Optimization of dynamic ticket pricing parameters

  • Mehmet ŞahinEmail author
Research Article


This paper proposes a joint pricing model that combines the advantages of variable and dynamic ticket pricing models, where optimal dynamic prices are calculated for sporting events based on game-, time-, and inventory-related factors. These prices are based on a reference price and several different multipliers. Three different scenarios are investigated to reveal the most effective pricing model, together with corresponding simulation models. For the first time, a fuzzy logic model is used to predict the game multiplier, which reflects the characteristics of each individual game. The required demand information is predicted by an adaptive neuro-fuzzy inference system (ANFIS) model, and the price multiplier parameters are optimized to maximize the expected total revenue. Results based on real sporting data show that the new dynamic strategies were able to increase the expected revenue compared with a traditional static pricing strategy, indicating that all three joint pricing model scenarios could be utilized effectively to price sporting event tickets.


Optimization Simulation Variable ticket pricing Dynamic ticket pricing Forecasting ANFIS 


Compliance with ethical standards

Conflicts of interest

The author declares no conflict of interest.


  1. Bayoumi, A.E.-M., M. Saleh, A.F. Atiya, and H.A. Aziz. 2013. Dynamic pricing for hotel revenue management using price multipliers. Journal of Revenue and Pricing Management 12: 271–285.CrossRefGoogle Scholar
  2. Borland, J., and R. Macdonald. 2003. Demand for sport. Oxford Review of Economic Policy 19: 478–502.CrossRefGoogle Scholar
  3. Boyd, D.W., and L.A. Boyd. 1998. The home field advantage: Implications for the pricing of tickets to professional team sporting events. Journal of Economics and Finance 22: 169–179.CrossRefGoogle Scholar
  4. Bruggink, T. H., and J.W. Eaton. 1996. Rebuilding attendance in Major League Baseball: The demand for individual games. Baseball Economics. Current Research 9–31.Google Scholar
  5. Buraimo, B., and R. Simmons. 2008. Do sports fans really value uncertainty of outcome? Evidence from the English Premier League. International Journal of Sport Finance 3: 146.Google Scholar
  6. Courty, P. 2003. Some economics of ticket resale. The Journal of Economic Perspectives 17: 85–97.CrossRefGoogle Scholar
  7. Cox, A. 2015. Spectator demand, uncertainty of results, and public interest: Evidence from the English Premier League. Journal of Sports Economics 19 (1): 3–30.CrossRefGoogle Scholar
  8. Davenport, T.H. 2014. Analytics in Sports: The New Science of Winning. International Institute for Analytics 2: 1–28.Google Scholar
  9. Dobson, S., and J. Goddard. 2011. The economics of football. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  10. Dorgham, K., M. Saleh, and A.F. Atiya. 2015. A novel dynamic pricing model for the telecommunications industry. In Modelling, Computation and Optimization in Information Systems and Management Sciences , 129–140. Cham: Springer.Google Scholar
  11. Drayer, J., and S.L. Shapiro. 2009. Value determination in the secondary ticket market: A quantitative analysis of the NFL playoffs. Sport Marketing Quarterly 18: 5–13.Google Scholar
  12. Drayer, J., S.L. Shapiro, and S. Lee. 2012. Dynamic ticket pricing in sport: An agenda for research and practice. Sport Marketing Quarterly 21: 184–194.Google Scholar
  13. Drayer, J., D.K. Stotlar, and R.L. Irwin. 2008. Tradition vs. trend: A case study of team response to the secondary ticket market. Sport Marketing Quarterly 17 (4): 235–240.Google Scholar
  14. Forrest, D., and R. Simmons. 2002. Outcome uncertainty and attendance demand in sport: The case of English soccer. Journal of the Royal Statistical Society 51: 229–241.Google Scholar
  15. García, J., and P. Rodríguez. 2002. The determinants of football match attendance revisited: Empirical evidence from the Spanish Football League. Journal of Sports Economics 3: 18–38.Google Scholar
  16. Jang, J.-S. R. 1996. Input selection for ANFIS learning. Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on, 1996. IEEE, pp. 1493–1499.Google Scholar
  17. Kemper, C., and C. Breuer. 2016. How efficient is dynamic pricing for sport events? Designing a dynamic pricing model for Bayern Munich. International Journal of Sport Finance 11: 4.Google Scholar
  18. Marburger, D.R. 1997. Optimal ticket pricing for performance goods. Managerial and Decision Economics 18 (5): 375–381.CrossRefGoogle Scholar
  19. Mathworks. 2018. Defuzzification Methods. Mathworks. Retrieved Feb 2, 2018 fr Accessed 2 Feb 2018.
  20. Negnevitsky, M. 2005. Artificial intelligence: A guide to intelligent systems. London: Pearson Education.Google Scholar
  21. Nufer, G., and J. Fischer. 2013. Ticket pricing in European football-Analysis and implications. International Journal of Human Movement and Sports Sciences 1: 49–60.Google Scholar
  22. Parris, D.L., J. Drayer, and S.L. Shapiro. 2012. Developing a pricing strategy for the Los Angeles Dodgers. Sport Marketing Quarterly 21 (4): 256–264.Google Scholar
  23. Peel, D.A., and D.A. Thomas. 1992. The demand for football: Some evidence on outcome uncertainty. Empirical Economics 17: 323–331.CrossRefGoogle Scholar
  24. Pwc. 2015. At the gate and beyond - Outlook for the sports market in North America through 2019. In PwC sports outlook, ed. A.W. Jones. London: Pwc.Google Scholar
  25. Rascher, D. 1999. A test of the optimal positive production network externality in Major League Baseball. Germany: University Library of Munich.Google Scholar
  26. Rascher, D.A., C.D. Mcevoy, M.S. Nagel, and M.T. Brown. 2007. Variable ticket pricing in Major League Baseball. Journal of Sport Management 21: 407–437.CrossRefGoogle Scholar
  27. Şahin, M., and R. Erol. 2017a. A comparative study of neural networks and ANFIS for forecasting attendance rate of soccer games. Mathematical and Computational Applications 22: 43.CrossRefGoogle Scholar
  28. Şahin, M., and R. Erol. 2017b. A dynamic ticket pricing approach for soccer games. Axioms 6: 31.CrossRefGoogle Scholar
  29. Shapiro, S.L., and J. Drayer. 2012. A new age of demand-based pricing: An examination of dynamic ticket pricing and secondary market prices in Major League Baseball. Journal of Sport Management 26: 532–546.CrossRefGoogle Scholar
  30. Shapiro, S.L., and J. Drayer. 2014. An examination of dynamic ticket pricing and secondary market price determinants in Major League Baseball. Sport Management Review 17: 145–159.CrossRefGoogle Scholar
  31. Srisaeng, P., G.S. Baxter, and G. Wild. 2015. An adaptive neuro-fuzzy inference system for forecasting Australia’s domestic low cost carrier passenger demand. Aviation 19: 150–163.CrossRefGoogle Scholar
  32. Will, D. H. 1999. The Federation’s viewpoint on the new transfer rules. In Competition Policy and Professional Sports: Europe after the Bosman Case, eds. S. Kessene, and C. Jeanrenaud. Antwerp: Standard Editions.  Google Scholar
  33. Zadeh, L.A. 1965. Fuzzy sets. Information and Control 8: 338–353.CrossRefGoogle Scholar
  34. Zimmermann, H.-J. 2011. Fuzzy set theory—and its applications. New York: Springer.Google Scholar

Copyright information

© Springer Nature Limited 2019

Authors and Affiliations

  1. 1.Department of Industrial EngineeringIskenderun Technical UniversityIskenderunTurkey

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