Fewer Draws, More Fun: Searching for Unbalanced Positions in Chess

Draw outcomes dominate both correspondence chess tournaments and competitions among top computer chess engines, diminishing their competitive and entertainment value. This study addresses the issue of draw dominance in computer chess, with direct implications for correspondence play. We investigate the problem of identifying alternative starting positions that lead to unbalanced games, where outcomes are less predictable even for state-of-the-art chess engines. Our approach focuses on searching, selecting, and evaluating positions —those with a 75% winning probability—as a means to increase decisive results. By analyzing these positions through extensive engine-based simulations, we aim to not only reduce draw rates but also introduce a framework for ranking and measuring the strength of the strongest chess engines.