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Grayson Barnes

#80Grayson Barnes

Grayson Barnes is a Versatile TE for West Virginia.

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 Grayson Barnes'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
21 Receptions232 Rec yards2 Rec TD11.0 Yards/rec

Performance Analysis · 2025 · vs TE peers

  • Efficiency100
  • Volume13
  • Explosiveness27
  • Consistency61
  • Pass-Down42
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 consistency61th %ile · average
Key findings
  • Top-10% efficiency among TEs — elite opponent-adjusted EPA rate.
  • Limited usage share suggests a rotational or specialist role.
  • 5 high-impact games (EPA/play > 0.4) this season — elite ceiling.
  • Strong second-half surge — EPA/play improved 0.76 from the first to second half of the season.
  • Peak game: 3.02 EPA/play in Wk 6 vs BYU (SP+ 16).

NIL Market Tier· 2025

On3 valuation ↗
Elite

Top-3 player at position nationally. Collective + national brand 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.

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
Evan SvobodaWyoming0.2900.521.8
Jordan MyersRice0.2700.215.9

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

Game Log · box score + EPA, week by week

+3.0203.02Wk 1 vs Robert Morris: +0.37 EPA/play1Wk 2 vs Ohio: -0.61 EPA/play2Wk 3 vs Pittsburgh: +1.01 EPA/play3Wk 4 vs Kansas: -0.21 EPA/play4Wk 6 vs BYU: +3.02 EPA/play6Wk 9 vs TCU: +0.85 EPA/play9Wk 11 vs Colorado: +1.80 EPA/play11Wk 12 vs Arizona State: +0.21 EPA/play12Wk 14 vs Texas Tech: +0.76 EPA/play14
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1vsRobert MorrisW45-333712.30200.37
2@OhioL10-17-4.0122.002-0.61
3vsPittsburghW31-248.455811.61341.01
4@KansasL10-414.13206.708-0.21
6@BYUL24-3815.911515.00153.02
9vsTCUL17-238.33258.31170.85
11vsColoradoW29-22-8.312020.00201.80
12@Arizona StateL23-253.93268.70170.21
14vsTexas TechL0-4927.612929.00290.76

Usage & Situational · Pro

Snap-share proxy
Overall
4.4%
Passing plays
10.0%
Rushing plays
0.0%
Standard downs
4.0%
Passing downs
5.3%
EPA by down type
Standard downs
0.49
Passing downs
0.44
Pass / Rush EPA
0.47 / —

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.