ASU Student Explores Environmental Impacts in Football with the Center for Accelerating Operational Efficiency

Sia Sheguri next to large 3-dimensional sign reading "informs" for the Informs conference she attended.

Arizona State University (ASU) senior Sia Sheguri is advancing her academic journey through research at the Center for Accelerating Operational Efficiency (CAOE). Working with CAOE’s Director, Ross Maciejewski, Executive Director, Ron Askin and Postdoctoral Research Scholar Jiayi Hong, Sia has ventured into data-driven environmental research, focusing on the carbon emissions associated with NCAA football conference alignments. Her work highlights her evolving expertise in computer science, data science, and her commitment to making technology accessible.

“It’s been a pleasure working with Sia. Coming from a CS background, she had strong  computing skills but not all the mathematical modeling experience needed for the project. Nonetheless, she enthusiastically delved into the project learning about optimization, geographic data bases and NCAA sports to enable her to collect and evaluate the data discovering  some interesting results and a prescriptive solution for balancing carbon footprint with fan-based factors important for viable conferences,” Ron Askin said.

Research Beginnings: A Serendipitous Connection

Sheguri’s journey with CAOE began after receiving a scholarship from the Women in Computer Science organization to attend the 2023 Grace Hopper Conference, a networking hub for women in technology. There, she met Dr. Ross Maciejewski, CAOE director and an advisor for the group, and soon connected with Dr. Ron Askin as well.

“After the [Grace Hopper] conference, I reached out to Dr. Ross about potential research opportunities,” Sheguri shared. “We discussed several ideas, and eventually developed the concept for our paper on carbon emissions in football games.”

The past few years have seen major realignments in NCAA Football Conferences with western teams such as Stanford and UC- Berkeley joining the Atlantic Coast Conference. The research explores the environmental impact of these new football conference alignments, a concept that evolved with input from both Maciejewski and Askin, leading to data models on optimal geographic realignments.

Navigating Challenges in Data Optimization

Sia faced a steep learning curve when she began the project, especially with data optimization.

“At first, I wasn’t familiar with many of the concepts, but as I progressed, I grew more confident in handling the data models and mathematical components involved,” Sheguri said.

The challenge became an opportunity for growth, resulting in significant advancements in her technical skills. One of the most surprising findings from Sheguri’s research was that even with teams in close geographic proximity, carbon emissions were not as low as anticipated. Initially, the team hypothesized that minimizing travel distances between teams within the same geographic region would significantly cut down on carbon emissions. By collecting conference schedules, team travel policies and stadium locations, it was shown that recent conference realignments significantly increased carbon emissions due to increased travel distances for some teams. Moreover, the use of regional divisions within conferences would not have as much impact as hypothesized if there were still some inter-division games. But, optimally realigning conferences based on geographic proximity could significantly reduce carbon emissions.

Conferences want broad exposure for recruiting and maintaining a strong fan base. Rather than focusing solely on geography, Sheguri’s team developed a model that considered additional variables like regional population density and GDP, which could reflect the socioeconomic factors influencing conference alignment decisions. By incorporating these constraints, the model generated conference alignments that minimize travel distances but also ensure economic and exposure parity.

“This shift in focus provided deeper insights into how realignments could balance environmental impact alongside other key considerations,” Sheguri explained.

Academic Growth and Future Aspirations

Sheguri presented her team’s findings at the INFORMS 2024 conference in Seattle on October 22, speaking to an audience of approximately 30 people interested in sports, namely how operations research can aid in sports management.

“At first, it was nerve-wracking to stand in front of professionals from various fields,” she said. “But I became more comfortable as I went on, and there was great engagement with questions and discussions.”

This experience has solidified Sia’s dedication to her field, leading her to join ASU’s 4+1 accelerated master’s program to deepen her expertise. Her work with CAOE has broadened her view of data science’s potential, especially in relation to her long-term career aspirations.

Sia Sheguri next to projector with presentation reading "NCAA Conference Realignment" presenting the finds at the Informs conference she attended.

“The research I conducted with CAOE has been instrumental in helping me build a strong foundation in data science and how it relates to computer science,” Sheguri said. “This project has not only expanded my technical skills but also inspired me to explore more advanced research opportunities in data-driven fields, which aligns closely with my long-term career goals.”

Making Technology Accessible: A Vision for the Future

Beyond her technical pursuits, Sia brings a passion for writing to her studies, aiming to bridge the gap between complex technical ideas and accessible communication. “Writing has always been a constant for me,” she noted, explaining that her goal is to demystify technology. “I see myself using writing to make tech concepts more understandable for a broader audience,” she added.

Sia’s journey at ASU and CAOE represents a blend of academic excellence, passion for the environment, and a desire to create accessible technology solutions—values that align with CAOE’s commitment to student development and broader operational efficiency.