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Variable Ticket Pricing in Major League Baseball


  • Daniel, Rascher
  • Chad, McEvoy
  • Mark, Nagel
  • Matt, Brown


Sport teams have historically been reluctant to change ticket prices during the season. Recently, however, numerous sport organizations have implemented variable ticket pricing in an effort to maximize revenues. In Major League Baseball, variable pricing results in ticket price increases or decreases depending on factors such as quality of the opponent, day of the week, month of the year, and for special events such as opening day, Memorial Day and Independence Day (July 4). Using censored regression and elasticity analysis, this paper demonstrates that variable pricing would have yielded approximately $590,000 per year in additional ticket revenue for each Major League team in 1996, ceteris paribus. Accounting for capacity constraints, this amounts to only about a 2.8% increase above what occurs when prices are not varied. For the 1996 season, the largest revenue gain would have been the Cleveland Indians, who would have generated an extra $1.4 million in revenue. The largest percentage revenue gain would have been the San Francisco Giants. The Giants would have seen an estimated 6.7% increase in revenue had they used optimal variable pricing.

Suggested Citation

  • Daniel, Rascher & Chad, McEvoy & Mark, Nagel & Matt, Brown, 2007. "Variable Ticket Pricing in Major League Baseball," MPRA Paper 25803, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:25803

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    References listed on IDEAS

    1. Daniel Levy & Mark Bergen & Shantanu Dutta & Robert Venable, 1997. "The Magnitude of Menu Costs: Direct Evidence from Large U. S. Supermarket Chains," The Quarterly Journal of Economics, Oxford University Press, vol. 112(3), pages 791-824.
    2. Donald L. Alexander, 2001. "Major League Baseball," Journal of Sports Economics, , vol. 2(4), pages 341-355, November.
    3. McDonald, Mark & Rascher, Daniel, 2000. "Does Bat Day Make Cents? The Effect of Promotions on the Demand for Major League Baseball," MPRA Paper 25739, University Library of Munich, Germany.
    4. Rodney Fort, 2004. "Inelastic sports pricing," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 25(2), pages 87-94.
    5. Daniel R. Marburger, 1997. "Optimal ticket pricing for performance goods," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 18(5), pages 375-381.
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    Cited by:

    1. Pettersson, Billie & Hoffmann, Mikael & Andersson, David & Wändell, Per & Levin, Lars-Åke, 2012. "Utilization and costs of glucose lowering therapies following health technology assessment for the new reimbursement scheme in Sweden," Health Policy, Elsevier, vol. 108(2), pages 207-215.
    2. Alan L. Morse & Stephen L. Shapiro & Chad D. McEvoy & Daniel A. Rascher, 2008. "The Effects of Roster Turnover on Demand in the National Basketball Association," International Journal of Sport Finance, Fitness Information Technology, vol. 3(1), pages 8-18, February.
    3. Tim Dittmer & Bob Carbaugh, 2014. "Major League Baseball: Dynamic Ticket Pricing and Measurement Costs," Journal for Economic Educators, Middle Tennessee State University, Business and Economic Research Center, vol. 14(1), pages 44-57, Fall.
    4. Drayer, Joris & Rascher, Daniel A. & McEvoy, Chad D., 2012. "An examination of underlying consumer demand and sport pricing using secondary market data," Sport Management Review, Elsevier, vol. 15(4), pages 448-460.
    5. Shapiro, Stephen L. & Drayer, Joris, 2014. "An examination of dynamic ticket pricing and secondary market price determinants in Major League Baseball," Sport Management Review, Elsevier, vol. 17(2), pages 145-159.
    6. repec:gam:jsusta:v:10:y:2018:i:2:p:429-:d:130618 is not listed on IDEAS
    7. Daniel, Rascher & Andrew, Schwarz, 2010. "Illustrations of Price Discrimination in Baseball," MPRA Paper 25807, University Library of Munich, Germany.
    8. repec:eee:touman:v:64:y:2018:i:c:p:291-302 is not listed on IDEAS

    More about this item


    baseball; variable pricing; dynamic pricing; regression; censored regression;

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism


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