Building ExpertNIL: Bringing Data to a Fragmented NIL Market
Why I built a platform around athlete valuation, comparable deals, and roster-level decision-making
Updated August 25, 2026
When I started ExpertNIL, one thing about the NIL market stood out: everyone was talking about what athletes were worth, but there was very little reliable information behind those numbers. Reported deals were scattered, terms were often unclear, and programs were making increasingly important roster decisions without much of a market benchmark.That problem became even more important as college athletics entered the revenue-sharing era. Schools were no longer just thinking about individual NIL deals. They had to think about how athlete compensation fit across an entire roster and budget. To me, that looked less like a marketing problem and more like a valuation and capital allocation problem.
What I Built
ExpertNIL grew into a college athletics valuation and intelligence platform built on more than 100,000 structured player, performance, recruiting, and NIL data points. The goal was simple: take a market that was difficult to compare and make it easier to analyze.I built a proprietary valuation model that weighed factors including player performance, position, recruiting profile, and historical NIL transactions. Because reported NIL information was not equally reliable, I also built confidence scoring into the model rather than treating every reported deal as fact.I then developed a comparable-deal engine that identified similar athletes and historical NIL agreements, allowing reported values to be benchmarked against the broader market. I also built program-facing dashboards for budget visibility, deal tracking, and roster-level analysis so individual valuations could be viewed in the context of broader spending decisions.
Turning Imperfect Data Into Better Decisions
The hardest part was not just collecting the data. It was deciding what information deserved weight, how much confidence to place in incomplete reports, and how to turn that information into something useful.Building ExpertNIL forced me to think through comparables, assumptions, budget constraints, and the reliability of the underlying data rather than simply repeating headline deal values. It taught me how to take an unstructured problem, build a framework around it, and defend the assumptions behind the answer.
ExpertNIL Acquisition
In 2026, ExpertNIL was acquired by a strategic buyer.
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