An examination of dynamic ticket pricing and secondary market price determinants in Major League Baseball |
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| Affiliation: | 1. Old Dominion University, Student Recreation Center #2012, Norfolk, VA 23529, United States;2. Temple University, Sport & Recreation Management, United States;1. School of Management and Marketing, Faculty of Business and Law, Deakin University, Australia;2. Honorary Fellow, College of Business, Victoria University, Australia;1. Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran;2. Department of Decision & Information Sciences, Charlton College of Business, University of Massachusetts Dartmouth, USA;1. Deutsches Elektronen–Synchrotron, DESY, Platanenallee 6, D–15738 Zeuthen, Germany;2. Institute of Algebra, Johannes Kepler University, Altenbergerstraße 69, A–4040, Linz, Austria;3. Institut für Theoretische Teilchenphysik, Karlsruher Institut für Technologie (KIT), D-76128 Karlsruhe, Germany |
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| Abstract: | Over the past several years, professional sport organizations have started to shift from cost-based ticket pricing strategies to a demand-based focus, where price considerations are driven, in part, by market demand. Dynamic ticket pricing (DTP), where prices fluctuate daily based on market factors, evolved from this transition. The motivation for DTP stems from the significant growth of the secondary ticket market, where ticket prices are almost completely demand driven. One issue with this strategy is the limited understanding of specific factors that influence dynamically priced tickets and secondary market tickets. The current study examined price determinants in the primary market where DTP has been implemented and comparable tickets in the secondary market. Four regression models were developed for this purpose. The first two models examined factors influencing dynamic ticket price. Both DTP models were found to be significant, explaining 91.4% and 70.8% of the variance in dynamic price, respectively. The second two models examined factors influencing secondary market ticket price. Both secondary market models were found to be significant, explaining 82.7% and 79.7% of the variance in secondary market price, respectively. There were many consistencies between models, including an emphasis on team and individual performance factors, ticket-related factors, and time-related factors. However, there were a number of unique aspects to each model, which may help sport managers develop pricing strategies that better reflect market demand for sport |
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| Keywords: | Sport finance Dynamic ticket pricing Professional sport |
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