This article develops a simulator for matches in the National Hockey League (NHL) with the intent of assessing strategies for pulling the goaltender. Aspects of the approach that are novel include breaking the game down into finer and more realistic situations, introducing the effect of penalties, and including the home-ice advantage. Parameter estimates used in the simulator are obtained through the analysis of an extensive dataset using constrained Bayesian estimation via Markov chain methods. Some surprising strategies are obtained which do not appear to be used by NHL coaches.
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Beaudoin et al. (2010) studied this question.
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