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arxiv: 2509.04691 · v3 · pith:ZQS3DSFNnew · submitted 2025-09-04 · 📊 stat.AP

Inferring Piece Value in Chess and Chess Variants

classification 📊 stat.AP
keywords chessvaluepiecefindvaluesantichessatomicpieces
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We use logistic regression to estimate the value of the pieces in standard chess and several chess variants, namely Chess 960, Atomic chess, Antichess, and Horde chess. We perform our regressions on several years of data from Lichess, the free and open-source internet chess server. We use the published player ratings to control for the confounding effect of differential player skill. We adjust for the attenuation bias in regressions due to the noise in observed ratings. We find that major piece values, relative to the value of a pawn, are fairly consistent with historical valuation systems. However we find slightly higher value to bishops than knights. We find that piece values are smaller, in absolute value, in Atomic and Antichess than standard chess. We also present approximate values of the pieces to equalize odds when players of varying skill face off. We briefly consider self-play experiments using the Stockfish engine, which give a contrasting view of piece value.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. PAWN: Piece Value Analysis with Neural Networks

    cs.LG 2026-04 unverdicted novelty 5.0

    A CNN autoencoder that encodes the entire chessboard state improves MLP prediction of relative piece values by 16% MAE reduction to roughly 0.65 pawns using 12 million Stockfish-labeled positions from grandmaster games.