The Analytics Behind Fairness: Rethinking Revenue Sharing in College Baseball

A New Era in College Sports 

On June 6, 2025, college sports changed forever. A landmark House v. NCAA settlement gave schools like Georgia Tech $20.5 million to allocate to their student-athletes each year. For the first time, athletic departments face a new question: How do you divide millions of dollars fairly? 

Take baseball as an example. Benchmarked against Texas Tech University, our estimate is that the Georgia Tech baseball program will receive about $400,000. Should star players who excelled in previous seasons earn more? Should every athlete get the same amount? How do you build trust so players believe the process is fair and rooted in merit? 

At Georgia Tech, we believe the solution is already in front of us: data. The same information that drives coaching decisions daily can also power a transparent, data-driven revenue sharing model for baseball.  

How Our Collaboration Began 

Dominic Toso, a former Division I baseball player, has been passionate about applying data to solve challenges in baseball since completing his playing career at the University of Richmond in 2022. At Bucknell University, where he began his collegiate baseball career, Toso earned Patriot League Second Team All-Conference honors and was named a Collegiate Baseball Freshman All-American (2018) while maintaining dean’s list standing and earning a spot on the Patriot League Academic Honor Roll. After transferring to Richmond, he became an Atlantic 10 First Team All-Conference selection (2022); was named to the All-ECAC Team, which recognizes top Division I players on the East Coast; and was a three-time Atlantic 10 Commissioner’s Honor Roll honoree (2020-2022). 

As a high-level hitter, Toso relied heavily on technologies such as Blast Motion, Rapsodo and TrackMan to refine his swing. Working closely with Collin Radack – assistant coach at Richmond and former St. Louis Cardinals draft pick – Toso used data to identify inefficiencies and make targeted adjustments that maximized his performance. 

After graduating, Toso joined EAB, an education-technology, services and research firm serving higher education, where he worked as a growth strategies initiative expert. There, he combined market research, Tableau dashboards and case studies to lead enrollment optimization strategies for institutional leaders – an experience that sharpened his understanding of how data can drive business decisions. At the same time, he coached at Ignite Baseball in Arlington, Va., where he learned how to apply technology-driven feedback from a coach’s perspective to accelerate player development. 

These combined experiences led Toso to approach Dr. Jonathan Fan, a faculty member at Georgia Tech’s Scheller College of Business, whose research and teaching focus on data analytics. The two began meeting weekly outside of class to refine the foundations of the Player Growth Index (PGI), fueled by their shared passion for baseball and analytics. Recognizing the need for deeper baseball expertise, Toso invited Radack, his longtime mentor, to join. Radack’s expertise in applying performance data to player development added a critical perspective and cemented a strong triple collaboration. 

The Athlete’s Perspective 

As part of a Market Research Tools & Design course in summer 2025, an MBA student at Scheller College of Business interviewed a Georgia Tech athlete about the shift toward revenue sharing. The athlete believes the move toward revenue sharing is one of the most positive developments in college athletics. In his view, the system must be rooted in merit and performance, not hype or manipulation. 

Turning Data into Decisions 

To meet this challenge, we designed the PGI, a composite score that integrates multiple measures of player value into one metric. Think of it as a GPA for athletes: one number that captures the overall value of a student-athlete. 

The PGI combines performance data from widely adopted technologies – such as Trackman, Rapsodo, Blast Motion and Qualisys – with in-game statistics most valued by MLB organizations, physical performance measurables, social media and name, image and likeness (NIL) metrics into a single composite score (0-100). 

Every MLB organization already uses these tools to predict player success and support draft decisions. Although Georgia Tech baseball and other Division I programs rely on them daily, their potential to shape a fair revenue sharing framework remains untapped. 

Voices from the Field 

These technologies are the MLB’s gold standard for evaluation. Radack explains: 

“Richmond Baseball uses a wide range of technology to support player development and in-game decision-making. On the hitting side, we utilize Blast, HitTrax, Trackman, Force Plate data and Vizual Edge to track swing data, batted ball metrics, zone recognition and visual training. For pitching, we rely on Trackman, high-speed cameras and arm care technology to monitor health, enhance development and refine pitch design. All of this data is also accessible to MLB scouts, which helps our players gain exposure and increases their chances of getting signed.” 

Radack relies heavily on advanced performance tools to elevate the Richmond baseball program. Since fully integrating data-driven training in 2022, he has turned Richmond’s offense into one of the most consistent powerhouses in the Atlantic 10. Under his leadership as hitting coach, the Spiders led the league in batting average in 2022 and 2023 and ranked among the top four in nearly every offensive category in 2024, including home runs, doubles and slugging percentage. His ability to develop award-winning hitters such as Alden Mathes (19th-round draft pick of the Baltimore Orioles), Kyle Schmidt (33rd-round draft pick of the Minnesota Twins) and Brady O’Brien (undrafted free agent signed by the Seattle Mariners) has solidified Richmond as a perennial offensive force. 

According to “Reshaping Baseball: The Impact of Analytics and Technology” [1], Rapsodo’s sensor technology has dramatically changed how talent is evaluated in NCAA baseball. The authors state: “Rapsodo’s affordable systems have democratized access to advanced analytics for mid-tier programs while bridging gaps between collegiate and professional levels.” We believe we can leverage the same data used by MLB organizations to evaluate players’ abilities and growth potential. These metrics form the foundation of the Player Growth Index. We expect that Georgia Tech’s Athletic Department will leverage this data similar to how MLB organizations do, to justify player valuations. 

The Four Pillars of PGI 

The PGI draws on four key areas: 

1. Advanced Player and On-field Performance:Metrics such as exit velocity, bat speed, on-base percentage (OBP) and slugging percentage (SLG).  
2. Year-over-Year Development:Growth over time (e.g., exit velocities, Wins Above Replacement (WAR), barrel percentage). 
3. NIL and Social Media Presence:Followers, engagement rate, NIL earnings and deals signed. 
4. Physical Performance Metrics:Mobility, strength and health metrics. 

Each factor is scored on a 0-100 scale, weighted by importance and then combined into one PGI score. The result is a transparent framework any coach, administrator or player can understand and trust. 

Why It Matters 

At its core, revenue sharing is about trust and a system built on merit. Players want fairness, and administrators need transparency. While this example focuses on baseball, the framework can extend to any sport. With the right metrics, athletic departments like Georgia Tech’s can enter this new era of athlete compensation with confidence, integrity and clarity. 

The PGI will help Georgia Tech identify and reward players who deliver the greatest value to the program. It highlights athletes who not only contribute to wins but also boost attendance, ticket sales and revenue. This gives the athletic department a data-driven way to invest in players who strengthen the school’s brand, maximize return on NIL dollars and fairly allocate resources to those creating the most impact. 

Reference 

1. https://doi.org/10.1287/orms.2025.02.07