Researchers from multiple universities across the US have created a pricing tool to help maximise ticket revenue for sports franchises.
Findings from the research, which included looking into the opponent, seat location, date of the game and previous performances, were referenced in the study ‘Data-Driven Sports Ticket Pricing for Multiple Sales Channels with Heterogeneous Customers’ published in Manufacturing and Service Operations Management.
It was written by co-authors Robert Easley, the John W. Berry Sr. Department chair and professor of Information Technology, Analytics and Operations at the University of Notre Dame, and Ovunc Yilmaz, assistant professor at the University of Colorado Boulder.
Additional authors included Alper Arslan, assistant professor of economics at the University of Texas San Antonio, and Ruxian Wang, professor of operations management and business analytics at Johns Hopkins University.
The researchers worked with the National Collegiate Athletic Association (NCAA) Division I football program to review its ticket sales data.
The research team analysed purchasing behaviours and demographic before looking into patrons that were purchasing season tickets and single-game tickets, and created different segments including big donors, the public and employees for season ticket purchasers and donors, alumni and parents for single-ticket purchasers.
Easley said: “There are insights that arise from the data that you would not know by simply looking at a diagram of seating. This suggests that elements such as viewing angle and distance to the field matter to some audiences but not others.
“The time of year, position of the sun and thus the expected temperature can interact with the time of the game, too.”
One insight found that patrons would not purchase tickets in an area when the seat availability fell below a certain point. The research also highlighted that some fans only cared about the best place to watch a game from, while some only wanted the cheapest seat available.
The data from this research was then used to create a pricing tool that could model the optimal prices for every seat in a stadium. This would be based on variables such as seat location, opponent, data and time of the game among other points. Easley and Yilmaz believe this framework would be able to work with any sports team that had historical purchasing data, and help maximise their ticketing revenues.
Easley and Yilmaz also plan to look into data from secondary ticket-purchasing platforms or resale markets for an even clearer perspective of sports ticketing.
Image: Riley McCullough on Unsplash