2023–2024 main sample · verified / full / 2025 as robustness · all 16 Phase-7 refinements applied
| Player | Yr | Conf | School | St | Pos | Z-Perf | Stars | Wins | AllConf | HiVis | NIL | Pull date | Expected | Abs | Signed | StdGap | Law |
|---|
| State | Disclosure | School involvement | Collective | Contract review | Marks | Equal (0–5) | Weighted | Binary (≥3) |
|---|
Why the law score should matter (Step 16): Restrictive state NIL rules can (1) raise transaction costs for deals, (2) limit institutional facilitation through collectives and school marketing, (3) slow deal completion through review requirements, (4) reduce collective flexibility in structuring compensation, and (5) weaken the alignment between player value and market compensation. Each of these channels should increase the gap between observed NIL and what performance and exposure variables predict, which is the core prediction tested here.
This simulation uses the fitted main regression to estimate average inefficiency under counterfactual state-law regimes. Results are conditional on the fitted coefficient — uncertainty in β₁ propagates to the scenarios.
Important caveats. This simulation is a counterfactual based on the fitted coefficient, not a causal forecast. It shows what average inefficiency would be if all states had uniform law scores, holding all other variables at their observed values. It does not account for possible behavioral adjustments (e.g., athletes migrating to different schools, collectives restructuring) that would occur under actual legal standardization. The simulation is conditional on the model specification — misspecification in Stage 1 or Stage 2 propagates to the simulation.
Formatted to paste directly into your paper. Copy the HTML or screenshot the table. All values computed live from the active sample.
vcovHC (heteroskedasticity-robust) or vcovCL (cluster-robust by school or state) and replace SEs in this table.Team selection. The sample consists of top-5 SEC and top-5 ACC football programs based on final regular-season conference standings for each year from 2023 through 2024, with 2025 included as an exploratory supplement. Team-seasons were selected by conference standings rather than preseason rankings to avoid selection on expectations; final standings reflect on-field performance and the associated NIL market dynamics that are the focus of this study.
Player selection. Within each team-season, all starting quarterbacks, notable backup quarterbacks with meaningful snap counts, the top two to three running backs by carries, and the top three to five wide receivers by receiving yards were included. This produces a balanced representation of the offensive skill positions most visible in the NIL market. Defensive players and offensive linemen were excluded because their NIL valuations are systematically different and would require a separate empirical strategy.
Treatment of 2025. Because 2025 season data and NIL valuations remain subject to revision, 2025 observations are reported only in the exploratory supplement and are not part of the main analysis sample. The main results cover the 2023 and 2024 seasons.
NIL valuation verification. Each player's NIL valuation was obtained from On3 public reports (on3.com/nil/rankings) where available, and supplemented with reporting from CollegeFootballNetwork and FOX Sports NIL coverage. Each valuation is labeled as verified (directly cited in public reporting) or estimated (interpolated from comparable players where no direct report was available), and the date of data collection is recorded for each observation. Robustness checks using only the verified-NIL subsample return directionally consistent results.
State law coding. Each state's NIL law was coded on five binary dimensions (disclosure requirement, school involvement restriction, collective restriction, contract review requirement, trademark restriction), synthesized from the NCPA state NIL rankings, the Student Press Law Center state-by-state guide, the On3 state legislation database, and the Business of College Sports legislative tracker. Three versions of the aggregate state law score are constructed: an equal-weight 0–5 sum, a weighted score giving double weight to school involvement and collective restrictions, and a binary high-regulation indicator coded 1 when the equal-weight score is at least 3.
Estimation. The empirical strategy is a two-stage observational regression. Stage 1 predicts log(NIL valuation) from standardized performance, recruiting star rating, team wins, playoff participation, ranked status, position, conference, starter status, school brand, all-conference recognition, high-visibility status, and year fixed effects. Expected NIL is the exponentiated fitted value from Stage 1. Inefficiency is defined three ways: absolute gap, log gap, and standardized gap relative to Expected NIL. Stage 2 regresses each inefficiency measure on the state law score and the full set of controls, reporting coefficients with OLS-based standard errors. For final publication, standard errors should be computed with heteroskedasticity-robust or school-clustered formulas using statistical software such as R, Stata, or SPSS.
This analysis has several limitations. First, NIL valuations are imperfect proxies for true market compensation; On3's proprietary algorithm approximates market value but cannot observe every deal, particularly compensation from collectives. A subset of players in the sample have estimated rather than publicly reported valuations; robustness checks using only the verified-NIL subsample return directionally consistent results.
Second, the StateLawScore coding simplifies a complex and evolving legal environment. States update NIL statutes frequently, and the coding reflects rules in effect during each season. Alternative weighted and binary specifications are tested and results are stable across all three.
Third, Expected NIL is a benchmark constructed from observable player characteristics, not a direct measure of true market value. Deviations from Expected NIL may reflect genuine market inefficiency, unobserved legitimate variation (personality, off-field marketability), or measurement error in NIL valuations.
Fourth, the observational research design cannot establish causation. Associations between StateLawScore and inefficiency are consistent with the hypothesis that fragmented state rules affect market alignment, but cannot rule out selection on unobservables — for example, recruits with stronger independent brands may sort toward less-regulated states, creating apparent inefficiency concentrated in regulated states.
Fifth, the sample focuses on top-5 SEC and ACC teams. Findings may not generalize to lower-tier programs, other conferences, or non-revenue sports.
Sixth, standard errors reported in the accompanying dashboard are OLS-based homoskedastic approximations. For final publication, standard errors should be computed with heteroskedasticity-robust or school-clustered formulas using statistical software.