Machine learning prediction of factors affecting Major League Baseball (MLB) game attendance: algorithm comparisons and macroeconomic factor of unemployment

• Published in: International journal of sports marketing & sponsorship Vol. 25; no. 2; pp. 382 - 395
• Main Authors: Park, Juho, Cho, Junghwan, Gang, Alex C., Lee, Hyun-Woo, Pedersen, Paul M.
• Format: Journal Article
• Language: English
• Published: London Emerald Publishing Limited 19.03.2024; Emerald Group Publishing Limited
• Subjects: Algorithms; Artificial intelligence; Automation; Big Data; Data analysis; Economic conditions; Feature selection; Forecasting; Machine learning; Macroeconomics; Methods; Precipitation; Professional baseball; Regression analysis; Time series; Unemployment; Variables; Attendance; Major league baseball; Unemployment; XGBoost; Machine learning
• ISSN: 1464-6668; 2515-7841
• DOI: 10.1108/IJSMS-06-2023-0129

PurposeThis study aims to identify an automated machine learning algorithm with high accuracy that sport practitioners can use to identify the specific factors for predicting Major League Baseball (MLB) attendance. Furthermore, by predicting spectators for each league (American League and National League) and division in MLB, the authors will identify the specific factors that increase accuracy, discuss them and provide implications for marketing strategies for academics and practitioners in sport.Design/methodology/approachThis study used six years of daily MLB game data (2014–2019). All data were collected as predictors, such as game performance, weather and unemployment rate. Also, the attendance rate was obtained as an observation variable. The Random Forest, Lasso regression models and XGBoost were used to build the prediction model, and the analysis was conducted using Python 3.7.FindingsThe RMSE value was 0.14, and the R2 was 0.62 as a consequence of fine-tuning the tuning parameters of the XGBoost model, which had the best performance in forecasting the attendance rate. The most influential variables in the model are “Rank” of 0.247 and “Day of the week”, “Home team” and “Day/Night game” were shown as influential variables in order. The result was shown that the “Unemployment rate”, as a macroeconomic factor, has a value of 0.06 and weather factors were a total value of 0.147.Originality/valueThis research highlights unemployment rate as a determinant affecting MLB game attendance rates. Beyond contextual elements such as climate, the findings of this study underscore the significance of economic factors, particularly unemployment rates, necessitating further investigation into these factors to gain a more comprehensive understanding of game attendance.