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Jaquise Martin

#10Jaquise Martin

Jaquise Martin is a Slot Specialist WR for Houston.

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 Jaquise Martin'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
5 Receptions69 Rec yards0 Rec TD13.8 Yards/rec
Returns
2 Kick returns17 KR yards0 KR TD

Performance Analysis · 2025 · vs WR peers

  • Efficiency67
  • Volume7
  • Explosiveness45
  • Consistency24
  • Pass-Down72
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)67th %ile · average
Game-to-game consistency24th %ile · below avg
Key findings
  • Limited usage share suggests a rotational or specialist role.
  • High game-to-game variance — boom-or-bust profile.
  • Particularly dangerous on passing downs — efficiency spikes in obvious pass situations.
  • Production faded as the season progressed — 1.68 EPA/play decline from first to second half.
  • Peak game: 3.66 EPA/play in Wk 10 vs West Virginia (SP+ -7).

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 · 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
Tyler BuchnerNotre Dame0.2500.312.8
Brandon ChatmanNavy0.2300.612.0
Justin LynchTemple0.2400.315.1
Samajie GrantArizona0.2500.315.5
Amare JonesTulane0.2300.813.3

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

Game Log · box score + EPA, week by week

+3.6603.66Wk 7 vs Oklahoma State: -0.21 EPA/play7Wk 9 vs Arizona State: +0.41 EPA/play9Wk 10 vs West Virginia: +3.66 EPA/play10Wk 11 vs UCF: -0.72 EPA/play11Wk 13 vs TCU: -0.07 EPA/play13
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
7@Oklahoma StateW39-17-15.111313.0013-0.21
9@Arizona StateW24-163.912020.00200.41
10vsWest VirginiaL35-45-6.812626.00263.66
11@UCFW30-27-1.2133.003-0.72
13vsTCUL14-178.3177.007-0.07
14@BaylorW31-241.4

Usage & Situational · Pro

Snap-share proxy
Overall
2.3%
Passing plays
5.4%
Rushing plays
0.0%
Standard downs
1.6%
Passing downs
3.9%
EPA by down type
Standard downs
0.14
Passing downs
0.27
Pass / Rush EPA
0.21 / —

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.