A regression-based predictive model of student attendance at UVA men´s basketball games

A regression-based predictive model was developed to allow better prediction of attendance for the student general admission seats at University of Virginia men´s home basketball games. The goal was to improve upon the existing prediction method that yielded prediction errors sometimes exceeding one thousand students. Based on the existing attendance prediction method and a literature review, twenty candidate factors were identified for potential use in an improved prediction model. Using a best subsets methodology and data from the previous four basketball seasons, a six predictor model was developed with an adjusted R2 value of 0.816. The predictors were based on whether UVA and/or the opponent were ranked, opponent popularity, and whether classes were in session. The resulting model was validated with data from home games from the 2002-2003 season. Its average prediction error of 263 students (standard deviation of 269 students) was a significant improvement over the existing prediction method