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

arXiv: https://arxiv.org/abs/2606.26267 · HTML: https://mlsanity.com/paper/2606.26267/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 24 Jun 2026.

## Builds on (most cited first, 3 of 3)

- [Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent](https://mlsanity.com/paper/1903.05614/) (2019)
- [Chess Rating Estimation from Moves and Clock Times Using a CNN-LSTM](https://mlsanity.com/paper/2409.11506/) (2024)
- [An Analysis of Elo Rating Systems via Markov Chains](https://mlsanity.com/paper/2406.05869/) (2024)

## 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 .