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Oscar Delp

#4Oscar Delp

Oscar Delp is a Versatile TE for Georgia.

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 Oscar Delp'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
20 Receptions261 Rec yards1 Rec TD13.1 Yards/rec

Performance Analysis · 2025 · vs TE peers

  • Efficiency100
  • Volume9
  • Explosiveness40
  • Consistency65
  • Pass-Down0
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 consistency65th %ile · average
Key findings
  • Top-10% efficiency among TEs — elite opponent-adjusted EPA rate.
  • Limited usage share suggests a rotational or specialist role.
  • 6 high-impact games (EPA/play > 0.4) this season — elite ceiling.
  • Production faded as the season progressed — 0.35 EPA/play decline from first to second half.
  • Peak game: 2.60 EPA/play in Wk 6 vs Kentucky (SP+ 2).

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

+2.6002.60Wk 1 vs Marshall: +1.48 EPA/play1Wk 3 vs Tennessee: +0.42 EPA/play3Wk 6 vs Kentucky: +2.60 EPA/play6Wk 7 vs Auburn: -0.47 EPA/play7Wk 8 vs Ole Miss: +0.42 EPA/play8Wk 10 vs Florida: -0.11 EPA/play10Wk 11 vs Mississippi State: +1.11 EPA/play11Wk 12 vs Texas: +0.90 EPA/play12Wk 15 vs Alabama: +0.26 EPA/play15
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1vsMarshallW45-7-4.523316.50281.48
3@TennesseeW44-4115.011818.00180.42
6vsKentuckyW35-141.813636.00362.60
7@AuburnW20-1011.6294.5014-0.47
8vsOle MissW43-3524.044210.50190.42
10vsFloridaW24-203.512020.0020-0.11
11@Mississippi StateW41-214.134113.71291.11
12vsTexasW35-1016.233612.00230.90
15vsAlabamaW28-714.82105.0050.26
20vsOle MissL34-3924.011616.0016

Usage & Situational · Pro

Snap-share proxy
Overall
3.2%
Passing plays
7.3%
Rushing plays
0.0%
Standard downs
3.7%
Passing downs
2.1%
EPA by down type
Standard downs
0.69
Passing downs
0.06
Pass / Rush EPA
0.57 / —

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.