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Grant Stec

#85Grant Stec

Grant Stec is a Versatile TE for Wisconsin.

What projects, and what doesn't · TEs · 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)0.42
    Beats guessing the TE average by 9%. n=2,225 TE seasons
  • EPA per play (efficiency)0.04
    Not projectable — we do not forecast this. n=2,016 TE seasons
  • Total EPA (value)0.46
    Beats guessing the TE average by 13%. n=2,016 TE seasons

Across all positions, usage carries year to year at 0.47 and efficiency at 0.07. So Grant Stec'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

Receiving
5 Receptions52 Rec yards0 Rec TD10.4 Yards/rec

Performance Analysis · 2025 · vs TE peers

  • Efficiency100
  • Volume7
  • Explosiveness23
  • Consistency69
  • Pass-Down50
Player type
Versatile TE

Balanced profile without a single dominant trait — contributes across multiple dimensions.

Balanced usageMulti-role
Peer percentiles
Opponent-adjusted EPA (WEPA/play)100th %ile · elite
Game-to-game consistency69th %ile · average
Key findings
  • Top-10% efficiency among TEs — elite opponent-adjusted EPA rate.
  • Limited usage share suggests a rotational or specialist role.
  • 3 high-impact games (EPA/play > 0.4) this season — elite ceiling.
  • Peak game: 1.96 EPA/play in Wk 6 vs Michigan (SP+ 12).

NIL Market Tier· 2025

On3 valuation ↗
Star

Top-10 nationally. Multiple mid-to-large collective deals expected.

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.

Historical Comparables · TE · efficiency + volume + value

Players from 2013–2025 matched on EPA efficiency, play volume, and adjusted value tier — not just one metric.

PlayerTeamWEPA/playLine valTotal EPA
Ty ThompsonTulane0.4200.720.2
Seth GreenMinnesota0.3900.032.4
Johnny LanganRutgers0.3000.017.7
Jordan MyersRice0.2700.215.9
Evan SvobodaWyoming0.2900.521.8

Comps are statistical — efficiency, volume, and value tier all factor in. Style and conference context differ.

Game Log · box score + EPA, week by week

+1.9601.96Wk 1 vs Miami (OH): +1.16 EPA/play1Wk 3 vs Alabama: +1.25 EPA/play3Wk 4 vs Maryland: -0.64 EPA/play4Wk 6 vs Michigan: +1.96 EPA/play6
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1vsMiami (OH)W17-0-3.423115.50221.16
3@AlabamaL14-3814.8199.0091.25
4vsMarylandL10-270.6155.005-0.64
6@MichiganL10-2412.4177.0071.96

Usage & Situational · Pro

Snap-share proxy
Overall
2.6%
Passing plays
5.9%
Rushing plays
0.0%
Standard downs
3.4%
Passing downs
0.7%
EPA by down type
Standard downs
0.44
Passing downs
Pass / Rush EPA
0.44 / —

Usage = share of team plays (CFBD has no true snap counts).

EPA = expected points added (opponent-adjusted). NIL estimates are model-based ranges, not reported deals. Data: CollegeFootballData. Not betting advice.