This paper proposes a new evaluation index for evaluating human fallibility in shogi. By analyzing the differences between the decision-making processes of AI and humans, we found that while humans rely on intuition and decisions made within a limited timeframe, AI often makes different decisions because it carries out exhaustive searches. In this study, we developed a new evaluation index that extracts features prone to human error using a policy network trained based on professional game records. This evaluation model uses logistic regression to predict the probability of making a mistake in the endgame. We have shown that the proposed indicator is effective through testing with test data. In the future, we plan to construct similar evaluation indicators for the opening and middle game phases and verify their effectiveness.