Skill Depth in Tabletop Board Games

There are well-established methods for rating the relative skill of players such as Elo or TrueSkill ratings. This is not the case for rating games by their relative difficulty, or the level of ‘skill’ required to play them well. Previous work has proposed skill-traces as an answer to this question, which use data from games played between agents with progressively higher computational budgets to estimate the difficulty of the game (or skill-depth). We try to improve on previous work by expanding the algorithmic space considered and that this can radically change the ratings of some games. We then propose a new parameterised model for the level of skill a game requires and test this on a suite of multiplayer tabletop board games, concluding that the parameters can be usefully interpreted and provide a slightly better fit to human-estimates.