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The Impact of Postseason Restructuring on the Competitive Balance and Fan Demand in Major League Baseball

Author

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  • Young Hoon Lee

    () (Department of Economics, Sogang University, Seoul)

Abstract

We analyzed issues of playoff uncertainty (PU) by examining competitive balance and outcome uncertainty. Specifically, we (i) developed an alternative measure of PU, (ii) analyzed the relationship between PU and postseason structure in Major League Baseball (MLB), and (iii) tested the PU of the outcome hypothesis using our PU measure. The results provide empirical evidence that postseason restructurings in 1969 and 1995 enhanced the PU significantly and had positive effects on fan demand for MLB. This implies that the league office can partly control PU by revising the league structure and thus increasing fan demand.

Suggested Citation

  • Young Hoon Lee, 2009. "The Impact of Postseason Restructuring on the Competitive Balance and Fan Demand in Major League Baseball," Working Papers 0901, Research Institute for Market Economy, Sogang University, revised 2009.
  • Handle: RePEc:sgo:wpaper:0901
    as

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    File URL: ftp://163.239.156.99/wpaper/LYH_RIME_2009-01.pdf
    File Function: Second version, 2009
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    References listed on IDEAS

    as
    1. Fried, Harold O. & Lovell, C. A. Knox & Schmidt, Shelton S. (ed.), 1993. "The Measurement of Productive Efficiency: Techniques and Applications," OUP Catalogue, Oxford University Press, number 9780195072181.
    2. Martin B. Schmidt & David J. Berri, 2004. "The Impact of Labor Strikes on Consumer Demand: An Application to Professional Sports," American Economic Review, American Economic Association, vol. 94(1), pages 344-357, March.
    3. Leo Kahane & Stephen Shmanske, 1997. "Team roster turnover and attendance in major league baseball," Applied Economics, Taylor & Francis Journals, vol. 29(4), pages 425-431.
    4. Ahn, Seung Chan & Hoon Lee, Young & Schmidt, Peter, 2001. "GMM estimation of linear panel data models with time-varying individual effects," Journal of Econometrics, Elsevier, vol. 101(2), pages 219-255, April.
    5. Anthony C. Krautmann & Young Hoon Lee & Kevin Quinn, 2011. "Playoff Uncertainty and Pennant Races," Journal of Sports Economics, , vol. 12(5), pages 495-514, October.
    6. Craig A. Depken II, 2000. "Fan Loyalty and Stadium Funding in Professional Baseball," Journal of Sports Economics, , vol. 1(2), pages 124-138, May.
    7. Brad R. Humphreys, 2002. "Alternative Measures of Competitive Balance in Sports Leagues," Journal of Sports Economics, , vol. 3(2), pages 133-148, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Young Hoon Lee & Rodney Fort, 2011. "Competitive Balance:Time Series Lessons from the English Premier League," Working Papers 1102, Research Institute for Market Economy, Sogang University, revised Jun 2011.
    2. Brian Mills & Rodney Fort, 2014. "League-Level Attendance And Outcome Uncertainty In U.S. Pro Sports Leagues," Economic Inquiry, Western Economic Association International, vol. 52(1), pages 205-218, January.
    3. Dorian Owen, 2014. "Measurement of competitive balance and uncertainty of outcome," Chapters,in: Handbook on the Economics of Professional Football, chapter 3, pages 41-59 Edward Elgar Publishing.
    4. Young Hoon Lee, 2013. "Estimation of temporal variations in fan loyalty: application of multi-factor models," Chapters,in: The Econometrics of Sport, chapter 8, pages 135-153 Edward Elgar Publishing.
    5. Rodney Fort & Young Hoon Lee, 2013. "Major League Baseball attendance time series: league policy lessons," Chapters,in: The Econometrics of Sport, chapter 2, pages 35-50 Edward Elgar Publishing.
    6. Scott Tainsky & Jason Winfree, 2010. "Short-Run Demand and Uncertainty of Outcome in Major League Baseball," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 37(3), pages 197-214, November.

    More about this item

    Keywords

    competitive balance; playoff uncertainty; uncertainty of outcome hypothesis; fan demand; major league baseball;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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