Accelerating Skill Assessment in Chess: A Drift-Diffusion-Enhanced Elo Rating System
Quick Answer
This paper shows that The Drift-Diffusion-Enhanced Elo Rating System (DD-Elo) improves chess skill assessment by integrating move-level data, enabling faster adaptation to skill changes compared to traditional Elo.
Quick Take
The Drift-Diffusion-Enhanced Elo Rating System (DD-Elo) improves chess skill assessment by integrating move-level data, enabling faster adaptation to skill changes compared to traditional Elo. This model, grounded in cognitive neuroscience, maintains alignment with Elo while providing a more responsive and explainable rating mechanism. Implementation code is available on GitHub.
Key Points
- DD-Elo integrates move-by-move data for improved skill assessment.
- The model adapts to skill changes faster than traditional Elo ratings.
- DD-Elo maintains theoretical alignment with the original Elo system.
- Extensive experiments validate the effectiveness of DD-Elo.
- Implementation code is publicly available on GitHub.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 26267v1 Announce Type: new 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 https://github. com/Aquila-zhou1/DD-Elo.
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