Determining the state-space complexity of games like Shogi—the Japanese variant of chess—has long challenged computer scientists and game theorists. Previous combinatorial approaches to estimate Shogi's computational complexity produced wildly divergent results, ranging from 10^64 to 10^69 possible game states, a gap spanning five orders of magnitude. This uncertainty has made it difficult for researchers to properly contextualize Shogi's difficulty relative to other games and to design appropriate AI systems for mastering it.