Have you ever wondered how ticket prices at a Milwaukee Bucks game are determined? A group of actuarial science students set out to put a method to the pricing at the third annual Milwaukee Bucks Hackathon Engineered by Modine.

Actuarial science students Owen Podziemski, Allie Ladwig, Colton Lindquist, Nicholas Maier and Thomas Packer were tasked with creating a pricing tier system for premium seating in the Fiserv Forum, which includes the lofts, lounges and other areas that are not within the general bowl seating.

“Our team first considered what would set these tickets apart from the general bowl seating, which already had its own tier system,” said Podziemski. The group determined the premium seats would be more likely purchased by large groups for things like corporate outings, or people coming from out of town for a special game. “We then used multiple statistical methods like linear regression and clustering to determine which aspects of the games were key influencers on premium ticket revenue.”

The group’s deductions led them to creating a tree model with 11 possible routes to take that lead to one of four end tiers for ticket price. They presented their findings to the judges and were one of seven teams to advance to the final round.

“We were very excited to make the finals. We competed with many large schools across the country like University of Central Florida, University of Waterloo and University of Pittsburgh,” said Podziemski.

The finals required the team to give a ten-minute virtual presentation to the judges describing their process and their end result of four tiers of premium ticket pricing. They earned fourth place in the competition. 

The hackathon challenged the team to utilize skills they learned in class, specifically linear models and tree regression they recently learned from Dr. Won Chul Song, assistant professor in the Mathematics Department. “Without these methods, the work would have been much more biased on personal opinion of game worth and less on calculated results,” said Podziemski.

The hackathon provided the students with a unique opportunity to work with a real data set on a real-world project. “It’s one thing to have class projects or assignments that are specifically designed to work with the methods you are learning, but having a data source and being able to apply any and all methods to test what would work best is so much more engaging,” said Podziemski. He compared the problem and dataset to a jigsaw puzzle. “All the pieces are there in the data, it’s all about how well you can unscramble them to see a picture.”