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Key Takeaways
- The Sport Sponsorship Predictive Artificial Intelligence Network (SSPAIN.ai) is a tool designed to provide a more precise picture of sponsorship valuations and a clearer financial future for sports organizations.
- SSPAIN.ai uses an advanced machine learning algorithm trained by data from more than 5,800 sponsorships, spanning 23,000 observations across the globe.
- The tool can forecast the probability that a sponsor will renew in the future and the expected duration of the sponsorship agreement.
- SSPAIN.ai is already generating interest from the college and professional ranks, including Texas A&M Athletics, the Dallas Mavericks, and the National Football League.
- The tool represents an example of how research can shape the way an industry functions and has been especially inspiring for the computer science students who helped develop it.
Introduction to Sponsorship Analytics
In the world of pro and college sports, sponsorships are a crucial source of revenue, but the tools used to forecast their value often lag behind the analytics used to measure performance on the field. As Dr. Jonathan A. Jensen, associate professor in the Department of Kinesiology and Sport Management at Texas A&M University, notes, "SSPAIN.ai was built to give sports organizations a clearer picture of their future sponsorship revenue." This is particularly important as capital from private equity firms and venture capitalists continues to flow into sports, making the ability to quantify sponsorship value more critical in valuing each franchise.
The Development of SSPAIN.ai
SSPAIN.ai was developed by Jensen and a team of computer science students in the College of Engineering, who drew on a decade of research into sponsorship analytics. The tool uses an advanced machine learning algorithm trained by data from more than 5,800 sponsorships, spanning 23,000 observations across the globe. As Jensen explains, "Using survival analysis, which is more commonly used in fields like public health, the tool can forecast the probability that a sponsor will renew in the future and the expected duration of the sponsorship agreement." This approach allows teams and leagues to be less reactive and more strategic in the way they manage their portfolios.
The Impact of SSPAIN.ai
The impact of SSPAIN.ai is already being felt, with interest from the college and professional ranks, including Texas A&M Athletics, the Dallas Mavericks, and the National Football League. As Pauline Wade, professor of practice in the Department of Computer Science and Engineering, notes, "Their collaboration across modeling, UI/UX, product ownership and security allowed them to iterate quickly on feedback from both the sponsor and external executives." The tool has also been especially inspiring for the computer science students who helped develop it, including Grant Martinez, who said, "This project gave us great exposure to real-world user feedback and when multiple executives from sports franchises around the nation tested our product."
The Future of SSPAIN.ai
Jensen sees SSPAIN.ai as a pre-seed investment opportunity for VC firms or as a valuable acquisition by a major league or franchise, positioning the tool as the gold standard in sponsorship predictive analytics. He plans to showcase it at the MIT Sloan Sports Analytics Conference trade show in Boston in March, as the statistical foundation of SSPAIN.ai was first presented at the Sloan conference as part of the finals of its research papers competition in 2017. As Jensen says, "Our ultimate goal is to give sports organizations confidence in their financial future. By turning sponsorship data into actionable insights, we’re helping teams and leagues make smarter decisions that will shape the business of sports for years to come."
Conclusion
In conclusion, SSPAIN.ai represents a significant advancement in the field of sponsorship analytics, providing sports organizations with a more precise picture of sponsorship valuations and a clearer financial future. As Jensen notes, "SSPAIN.ai was built to give sports organizations a clearer picture of their future sponsorship revenue." With its advanced machine learning algorithm and data from more than 5,800 sponsorships, the tool is poised to make a major impact on the sports industry. As the sports world continues to evolve, it will be interesting to see how SSPAIN.ai shapes the way teams and leagues approach sponsorship and revenue management.
https://stories.tamu.edu/news/2026/01/06/ai-tool-uses-data-to-redefine-sports-sponsorships/