Using Machine Learning to Predict National Hockey League Average Home Game Attendance

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Published: Dec 5, 2018
DOI: https://doi.org/10.5750/jpm.v12i2.1608
Keywords:
National Hockey League linear regression random forests M5 prime extreme gradient boosting

Main Article Content

Barry E King
Butler University
Jennifer L Rice
Butler University
Julie Vaughan
Butler University

Abstract

Research predicting National Hockey League average attendance is presented. The seasons examined are the 2013 hockey season through the beginning of the 2017 hockey season. Multiple linear regression and three machine learning algorithms – random forest, M5 prime, and extreme gradient boosting – are employed to predict out-of-sample average home game attendance. Extreme gradient boosting generated the lowest out-of-sample root mean square error.  The team identifier (team name), the number of Twitter followers (a surrogate for team popularity), median ticket price, and arena capacity have appeared as the top four predictor variables. 

Article Details

Issue
Vol. 12 No. 2 (2018)
Section
Articles
Author Biographies

Barry E King, Butler University

Associate Professor of Information Systems and Operations Management

Jennifer L Rice, Butler University

Assitant Professor of Economics

Julie Vaughan, Butler University

Research Assistant College of Pharmacy and Health Sciences

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