The NIL/Transfer Portal Era of college football has evolved to include direct monetary payments from schools to players. At the upper end of the market, some team rosters are expected to receive as much as $40M in total compensation (direct payments, NIL deals, etc.) in 2025.
Based on public reporting, online references, and research articles, we estimate the total 2025 FBS market cap (i.e., the total compensation to be received by 15,000+ FBS players) to be approximately $1.9B.
Combining the 2025 market cap with the pioneering analytical rating systems proprietary to MCILLECE SPORTS, all experienced FBS players can be ascribed fair market $ valuations for the 2025 season, rounded to and reported in the thousands in the lookup table below:
| TeamID | Team | Order | POS | Player | Eff1 | Sigma1 | Eff2 | Sigma2 | 2025 WGT | Valuation |
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1 | |||||||||
| TeamID | Team | Order | Player | |||||||
| ∑ = 0 |
Column Information
- TeamID: Numeric team identifier. Useful for filtering out teams with similar names, such as Michigan and Western Michigan.
- Team: All 136 FBS teams are included.
- Order: Default position (POS) ordering, from offense to defense to special teams. Use the Order filter to review position groups.
- POS: Position group. TE includes FBs and H-Backs. PK comprises FGs and PATs.
- Player: 2025 roster player assigned nonzero usage rate in lineup optimization (see 2025 WGT below). Only players with D1 college football experience are explicitly listed. Unspecified roster players are listed as “—“. Some roster errors inevitably occur, and injured players are not removed.
- Eff1: Efficiency rate for rush offense, rush defense, or special teams categories. Efficiency measures the % of the player’s total win probability effects that are positive.
- Sigma1: Standard deviation of the 2025 Eff1 projection. Smaller (more precise) for more experienced players.
- Eff2: Efficiency Rate for pass offense and pass defense categories. This column does not apply to special teams, so all entries are zero in those rows.
- Sigma2: Standard deviation of the 2025 Eff2 projection. About 95% of players will finish 2025 within two standard deviations of the corresponding Efficiency Rate estimate.
- 2025 WGT: Optimum usage/impact rate, based on roster composition, coaching systems, and position group constraints. See discussion below for more information.
- Valuation ($ thousands): Estimated fair market value for the 2025 season, rounded to and reported in the thousands. These are not predictions of actual compensation received. See discussion below for more information. Totals at the bottom reflect the sum of all players selected by the current filters, an easy way to view total team or position group valuations.
The $ valuations are dependent on 2025 win/loss impact and rely heavily on player ratings derived from actual D1 CFB experience. Thus, highly-paid, high-profile recruits are only implicitly included in the other player (“—“) listings in these tables, which factor in coaching and talent but tend to have lower valuations than experienced players. There is no such thing as a “can’t-miss” prospect, and until they prove it and earn it on the D1 college football field, they won’t be highly valued monetarily here, nor should they be by athletic departments in the Transfer Portal Era.
Example 1 – Michigan QBs
5* QB recruit Bryce Underwood famously received a massive deal to sign with Michigan. But in these tables, experienced Fresno transfer Mikey Keene enters the 2025 season as the projected starter with a valuation of about $2.3M. If Underwood becomes the starter, he would effectively absorb Keene’s valuation, and he could quickly exceed it if his performance matches the hype.
Further, player values depend on modeled “optimal usage” weights (how impactful the player is expected to be in 2025) and “unit shares” (how much unit efficiency affects winning & losing games).
Optimal usage weights are derived from nonlinear, mathematical optimization algorithms that account for coaching systems, player ratings, and unit constraints (e.g., can’t throw to Jeremiah Smith every play) to maximize the chances of producing a winning team. Optimal usage weights are neither perfectly optimal (due to player rating error) nor perfectly predictive of true usage rates (because coaches think differently than mathematical models). Models make mistakes, and coaches do, too.
Example 2 – Penn State RBs
Optimal usage weights strongly prefer Nicholas Singleton (65%) as the feature back over Kaytron Allen (13%), giving Singleton a much higher valuation. However, the two RBs will likely split time about equally in 2025, which would balance out their valuations closer to a 50/50 split of their combined preseason valuation of $4.8M.
Unit shares are derived from all 136 FBS staff offensive, defensive, and special teams systems, informed by the detailed statistical histories of over 6,000 D1 college football coaches. [For more information about coaching systems, see the Coach and Staff Ratings pages in the tabs above.]
Lastly, adjustments can be made based on changing the market cap or adding nominal $ values to noncontributory 2025 players.
Ultimately, while imperfect, we believe these Valuation tables are groundbreaking, providing ADs, coaches, players, and fans with a comprehensive, systematic, analytical approach to fairly estimate roster compensation values, and we are confident there is no comparable resource available.
Please direct any inquiries to: analytics@mcillecesports.com