What projects, and what doesn't · QBs · held out 2019-2025
How well one season predicts the next, measured on seasons the model never trained on. 1.00 would be perfectly predictable; 0.00 means last year told us nothing.
- Usage share (volume)0.46Beats guessing the QB average by 16%. n=2,421 QB seasons
- EPA per play (efficiency)0.05Not projectable — we do not forecast this. n=1,965 QB seasons
- Total EPA (value)0.51Beats guessing the QB average by 20%. n=1,965 QB seasons
Across all positions, usage carries year to year at 0.47 and efficiency at 0.07. So Jason Wright's projection is a projection of opportunity — how much of the offense he runs through. How well he converts it is something this model does not claim to know a year in advance, and the number above is why.
2025 Production
Performance Analysis · 2025 · vs QB peers
- Efficiency0
- Volume18
- Dual-Threat15
- Consistency0
- Clutch6
High-rep offense that leans on this QB's arm — value comes through cumulative production across a high number of plays.
- ▸Below-average efficiency vs QB peers — value comes through volume, not per-play impact.
- ▸Limited usage share suggests a rotational or specialist role.
- ▸High game-to-game variance — boom-or-bust profile.
NIL Market Tier· 2025
On3 valuation ↗Rotational contributor. Smaller collective or local deals.
Tier is a model estimate based on position, school brand, performance rank, and usage — not a reported deal. NIL deals are private. For a real market valuation, see On3's NIL profile, which factors in social following and actual deal tracking.
Game Log · box score + EPA, week by week
Usage & Situational · Pro
- Standard downs
- -0.04
- Passing downs
- -0.22
- Pass / Rush EPA
- -0.65 / -0.02
Usage = share of team plays (CFBD has no true snap counts).