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Pre- and within-season attendance forecasting in Major League Baseball: A random forest approach

Steffen Mueller

No 65, Working Papers from Chair for Economic Policy, University of Hamburg

Abstract: This study explores the forecasting of Major League Baseball game ticket sales and identifies important attendance predictors by means of random forests that are grown from classification and regression trees (CART) and conditional inference trees. Unlike previous studies that predict sport demand, I consider different forecasting horizons and only use information that is publicly accessible in advance of a game or season. Models are trained using data from 2013 to 2014 to make predictions for the 2015 regular season. The static within-season approach is complemented by a dynamic month-ahead forecasting strategy. Out-of-sample performance is evaluated for individual teams and tested against least-squares regression and a naive lagged attendance forecast. My empirical results show high variation in team-specific prediction accuracy with respect to both models and forecasting horizons. Linear and tree-ensemble models, on average, do not vary substantially in predictive accuracy; however, OLS regression fails to account for various team-specific peculiarities.

Keywords: Attendance; Major League Baseball; Random forest; Conditional forest; Sport demand; Sports forecasting; Ticket sales; Variable importance (search for similar items in EconPapers)
JEL-codes: C44 C53 Z2 (search for similar items in EconPapers)
Pages: 50 pages
Date: 2018-06-27
New Economics Papers: this item is included in nep-for, nep-knm and nep-spo
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Published in Hamburg Contemporary Economic Discussions, Issue 65, 2018

Downloads: (external link)
http://www.hced.uni-hamburg.de/WorkingPapers/HCED-065.pdf First Version, 2018 (application/pdf)

Related works:
Journal Article: Pre- and within-season attendance forecasting in Major League Baseball: a random forest approach (2020) Downloads
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