IDEAS home Printed from https://ideas.repec.org/p/sgo/wpaper/1402.html
   My bibliography  Save this paper

Major League Baseball Attendance: Long-term Analysis Using Factor Models

Author

Listed:
  • Seung C.Ahn

    (Department of Economics, Sogang University, Seoul)

  • Young H. Lee

    (Department of Economics, Sogang University, Seoul)

Abstract

Although Major League Baseball (MLB) has a long history, most studies of attendance have focused on recent years because important explanatory data, such as ticket prices, are often missing for earlier periods. The present study aims to fill gaps in the data by analyzing individual team attendance records between 1904 and 2012. If important missing variables are determined using common factors that can influence between-teams attendance, the attendance function can be estimated by a panel factor model. Using this approach, our results indicate that the determinants of fans¡¯ attendance decisions have changed over time. In earlier years, winning performance was an important determinant of attendance. However, in recent years, other factors have also influenced attendance. Not only the home team¡¯s winning performance, but also the outcome uncertainty, size and quality of the stadium, and playing styles influence fan attendance in present-day MLB.

Suggested Citation

  • Seung C.Ahn & Young H. Lee, 2014. "Major League Baseball Attendance: Long-term Analysis Using Factor Models," Working Papers 1402, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    • Handle: RePEc:sgo:wpaper:1402
    as

    Download full text from publisher

    File URL: https://tinyurl.com/yp3mo779
    File Function: First version, 2014
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dominik Schreyer, 2019. "Football spectator no-show behaviour in the German Bundesliga," Applied Economics, Taylor & Francis Journals, vol. 51(45), pages 4882-4901, September.
    2. Brian M. Mills & Rodney Fort, 2018. "Team-Level Time Series Analysis in MLB, the NBA, and the NHL," Journal of Sports Economics, , vol. 19(7), pages 911-933, October.
    3. Hayley Jang & Doyoung Kim & Young Hoon Lee, 2023. "Uncertainty of Outcome Hypothesis: Theoretical Development and Empirical Evaluation," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 62(3), pages 271-291, May.
    4. Judah Brown & Brandon J. Sheridan, 2020. "The Impact of National Anthem Protests on National Football League Television Ratings," Journal of Sports Economics, , vol. 21(8), pages 829-847, December.
    5. Young H. Lee, 2018. "Common Factors in Major League Baseball Game Attendance," Journal of Sports Economics, , vol. 19(4), pages 583-598, May.
    6. Adam C. Merkle & Catherine Hessick & Britton R. Leggett & Larry Goehrig & Kenneth O’Connor, 2020. "Exploring the components of brand equity amid declining ticket sales in Major League Baseball," Journal of Marketing Analytics, Palgrave Macmillan, vol. 8(3), pages 149-164, September.
    7. Peter A. Groothuis & Kurt W. Rotthoff & Mark C. Strazicich, 2017. "Structural Breaks in the Game," Journal of Sports Economics, , vol. 18(6), pages 622-637, August.
    8. Ryśnik Jakub, 2019. "Identification and Evaluation of Factors Influencing Sports Fan Attendance at International Events: Volleyball Case Study," Turyzm / Tourism, Sciendo, vol. 29(2), pages 123-135, December.

    More about this item

    Keywords

    Attendance; outcome uncertainty; common factors; factor loading; panel data; fan loyalty;
    All these keywords.

    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sgo:wpaper:1402. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Jung Hur (email available below). General contact details of provider: https://edirc.repec.org/data/risogkr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.