Forecasting Stadium Attendance Using Machine Learning Models: A Case of the National Football League
Yu Pang,
Fengchen Wang
Abstract:The study examines the use of machine learning models to forecast attendance at sports stadiums, specifically analyzing National Football League (NFL) games from 2000 to 2019, with over 5,055 regular-season games. The models, including Linear Regression, Classification and Regression Trees (CART), Random Forest, CatBoost, and XGBoost, integrate a diverse set of variables such as team performance, economic indicators, stadium characteristics, and weather conditions. Each model's accuracy and effectiveness are a… Show more
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