For several decades now, games have become an important research ground for artificial intelligence. In addition to often present useful and complex problems, they also provide a clear framework thanks to their rules, sometimes numerous. In this article, we explore a very difficult two-players board game named Blood Bowl. This game allows the players to perform many different actions, which depend for a large part on the result of one or more dice rolls. Thus, it can be seen as a multi-action probabilistic problem driven by a Markov decision process. In this article, we present the first stochastic model of the main phase of Blood Bowl to our knowledge and the premise of a dedicated planning framework. Such a framework could offer interesting grounds and insights for modeling high turn-wise branch factor games.