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Kwazi Gilmer

#11Kwazi Gilmer

WR·UCLA·2025

Kwazi Gilmer is a Versatile WR for UCLA. Kwazi's 2025 season ranks in the 100th percentile nationally by opponent-adjusted EPA per play across 83 plays — a elite rate for the WR.

What projects, and what doesn't · WRs · 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)0.47
    Beats guessing the WR average by 16%. n=6,302 WR seasons
  • EPA per play (efficiency)0.09
    Not projectable — we do not forecast this. n=5,767 WR seasons
  • Total EPA (value)0.45
    Beats guessing the WR average by 12%. n=5,767 WR seasons

Across all positions, usage carries year to year at 0.47 and efficiency at 0.07. So Kwazi Gilmer'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
50 Receptions535 Rec yards4 Rec TD10.7 Yards/rec

Performance Analysis · 2025 · vs WR peers

  • Efficiency100
  • Volume35
  • Explosiveness25
  • Consistency39
  • Pass-Down100
Player type
Versatile WR

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 consistency39th %ile · below avg
Key findings
  • Top-10% efficiency among WRs — elite opponent-adjusted EPA rate.
  • High game-to-game variance — boom-or-bust profile.
  • 4 high-impact games (EPA/play > 0.4) this season — elite ceiling.
  • Particularly dangerous on passing downs — efficiency spikes in obvious pass situations.
  • Production faded as the season progressed — 0.16 EPA/play decline from first to second half.
  • Peak game: 0.80 EPA/play in Wk 6 vs Penn State (SP+ 18).

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 · WR · 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
Malik DunnerBall State0.2900.422.0
Noah ShortArmy0.3000.520.7
Samajie GrantArizona0.2500.315.5
Justin LynchTemple0.2400.315.1
Chris TyreeNotre Dame0.3100.623.6

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

Game Log · box score + EPA, week by week

+0.8000.80Wk 1 vs Utah: +0.12 EPA/play1Wk 2 vs UNLV: +0.44 EPA/play2Wk 3 vs New Mexico: +0.41 EPA/play3Wk 5 vs Northwestern: +0.11 EPA/play5Wk 6 vs Penn State: +0.80 EPA/play6Wk 7 vs Michigan State: -0.30 EPA/play7Wk 8 vs Maryland: +0.58 EPA/play8Wk 9 vs Indiana: -0.57 EPA/play9Wk 11 vs Nebraska: -0.05 EPA/play11Wk 12 vs Ohio State: +0.38 EPA/play12Wk 13 vs Washington: -0.05 EPA/play13Wk 14 vs USC: +0.31 EPA/play14
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1vsUtahL10-4322.233110.30140.12
2@UNLVL23-304.388710.90210.44
3vsNew MexicoL10-350.955611.20200.41
5@NorthwesternL14-175.835117.01290.11
6vsPenn StateW42-3718.157915.81430.80
7@Michigan StateW38-13-3.4294.505-0.30
8vsMarylandW20-170.645614.00180.58
9@IndianaL6-5632.411313.0013-0.57
11vsNebraskaL21-286.22136.508-0.05
12@Ohio StateL10-4830.14379.31180.38
13vsWashingtonL14-4818.433010.0015-0.05
14@USCL10-2916.910737.31110.31

Usage & Situational · Pro

Snap-share proxy
Overall
12.1%
Passing plays
23.5%
Rushing plays
0.0%
Standard downs
11.5%
Passing downs
13.4%
EPA by down type
Standard downs
0.04
Passing downs
0.69
Pass / Rush EPA
0.28 / —

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.