The Beat

NIL Valuation Methodology

v1.2 · Published May 2026

Most NIL valuations are black boxes. Ours isn't. Here's exactly how we value college quarterbacks — inputs, assumptions, known gaps, and where we've been wrong.

Last updated: May 4, 2026


How It Works

The Formula

PPA Pass

Points added per attempt

Usage Pass

Volume adjustment

SP+ Rating

Team quality factor

×

NIL Valuation Est.

NIL Est. = team_budget
× QB_positional_share
× ( wepa_pass ÷ league_avg_wepa )
× usage_adjustment × sp_plus_factor

floor $75K · ceiling $15M

The formula estimates a quarterback's NIL value by starting with what a program can spend, isolating the QB's typical share, then adjusting up or down based on efficiency (PPA), volume (usage), and the team's overall strength (SP+). A great QB on a mediocre team is worth less than the same player on a playoff contender.


The Inputs

Data Sources & Variables

Input
Source
Description
Value
Team BudgetNIL Collective ReportsEstimated total NIL spending capacity for the program$2M–$20M
QB Positional ShareHistorical AnalysisPercentage of NIL budget typically allocated to QB position25%–35%
WEPA PassCFB ReferenceWin Expected Points Added per pass attempt (opponent-adjusted)−0.10 to 0.73
League Avg WEPACFB ReferenceFBS-wide median WEPA for qualifying QBs (dynamic baseline)~0.35
Usage Pass AdjustmentDerivedMultiplier based on passing volume relative to league average0.8–1.3
SP+ RatingESPN/Bill ConnellyTeam quality rating used as program-context multiplier−15 to +30

WEPA (Win Expected Points Added) adjusts raw passing efficiency for opponent strength — a QB posting big numbers against weak defenses is discounted accordingly. SP+ provides program-level context: the same efficiency on a playoff contender is worth more than on a rebuilding program.

Raw PPA pass is retained as a secondary diagnostic metric in the data pipeline but is no longer the primary formula input. WEPA is the opponent-adjusted successor.


Validation

How Does It Rank Known Players?

Rather than comparing predictions to a handful of reported deals, we validate by checking whether the model produces sensible orderings for players whose market value is publicly discussed. WEPA-adjusted rankings shift roughly 30% of the top 50 vs. raw PPA — the reordering is the model working as intended.

RankPlayerTeamNIL Est.
1Will HowardOhio State$15.0M (ceiling)
2Quinn EwersTexas$15.0M (ceiling)
3Carson BeckGeorgia$14.5M
4Dillon GabrielOregon$12.1M
5Jalen MilroeAlabama$12.1M
6Garrett NussmeierLSU$11.9M
7Drew AllarPenn State$10.1M
8Miller MossUSC$9.9M
9Cameron WardMiami$9.2M
10Nico IamaleavaTennessee$9.1M
11Jaxson DartOle Miss$8.7M
12Riley LeonardNotre Dame$8.6M
13Davis WarrenMichigan$8.5M
14Cade KlubnikClemson$8.2M
15Marcel ReedTexas A&M$8.0M
16Jack TuttleMichigan$6.5M
17Payton ThorneAuburn$6.4M
18Gunner StocktonGeorgia$6.3M
19Josh HooverTCU$6.2M
20Ethan GarbersUCLA$5.8M

Two players hit the $15M ceiling — this is a known model behavior at the intersection of elite WEPA, high usage, and top-tier program budgets. The ceiling prevents runaway outputs; a social premium signal (planned for v2.0) would further differentiate the top.

Notable Results

Arch Manning · #30 · $4.7M

Backup snaps, small sample — confidence flagged low. Model correctly discounts limited usage. Sanity check: PASS.

Shedeur Sanders · #21 · $5.8M

Our model values him at $5.8M based on efficiency and program budget. Reported deals suggest $4–5M. The gap is now inverted from v1.1 — social brand premium creates complexity in both directions.

Jaxson Dart · #11 · $8.7M

The biggest v1.1 → v1.2 mover. Raw PPA undervalued him against Ole Miss's schedule; WEPA corrects for opponent strength and surfaces his efficiency accurately.


What We Don't Model

Known Limitations

Social following & brand premium

Players like Shedeur Sanders command premiums far beyond on-field value. We call this the 'Shedeur Sanders problem.'

NIL collective variability by school

Some collectives are better funded, organized, or aggressive than others. We use estimates, not actuals.

Transfer destination premium

A QB transferring to a blue-blood may command more than the same player staying at a G5 school.

FCS-origin players

Limited data availability for players transferring up from FCS programs.

In-season performance changes

Valuations are point-in-time. A breakout or benching mid-season isn't reflected until next refresh.

Option offense programs excluded

Quarterbacks at triple-option and heavy-option programs (Air Force, Navy, Army, Georgia Tech) are excluded from v1.1 valuations. These offenses produce structurally atypical pass PPA distributions — every pass attempt is high-leverage by design — making direct comparison with pro-style and spread offense QBs unreliable.

Minimum usage threshold

Only quarterbacks with a pass usage rate of 15% or higher receive a valuation. Below this threshold, sample sizes are too small to produce meaningful PPA estimates — typically backups and emergency QBs with fewer than 20 pass attempts.

Valuation floor

All valuations are floored at $75,000. Any FBS quarterback with qualifying snaps has real NIL market value from local partnerships, camp appearances, and autograph signings regardless of on-field production.

Dual-threat and scramble value

WEPA captures passing efficiency only. Quarterbacks whose value comes from designed runs or broken-play scrambles — Jalen Milroe, Marcel Reed — may be undervalued on efficiency alone. Rush PPA is captured in the data pipeline and is a v2.0 candidate.

These gaps are intentional starting points, not oversights. Version 2 will address social premium. See the changelog.


Beyond the Model

Model Philosophy

A framework is only as strong as the principles behind it.

What we optimize for: Our model prioritizes signal extraction over comprehensiveness. We'd rather be directionally right on 80% of players than precisely wrong on 100%. This means deliberately excluding variables that add noise — however popular they might be in fan discourse.

The tradeoffs we accept: Simplicity over completeness. A model with 6 inputs that we understand deeply beats a 60-variable black box every time. We sacrifice some edge cases (the Shedeur Sanders problem) for broad reliability across the position group.

Transparency as a core principle: Every number we publish can be traced back to its source. We show our work not because we have to, but because we believe the NIL space needs more accountability and less speculation. If our methodology is flawed, we want to know — and we want you to be able to tell us why.

Long-term vision: NIL valuation today resembles baseball scouting pre-Moneyball: heavy on gut feel, light on rigor. Our goal isn't to replace human judgment but to give it a foundation. Over time, as more deal data becomes public and position-specific models mature, we expect accuracy to improve — and we'll document every step of that evolution.

During early backtesting, the model identified a starting quarterback whose efficiency metrics — passing PPA, usage rate, and team offensive context — placed his market value significantly above prevailing estimates at the time. Within weeks of that analysis, the player transferred to a program with one of the country's largest NIL collectives and signed a reported deal consistent with the model's projection. Performance tells the story before the market catches up.

Core Principles

Signal Over Noise

We prioritize metrics with demonstrated predictive value over popular but unreliable indicators.

Transparency First

Every assumption, data source, and limitation is documented. If we can't explain it, we don't include it.

Iterative Improvement

This model will evolve. We backtest, admit errors publicly, and ship improvements quarterly.