Automatically analyzing games is an important challenge for automated game design, general game playing, and cocreative game design tools. However, understanding the nature of an unseen game is extremely difficult due to the lack of a priori design knowledge and heuristics. In this article, we formally define hyperstate space graphs , a compressed form of state-space graphs, which can be constructed without any prior design knowledge about a game. We show how hyperstate space graphs produce compact representations of games, which closely relate to the heuristics designed by hand for search-based artificial intelligence (AI) agents; we show how hyperstate space graphs relate to modern ideas about game design; we report on exploratory uses of hyperstate space graphs as an analytical tool, and we point toward future applications for hyperstates across game AI research.