Long before the Chiefs and the Eagles battle it out in Super Bowl LIX, IU Indianapolis students studying sports analytics teamed up to compete in a bowl of their own—the NFL Big Data Bowl.
Sponsored by the NFL’s Football Operations organization, the Big Data Bowl is an annual competition offering college students and sports analytics professionals a chance to contribute to the league’s use of advanced analytics in player and team performance. Using traditional football data and “Next Gen Stats” that include real-time player and ball tracking, participants compete for $100,000 in prize money and the opportunity to present their projects at the 2025 NFL Scouting Combine in Indianapolis.
For this year’s Big Data Bowl, participants were charged with using complex sets of geospatial data that tracked players’ pre-snap movements to predict what would happen after the snap.
“Case study competitions are valued in the job market like an internship and offer the full package—resume building, the potential for networking and similarity to a first job project where you’re working independently,” said sport management associate professor Liz Wanless, Ed.D., who teaches applied data science to master’s students specializing in sports analytics and directs the Sports Innovation Institute. “And because it’s also a competition, all of these elements are wrapped in a sense of urgency that’s different from assignments we do in class.”
Anurag Reddeddy, a student in the applied data science master’s program, was a member of the three-person team that participated in this year’s NFL Big Data Bowl. Reddeddy, along with co-team members Elliott Scott, also a master’s student in applied data science, and Jorian Mangum, a graphic design major at IU Indianapolis, submitted their entry, “Predicting Wide Receiver Routes in the NFL.”
“Developing the data model was not difficult for us,” said Reddeddy, who is completing his final semester in the master’s program. “However, identifying the data and what kind of data we needed to feed into the model was challenging.”
Transforming raw tracking data provided by the NFL (positions, velocities, and movements of players over time), Reddeddy and Scott used a sequence-to-sequence modeling approach to separate each on-field play into “before snap” and “after snap” frames. The model was designed to forecast through machine learning the coordinates of wide receiver routes from the snap through the outcome of the pass. With visuals accounting for 20 percent of the team’s data bowl score, Reddeddy and Scott relied on Mangum’s graphic design skills to develop a video highlighting their project.
While the team’s entry didn’t make the cut of 2025 Big Data Bowl finalists, Reddeddy gained valuable experience he’ll carry into his job search later this year.
“The best way to learn something is to step in, and this competition enhanced my understanding of how football actually works,” said Reddeddy. “It may open up opportunities for me to work in the football industry, and I might have a little advantage over other job applicants because of my participation in this NFL competition.”
Wanless agrees, citing not only the inherent benefits of real-world experience, but also the strengths of the applied data science sports analytics specialization at IU Indianapolis.
“It’s not enough anymore to just analyze data and spreadsheets,” Dr. Wanless said. “That’s why our program is structured to ensure students have the informatics, computer science, and data engineering skills that today’s sports industry demands. What we’re teaching our students truly represents the next wave of competitive advantage in the field.”
Editor’s note: The Applied Data Science master’s degree with Sports Analytics specialization is offered as an accelerated sport management/sports analytics degree option by the Department of Tourism, Event and Sport Management in the IU School of Health & Human Sciences in partnership with the IU Luddy School of Informatics, Computing and Engineering. For more information, visit go.iu.edu/tesm.