This study proposes a model using a Bayesian network to understand tactical behavior in Karate matches. The model is a probabihstic causal model consisting of the states of two competitors engaged in combat. Each state node of the model Outputs a probability distribution of the occurrence of offensive, defensive, and evaluative actions. Using the model, we also propose an analysis method of Karate tactics that can obtain the high likelihood processes for a competitor for the case in wliicli the competitor's offensive action ends in success. The extracted processes indicate competitor's tactical pattem. For an expernnent of the method, we collected action data from the Videos of Karate matches in which elite competitors had competed. and trained two eilte competitor's model. The method has extracted the elite competitors' tactical actions correctly using the trained models.
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