TAILIEUCHUNG - Predicting attendance at major league soccer matches: A comparison of four techniques

This paper discusses predicting attendance at Major League Soccer events using data from the 2014 and 2015 seasons. Panel data is obtained for each team, season, and weather category. A traditional least squared dummy variable linear regression technique is used along with three machine learning algorithms – random forest, M5 prime, and extreme gradient boosting. Extreme gradient boosting provides superior results with respect to out-of-sample root mean square error statistics. Well-founded technique for working with different methods is presented and the efficacy of contemporary algorithms is offered. | Journal of Computer Science and Information Technology December 2018, Vol. 6, No. 2, pp. 15-22 ISSN: 2334-2366 (Print), 2334-2374 (Online) Copyright © The Author(s). All Rights Reserved. Published by American Research Institute for Policy Development DOI: URL: Predicting Attendance at Major League Soccer Matches: A Comparison of Four Techniques Barry E. King1 & Jennifer Rice2 Sport team managers need to predict attendance levels at sporting events to plan staffing levels, plan inventories, and decide upon possible promotions. This paper discusses predicting attendance at Major League Soccer events using data from the 2014 and 2015 seasons. Panel data is obtained for each team, season, and weather category. A traditional least squared dummy variable linear regression technique is used along with three machine learning algorithms – random forest, M5 prime, and extreme gradient boosting. Extreme gradient boosting provides superior results with respect to out-of-sample root mean square error statistics. Well-founded technique for working with different methods is presented and the efficacy of contemporary algorithms is offered. Keywords: Major League Soccer, machine learning, least square dummy variable linear model, random forest, M5 prime, extreme gradient boosting 1. Introduction Attendance at sporting events has been well-researched as evidenced by the numerous references in the Literature Review section. Being relatively new, attendance at Major League Soccer (MLS) matches has not received as much attention. Prediction models for attendance mostly have been multivariate linear regression attempts. This study focuses on attendance at MLS matches and examines the efficacy of three machine learning regression methods in addition to a panel adjusted linear regression approach. The goal of the article is to demonstrate well-found practice in developing machine learning models and to examine the .

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• Predicting attendance at major league soccer matches
• A comparison of four techniques
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• The efficacy of contemporary algorithms
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