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

Abstract

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 sports demand, I consider different forecasting horizons and only use information that is publicly accessible in advance of a game or season. The 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 different least-squares dummy variable regression models and a naïve 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, least-squares regression fails to account for various team-specific peculiarities, despite accounting for team fixed effects and censoring attendance predictions to fit to stadium capacities.