Designing and Evaluating Dynamic Pricing Policies for Major League Baseball Tickets

Abstract

Problem definition: We develop and estimate a demand model that can be used to evaluate and design dynamic pricing policies for single-game tickets by a sports team. Academic/practical relevance: Many firms have difficulty evaluating the impact of their pricing policy, which further inhibits their ability to design and implement dynamic pricing. We address this issue in the context of single-game ticket pricing for a Major League Baseball franchise. Methodology: We develop and estimate a comprehensive regression-based forecasting model that captures relevant aspects of the demand generation process such as ticket quantity and stadium seat section choice. The model is then used to evaluate the revenue associated with a specific pricing policy. Results: The demand model reveals factors that drive sports ticket revenue, such as the effect of home-team performance on overall price sensitivity and the relationship between customers’ arrival timing and product choice. These insights are incorporated into the optimization to improve the dynamic pricing policies. Managerial implications: We show that by leveraging the model insights and allowing sufficient pricing flexibility, the franchise can achieve a potential revenue improvement of 17.2% through daily price reoptimization. This improvement is comparable to the outcome of a clairvoyant policy in which the future evolution of demand is assumed to be known. The online supplement is available at https://doi.org/10.1287/msom.2018.0760 .

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