Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/200698
Authors: 
Mueller, Steffen Q.
Year of Publication: 
2018
Series/Report no.: 
Hamburg Contemporary Economic Discussions 65
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.
Subjects: 
Attendance
Major League Baseball
Random forest
Conditional forest
Sport demand
Sports forecasting
Ticket sales
Variable importance
JEL: 
C44
C53
Z2
ISBN: 
978-3-942820-45-5
Document Type: 
Working Paper
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