Sports teams or leagues searching for the perfect sponsor may soon have a far more precise way to do it. A new AI program — trained on more than 23,000 data points — promises to help leagues and teams find their optimal match, turning what has long been an instinct-driven industry into one guided by predictive analytics.
The program was developed by Dr. Jonathan A. Jensen, an associate professor in the Department of Kinesiology and Sport Management at Texas A&M University. Dr. Jensen began exploring the concept in 2014, when advances in AI opened new possibilities at the intersection of data science and sport business — a convergence that is now reshaping how billions of dollars flow through the industry.
“Someone once said, ‘Money isn’t everything,’” Dr. Jensen said in a recent presentation. “I’m not certain who said those words, but one thing I can say for certain: that person did not work in the sport industry.”
In today’s sports economy, he said, money is not just important — it is foundational. Across professional and college athletics, sponsorships represent one of the three primary revenue streams, alongside ticket sales and media rights. More than $65 billion in sponsorship deals are bought and sold globally each year, often based on relationships, intuition and precedent rather than data analysis.
What began as an academic exercise — Dr. Jensen’s dissertation research — has evolved into the Sport Sponsorship Predictive AI Network, or SSPAIN.ai. Early versions of the model relied on just 80 observations. Today, it draws from more than 5,800 sponsorships and 23,000 data points from around the world.
“The only predictive, analytical tool in the marketplace that utilizes a peer-reviewed statistical model to generate predictions for sponsorship renewal and revenue,” Dr. Jensen wrote on LinkedIn, “SSPAIN.ai helps sport organizations, leagues, and events feel more confident about their sponsorship decisions and future financial performance.”
At its core, the system uses a statistical method known as survival analysis — more commonly associated with public health research — to forecast two critical outcomes: the likelihood a sponsor will renew a deal, and how long that partnership is likely to last.
Those predictions are then translated into financial valuations, allowing teams and leagues to assess not just the immediate value of a sponsorship, but its long-term potential.
“The goal is to help them sleep a little better at night,” Dr. Jensen said, referring to the chief financial officers tasked with managing increasingly complex revenue streams.
For decades, sponsorship decisions have been shaped by relationships and brand alignment — the handshake deals and gut instincts that define much of sports business culture. But as private equity flows into franchises and valuations soar, the margin for error has shrunk. AI offers a way to reduce that uncertainty.
By analyzing vast datasets — including fan demographics, engagement patterns and historical deal performance — predictive models can identify which partnerships are most likely to succeed. For sponsors, that means finding teams whose audiences align with their target consumers. For teams, it means targeting companies more likely to commit long-term.
Hans Westerbeek, a professor of international sport business for Victoria University in Melbourne, Australia, has argued that sponsorship is one of the last major areas of sports yet to fully embrace AI. In his research, he notes that both sponsors and teams stand to benefit: companies can identify the most effective investments, while organizations can better target potential partners.
Just as importantly, AI can measure outcomes. Where sponsorship return on investment was once estimated by manually counting logo appearances, algorithms can now track exposure, sentiment and consumer behavior across digital platforms — offering a far more nuanced picture of impact.
The rise of sponsorship analytics is part of a broader transformation sweeping the sports industry. While AI first gained traction in player performance — from injury prevention to game strategy — its reach now extends far beyond the field.
Teams use AI to break down game film in ways imperceptible to the human eye. In gymnastics, systems developed in collaboration with International Gymnastics Federation analyze athletes’ movements in three dimensions to assist judges with scoring. At the 2024 Paris Olympics, AI helped generate personalized highlight reels and streamline broadcasting workflows.
Even fan engagement has been reshaped. AI-driven platforms now curate content based on individual preferences, recommending highlights, merchandise and experiences tailored to each user. The result is a more immersive and personalized connection between fans and teams — and, by extension, more valuable sponsorship opportunities.
For all its technical complexity, SSPAIN.ai is ultimately a business tool — one designed to influence decisions that ripple across entire organizations.
“If your favorite sport team can either make a little bit more money or perhaps save a little bit more money,” Dr. Jensen said, “it might mean the difference between being able to retain that high-performing player, go out and get that new free agent, or even have renovations at your favorite arena.” In that sense, predictive sponsorship analytics is not just about marketing efficiency. It is about competitive advantage.
The model has already attracted interest from across the sports landscape, including professional teams, college athletic departments and major leagues.
Still, AI is unlikely to replace the human side of sports business. Relationships, brand identity and strategic judgment still matter — and likely always will. Experts say that AI will augment decision-making, automating data analysis while leaving final choices to people.
That balance is already evident in how teams are beginning to adopt such tools: not as replacements for executives, but as decision-support systems.
Dr. Jensen envisions a future in which predictive analytics becomes standard across the industry — not just for sponsorships, but for every aspect of sports business.
In the near term, he hopes SSPAIN.ai will gain traction through trial partnerships and, eventually, investment from venture capital firms or acquisition by a major league. The long-term ambition is more sweeping: to establish a new benchmark for how sponsorships are evaluated and managed.
“Our ultimate goal,” he said, “is to give sports organizations confidence in their financial future.”
