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A Bayesian Approach to Predict the Number of Goals in Hockey

Department of Mathematics, Brock University, St. Catharines, ON L2S 3A1, Canada
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Stats 2019, 2(2), 228-238; https://doi.org/10.3390/stats2020017
Received: 27 March 2019 / Revised: 11 April 2019 / Accepted: 16 April 2019 / Published: 21 April 2019
In this paper, we use a Bayesian methodology to analyze the outcome of a hockey game using different sources of information, such as points in previous games, home advantage, and specialists’ opinions. Two different models to predict the number of goals are considered, taking into account that it is the nature of hockey that goals are infrequent and rarely exceed six per team per game. A Bayesian predictive density to predict the number of the goals using each model will be used and the possible winner of the game will be predicted. The corresponding prediction error for each model will be addressed. View Full-Text
Keywords: Conway–Maxwell–Poisson distribution; hockey; Poisson distribution; predictive density estimation Conway–Maxwell–Poisson distribution; hockey; Poisson distribution; predictive density estimation
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Sadeghkhani, A.; Ahmed, S.E. A Bayesian Approach to Predict the Number of Goals in Hockey. Stats 2019, 2, 228-238.

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