Poisson Models to Predict Scoring Rates in Hockey
Abstract: Trying to predict the score of a hockey game can be a complicated and seemingly impossible task due to the fast-pace nature of the game. We propose to model scoring rates by investigating various factors such as a team’s offensive ability, defensive ability of its opponent, and home-ice advantage. Considering that hockey scores are not normally distributed, we assume that scores follow a Poisson distribution and use these factors to build a Poisson regression model for the scoring rates. We apply this model to data from the 2008-2009 ECAC Division I Women’s Ice Hockey season. Using the Poisson model we examine each team’s scoring to generate a Poisson scoring rate and use the fit to produce an offensive rating, defensive rating, and predicted winning percentage for each team.