drift-Diffusion-Enhanced Elo Rating System
[Submitted on 24 Jun 2026]
Title:Accelerating Skill Assessment in Chess: A Drift-Diffusion-Enhanced Elo Rating System
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Abstract:Rating systems such as Elo serve as the gold standard for matchmaking in competitive chess. However, they inherently suffer from response lag due to their exclusive reliance on match outcomes, neglecting the granular quality of gameplay. Nevertheless, incorporating move-by-move information into rating adjustments presents a significant challenge given the substantial noise and the vastness of the game-state space. To address this, we propose the Drift-Diffusion-Enhanced Elo Rating System (DD-Elo), a novel skill assessment framework inspired by the drift diffusion model (DDM) from cognitive neuroscience. By modeling skill expression as a decision-making process, our model integrates move-level data to capture rapid skill fluctuations. We provide a rigorous mathematical derivation proving that DD-Elo maintains a bounded deviation from the traditional Elo system, ensuring theoretical alignment. Extensive experiments demonstrate that DD-Elo adapts to skill changes faster than Elo. Our findings suggest that DD-Elo offers an explainable, highly responsive, and backward-compatible solution for chess rating ecosystems. The implementation code is publicly available at this https URL .
Comments: Accepted at the IEEE Conference on Games (IEEE CoG) 2026
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.26267 [cs.AI]
(or arXiv:2606.26267v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.26267
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhizheng Fu [view email] [v1] Wed, 24 Jun 2026 18:08:45 UTC (2,247 KB)
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