  Recent advances in game informatics have enabled us to find strong strategies
across a diverse range of games. However, these strategies are usually
difficult for humans to interpret. On the other hand, research in Explainable
Artificial Intelligence (XAI) has seen a notable surge in scholarly activity.
Interpreting strong or near-optimal strategies or the game itself can provide
valuable insights. In this paper, we propose two methods to quantify the
feature importance using Shapley values: one for the game itself and another
for individual AIs. We empirically show that our proposed methods yield
intuitive explanations that resonate with and augment human understanding.
