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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

    (Sogang University, yhnlee@sogang.ac.kr)

Abstract

The author analyzed issues of playoff uncertainty (PU) by examining competitive balance and outcome uncertainty. Specifically, the author (a) developed an alternative measure of PU, (b) analyzed the relationship between PU and postseason structure in Major League Baseball (MLB), and (c) 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," Journal of Sports Economics, , vol. 10(3), pages 219-235, June.
    • Handle: RePEc:sae:jospec:v:10:y:2009:i:3:p:219-235
    DOI: 10.1177/1527002508325785
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Young Hoon Lee, 2013. "Estimation of temporal variations in fan loyalty: application of multi-factor models," Chapters, in: Plácido Rodríguez & Stefan Késenne & Jaume García (ed.), The Econometrics of Sport, chapter 8, pages 135-153, Edward Elgar Publishing.
    2. Neil Longley & Nelson J. Lacey, 2012. "The “Second†Season," Journal of Sports Economics, , vol. 13(5), pages 471-493, October.
    3. 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.
    4. Dorian Owen, 2014. "Measurement of competitive balance and uncertainty of outcome," Chapters, in: John Goddard & Peter Sloane (ed.), Handbook on the Economics of Professional Football, chapter 3, pages 41-59, Edward Elgar Publishing.
    5. Seung C. Ahn & Young H. Lee, 2014. "Major League Baseball Attendance," Journal of Sports Economics, , vol. 15(5), pages 451-477, October.
    6. 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.
    7. Brian M. Mills & Steven Salaga, 2015. "Historical Time Series Perspectives on Competitive Balance in NCAA Division I Basketball," Journal of Sports Economics, , vol. 16(6), pages 614-646, August.
    8. Young Hoon Lee & Rodney Fort, 2012. "Competitive Balance: Time Series Lessons from the E nglish P remier L eague," Scottish Journal of Political Economy, Scottish Economic Society, vol. 59(3), pages 266-282, July.
    9. John Considine & Liam Gallagher, 2018. "Competitive balance in a quasi-double knockout tournament," Applied Economics, Taylor & Francis Journals, vol. 50(18), pages 2048-2055, April.
    10. Nicholas King & P. Dorian Owen & Rick Audas, 2012. "Playoff Uncertainty, Match Uncertainty and Attendance at Australian National Rugby League Matches," The Economic Record, The Economic Society of Australia, vol. 88(281), pages 262-277, June.
    11. Rodney Fort & Young Hoon Lee, 2013. "Major League Baseball attendance time series: league policy lessons," Chapters, in: Plácido Rodríguez & Stefan Késenne & Jaume García (ed.), The Econometrics of Sport, chapter 2, pages 35-50, Edward Elgar Publishing.
    12. 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.

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    More about this item

    Keywords

    competitive balance; playoff uncertainty; uncertainty of outcome hypothesis; fan demand; major league baseball;
    All these keywords.

    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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