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Parity in professional sports when revenues are maximized

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  • Biner, Burhan

Abstract

There are two major hypotheses regarding the talent distribution among the teams that would maximize the total revenues in a sports league; dominant teams versus parity. This paper examines the revenue structure of National Football League and proposes policy recommendations regarding talent distribution among the teams. By using a unique, rich data set on game day stadium attendance and TV ratings we are able to measure the total demand as a function of involved teams' talent levels. Reduced form regression results indicate that TV viewers are more interested in close games, on the other hand stadium attendees are more interested in home team's dominance, in other words stadium demand and TV demand work against each other. We therefore propose a policy that promotes slight parity among the teams where big market teams have a slight advantage over the others. Total revenues of the league are maximized under such policy.

Suggested Citation

  • Biner, Burhan, 2014. "Parity in professional sports when revenues are maximized," Economic Modelling, Elsevier, vol. 40(C), pages 12-20.
    • Handle: RePEc:eee:ecmode:v:40:y:2014:i:c:p:12-20
    DOI: 10.1016/j.econmod.2014.03.002
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    References listed on IDEAS

    as
    1. Biner, Burhan, 2013. "Is parity good? Externalities in professional sports," Economic Modelling, Elsevier, vol. 30(C), pages 715-720.
    2. Andrew Welki & Thomas Zlatoper, 1999. "U.S. professional football game-day attendance," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 27(3), pages 285-298, September.
    3. El-Hodiri, Mohamed & Quirk, James, 1971. "An Economic Model of a Professional Sports League," Journal of Political Economy, University of Chicago Press, vol. 79(6), pages 1302-1319, Nov.-Dec..
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    Cited by:

    1. Narayan, Paresh Kumar & Rath, Badri Narayan & Prabheesh, K.P., 2016. "What is the value of corporate sponsorship in sports?," Emerging Markets Review, Elsevier, vol. 26(C), pages 20-33.
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    3. Dmitry I. Ignatov & Sergey I. Nikolenko & Taimuraz Abaev & Jonas Poelmans, 2014. "Improving Quality Of Service For Radio Station Hosting: An Online Recommender System Based On Information Fusion," HSE Working papers WP BRP 31/MAN/2014, National Research University Higher School of Economics.
    4. Thadeu Gasparetto & Carlos Fernandez-Jardon & Angel Barajas, 2014. "Brand Teams And Distribution Of Wealth In Brazilian State Championships," HSE Working papers WP BRP 30/MAN/2014, National Research University Higher School of Economics.

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    More about this item

    Keywords

    Dominant team; Cartels; Censored regression; Heckman selection model; Random coefficients model;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • L52 - Industrial Organization - - Regulation and Industrial Policy - - - Industrial Policy; Sectoral Planning Methods
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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