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Scott Moore

#40Scott Moore

Scott Moore is a Versatile TE for Delaware.

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 Scott Moore'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
6 Receptions46 Rec yards1 Rec TD7.7 Yards/rec

Performance Analysis · 2025 · vs TE peers

  • Efficiency100
  • Volume7
  • Explosiveness4
  • Consistency44
  • 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 consistency44th %ile · below avg
Key findings
  • Top-10% efficiency among TEs — elite opponent-adjusted EPA rate.
  • Limited usage share suggests a rotational or specialist role.
  • Peak game: 1.23 EPA/play in Wk 14 vs UTEP (SP+ -18).

NIL Market Tier· 2025

On3 valuation ↗
Starter

Meaningful starter. Local collective + position-group 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
Robby PreckelNorthwestern0.0600.12.5
Kaden FeaginIllinois0.1500.07.8
A.J. DoyleMassachusetts0.0400.02.1
Blake BellOklahoma0.0700.14.4
Jaheim BellSouth Carolina0.1100.08.0

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

Game Log · box score + EPA, week by week

+1.2301.23Wk 8 vs Jacksonville State: +0.09 EPA/play8Wk 11 vs Louisiana Tech: +0.37 EPA/play11Wk 13 vs Wake Forest: -0.42 EPA/play13Wk 14 vs UTEP: +1.23 EPA/play14
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
8@Jacksonville StateL25-38-6.7155.0050.09
11vsLouisiana TechW25-24-1.322512.50200.37
13@Wake ForestL14-525.7166.006-0.42
14vsUTEPW61-31-17.52105.0161.23

Usage & Situational · Pro

Snap-share proxy
Overall
2.6%
Passing plays
4.3%
Rushing plays
0.0%
Standard downs
2.7%
Passing downs
2.4%
EPA by down type
Standard downs
0.24
Passing downs
-0.82
Pass / Rush EPA
0.11 / —

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.