Transformer models have demonstrated impressive capabilities when trained atscale, excelling at difficult cognitive tasks requiring complex reasoning andrational decision-making. In this paper, we explore the application oftransformer models to chess, focusing on the critical role of the positionencoding within the attention mechanism. We show that in chess, transformersendowed with a sufficiently versatile position encoding can match existingchess-playing models at a fraction of the computational cost. Our architecturesignificantly outperforms AlphaZero at 8x fewer FLOPS and matches priorgrandmaster-level transformer-based agents at 30x fewer FLOPS.