Hello and welcome, as always, to your Thursday FOIAball.
We’ve got the confidential proposals Big Four consulting firms sent to universities to build the NILGo platform.
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How Deloitte pitched NILGo to the NCAA
College sports’ current Name, Image, and Likeness arrangement may be the only system where individuals' private business deals require third-party approval.
Despite ungodly sums of money floating around athletics, the NCAA still wants to claim that literal cash isn’t buying championships.
The setup would be absurd anywhere else. If Clavicular wants to start doing ad reads for Athletic Greens (now AG1), he doesn’t need permission to take whatever big check they want to cut. When Livvy Dunne got a post-graduation deal with Secret, she wasn’t forced to hand over the numbers.
Say what you will about our free-market economy, but companies are allowed to pay whatever they think is right. And individuals can set a price for their services.
(FOIAball is now $3.99 for your first six months, FYI.)
But student-athletes must submit endorsement deals over $600 to the College Sports Commission, a for-profit entity itself, which gets to decide just how much they get to profit.
And FOIAball believes student-athletes should have as much information as possible about how the CSC works.
So when we obtain the pitch decks detailing the proprietary data and algorithms that Big Four consultants want millions to design and build to determine teenagers’ value to the world, you can be certain we’re gonna publish them
Like… right now.
In January 2024, the NCAA approved new transparency rules around NIL, which required student-athletes to disclose deals over $600.
A few weeks later, it put out a Request for Proposal for the database needed to store all that information. In November 2024, Deloitte won the bid, earning a contract to build and manage what is now NILGo.
FOIAball obtained the final proposals from Deloitte and PwC that were circulated to schools before the decision.
We need to be clear that these were from before the House settlement was finalized in July 2025 and before the establishment of the College Sports Commission, which oversees NILGo.
So it is possible some elements have changed. But they still sketch out the data Deloitte likely used to build the AI system that automatically approves or rejects deals.
We also got a pretty good idea of what the CSC is paying for the whole shebang.
Deloitte’s deck did not have a cost for the project. PwC bid the contract at $6 million. That included $2.5 million to build the platform and backend infrastructure, then another $3.5 million to operate it over a five-year period.
PwC did offer a 10% discount on the initial cost as an “investment in our shared success,” but $260,000 off apparently wasn’t enough to sway college decision makers.
Deloitte’s deck was titled “NIL Platform Development,” and though we can’t be certain everything in the deck is what the NILGo algorithms are now (again, just wanna be clear about that), the broad strokes are probably very similar.
There is some interesting language at the beginning of the pitch.
The deck’s stated objective is to “evaluate NIL deals between Student-Athletes and third parties … and calculate a fair market value range for each deal.”
That runs somewhat counter to the language on the College Sports Commission site, which notes that NILGo does not calculate Fair Market Value for each third-party NIL deal, instead saying it determines a “Range of Compensation.”
The idea is that the CSC and NILGo aren’t deciding exactly what you should get paid, but rather whether what you are getting paid is reasonable. It’s semantics that are belied by the language in the pitch docs.
PwC’s proposal clearly points out that for any range, there’s a figure for a “specified fair market value” that an acceptable number, higher or lower, would be based on.
But whatever that exact value or spread is, the legitimacy of a student-athlete’s deal is still determined by an algorithm made up of opaque data points.
Opaque, that is, until now.
Deloitte’s proposed process begins with building a dataset to train its models on, leveraging “primary, public, and proprietary databases,” turning athletes’ “inputs into numerical scores across categories” to establish whether a deal is acceptable.
What kind of data did Deloitte say it would train its model on to determine what people are worth, absent a truly free market?
It’s based on three categories: Impact, Institution, and Sport.
For “Impact,” would it surprise you to know Deloitte has most likely crawled every single NCAA athlete's social media profile to determine not just how many followers they have, but how engaged their audience is?
“The system will fetch public data like social media followership via social media APIs. We will expand the …. with proprietary data from Deloitte, Institutions, Conferences, or other third parties.”
The “Institution” data has figures students have very little control over, like how many followers their school has on Instagram and its overall student body size.
The “Sport” figures are calculated by the estimated market size of specific sports, which includes viewership and social following. How one figures out how many online fans men’s tennis has is data we can’t get, since conferences provide those figures straight to Deloitte.
All these elements get rolled into a Student Athlete Score that, according to Deloitte, is “utilized in the calculation of the fair market range for the specific deal.”
Here are the two diagrams detailing data sources and Student Athlete Score.
The “Impact” score also includes metrics like the position athletes play, as well as their actual performance.
That’s where things get a little tricky.
The NCAA bans “pay-to-play.” In the NIL era, athletes technically aren’t being compensated for their efforts on the field, but for the weight of their name.
It’s impossible to truly remove performance from how much someone values you as a marketing figure. Obviously, the better you are, the more you are worth.
Student-athletes are not supposed to be incentivized in contracts with certain benchmarks. However, the only objective way to determine performance is through statistical analysis.
In slides outlining how the process will work with a generic women’s basketball player, Deloitte cites on-court numbers, such as whether someone is a starter or benchwarmer, and what percentile they rank nationally for points and assists per game.
For a hypothetical wide receiver, Deloitte cites a more generic “top 40% performance.”
One reason you probably shouldn’t hire a business consultant for sports matters is that Deloitte uses a picture of a quarterback for its analysis of a wide receiver.
That entire model is then mashed against a Payor score, which analyzes the company the deal is coming from, calculating whether they have proper business alignment with the player.
The generic wide receiver received a low business alignment score over concerns about the Payor not being a regional fit for the player and program.
It also looks at the company’s history of past deals, flagging when it’s previously been spotted paying above what Deloitte has determined is fair market value. Under the auspices of ensuring money doesn’t flow shadily, what you can see happening is NILGo proactively capping the amount student-athletes can make.
Will AI be essential in this process? You bet. Deloitte pledges its model will become smarter over time, consulting buzzwords for AI.
By “retroactively flagging deals that we know are not compliant,” the algorithm “can eventually train the model to recognize and flag more advanced patterns that indicate compliance issues.”
The PwC deck is titled “Designing and Managing the NIL Clearinghouse,” and though the firm didn’t win the bid, it is more explicit in how a proprietary AI would be used to evaluate deals.
PwC’s deck also goes deeper on fair market value. It states its clearinghouse would determine a specific fair market value for any deal. Once that is determined, there would be an acceptable range on either end
“Deals will have a specified fair market value (e.g., $100K) and the Clearinghouse will automatically approve deals valued above or below the fair market value within a certain Percent threshold (e.g. 10-20%) to ensure fairness.”
The PwC slides also show the backend interfaces for administrators at schools and corporate sponsors, where they can view deals for specific players and the overall marketplace.
It, too, would all have been guaranteed by AI, just like Deloitte’s.
“The use of AI and PwC's 3rd-party-role ensure fully objective review process that will not be skewed to disadvantage any involved parties.”
So rest assured, college athletes. Computers are in complete control of your financial future.
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