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Darrell Gill Jr.
Darrell Gill Jr.

#15Darrell Gill Jr.

Darrell Gill Jr. is a Slot Specialist WR for Syracuse. Darrell's 2025 season ranks in the 100th percentile nationally by opponent-adjusted EPA per play across 56 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 Darrell Gill Jr.'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
32 Receptions506 Rec yards5 Rec TD15.8 Yards/rec

Performance Analysis · 2025 · vs WR peers

  • Efficiency100
  • Volume22
  • Explosiveness59
  • Consistency62
  • Pass-Down74
Player type
Slot Specialist WR

The offense's primary passing-down weapon — routes, separation, and reliability on 3rd down define this role.

3rd-down converterRoute technicianHigh passing-down share
Peer percentiles
Opponent-adjusted EPA (WEPA/play)100th %ile · elite
Game-to-game consistency62th %ile · average
Key findings
  • Top-10% efficiency among WRs — elite opponent-adjusted EPA rate.
  • Limited usage share suggests a rotational or specialist role.
  • 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 — 0.36 EPA/play decline from first to second half.
  • Peak game: 1.01 EPA/play in Wk 3 vs Colgate.

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
Xavier WhiteTexas Tech0.3000.518.6
Ainias SmithTexas A&M0.3100.517.0
Noah ShortArmy0.3000.520.7
Nick NashSan José State0.3100.514.0
Malik DunnerBall State0.2900.422.0

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

Game Log · box score + EPA, week by week

+1.0101.01Wk 1 vs Tennessee: +0.25 EPA/play1Wk 2 vs UConn: +0.53 EPA/play2Wk 3 vs Colgate: +1.01 EPA/play3Wk 4 vs Clemson: +0.40 EPA/play4Wk 5 vs Duke: +0.38 EPA/play5Wk 6 vs SMU: +0.14 EPA/play6Wk 8 vs Pittsburgh: +0.03 EPA/play8Wk 9 vs Georgia Tech: +0.49 EPA/play9Wk 10 vs North Carolina: -0.63 EPA/play10Wk 11 vs Miami: +0.23 EPA/play11Wk 13 vs Notre Dame: +0.64 EPA/play13
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1vsTennesseeL26-4515.023316.50260.25
2vsUConnW27-205.148421.00400.53
3vsColgateW66-24615225.32431.01
4@ClemsonW34-219.524120.51220.40
5vsDukeL3-386.62199.50180.38
6@SMUL18-3113.411313.00130.14
8vsPittsburghL13-308.44348.51110.03
9@Georgia TechL16-419.357915.81340.49
10vsNorth CarolinaL10-27-6.6144.004-0.63
11@MiamiL10-3820.72189.00120.23
13@Notre DameL7-7024.43299.70140.64

Usage & Situational · Pro

Snap-share proxy
Overall
7.6%
Passing plays
14.6%
Rushing plays
0.2%
Standard downs
5.2%
Passing downs
12.6%
EPA by down type
Standard downs
0.22
Passing downs
0.36
Pass / Rush EPA
0.32 / -0.92

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.