A Bayesian Approach to Predict the Number of Goals in Hockey

Abdolnasser Sadeghkhani and Seyed Ejaz Ahmed
Additional contact information
Abdolnasser Sadeghkhani: Department of Mathematics, Brock University, St. Catharines, ON L2S 3A1, Canada
Seyed Ejaz Ahmed: Department of Mathematics, Brock University, St. Catharines, ON L2S 3A1, Canada

Stats, 2019, vol. 2, issue 2, 1-11

Abstract: 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.

Keywords: Conway–Maxwell–Poisson distribution; hockey; Poisson distribution; predictive density estimation (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2571-905X/2/2/17/pdf (application/pdf)
https://www.mdpi.com/2571-905X/2/2/17/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jstats:v:2:y:2019:i:2:p:17-238:d:224760

Access Statistics for this article

Stats is currently edited by Mrs. Minnie Li

More articles in Stats from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().