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Malcolm Simmons

#11Malcolm Simmons

Malcolm Simmons is a Vertical Threat WR for Auburn. Malcolm's 2025 season produced 24.3 total EPA across 42 plays.

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 Malcolm Simmons'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
25 Receptions457 Rec yards2 Rec TD18.3 Yards/rec
Returns
16 Punt returns60 PR yards0 PR TD

Performance Analysis · 2025 · vs WR peers

  • Efficiency100
  • Volume18
  • Explosiveness75
  • Consistency67
  • Pass-Down100
Player type
Vertical Threat WR

Elite deep receiver who stretches the field. Wins downfield, commands safety attention, and creates the threat that opens underneath routes.

Downfield threatYAC upsideCreates space for teammates
Peer percentiles
Opponent-adjusted EPA (WEPA/play)100th %ile · elite
Game-to-game consistency67th %ile · average
Key findings
  • Top-10% efficiency among WRs — elite opponent-adjusted EPA rate.
  • Limited usage share suggests a rotational or specialist role.
  • 7 high-impact games (EPA/play > 0.4) this season — elite ceiling.
  • Particularly dangerous on passing downs — efficiency spikes in obvious pass situations.
  • Strong second-half surge — EPA/play improved 0.58 from the first to second half of the season.
  • Peak game: 2.36 EPA/play in Wk 14 vs Alabama (SP+ 15).

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 · 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
Quadree HendersonPittsburgh0.5301.633.4
DeAndre HughesAir Force0.4701.328.2
Savion WilliamsTCU0.4501.125.2
Keytaon ThompsonMississippi State0.4601.232.2
Javion PoseyFlorida Atlantic0.4501.423.8

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

Game Log · box score + EPA, week by week

+2.3602.36Wk 1 vs Baylor: +1.19 EPA/play1Wk 2 vs Ball State: +0.23 EPA/play2Wk 4 vs Oklahoma: +0.50 EPA/play4Wk 5 vs Texas A&M: -0.54 EPA/play5Wk 7 vs Georgia: +0.74 EPA/play7Wk 8 vs Missouri: +0.79 EPA/play8Wk 9 vs Arkansas: -0.05 EPA/play9Wk 11 vs Vanderbilt: +1.28 EPA/play11Wk 13 vs Mercer: +0.41 EPA/play13Wk 14 vs Alabama: +2.36 EPA/play14
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1@BaylorW38-241.412626.00261.19
2vsBall StateW42-3-23.04379.30220.23
4@OklahomaL17-2418.34307.50150.50
5@Texas A&ML10-1620.7273.506-0.54
7vsGeorgiaL10-2024.1122.0020.74
8vsMissouriL17-2314.40.79
9@ArkansasW33-245.13175.709-0.05
10vsKentuckyL3-101.8
11@VanderbiltL38-4520.324623.00391.28
13vsMercerW62-17514929.81910.41
14vsAlabamaL20-2714.8314347.71662.36

Usage & Situational · Pro

Snap-share proxy
Overall
6.2%
Passing plays
11.7%
Rushing plays
1.1%
Standard downs
4.6%
Passing downs
9.7%
EPA by down type
Standard downs
0.14
Passing downs
1.02
Pass / Rush EPA
0.50 / 1.34

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.