mlsanityData to 7 Oct 2026

Accelerating Skill Assessment in Chess: A Drift-Diffusion-Enhanced Elo Rating System

arXiv 2606.26267 ↗First posted 24 Jun 2026cs.AIcited by 0

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Abstract (arXiv)

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 https://github.com/Aquila-zhou1/DD-Elo .

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 31 references that are in the map. First posted on arXiv 24 Jun 2026.

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References · 3

  1. 2019Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descentcited by 31
  2. 2024Chess Rating Estimation from Moves and Clock Times Using a CNN-LSTMcited by 4
  3. 2024An Analysis of Elo Rating Systems via Markov Chainscited by 1

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