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Baylin Brooks

#7Baylin Brooks

Baylin Brooks is a Versatile WR for Western Michigan. Baylin's 2025 season produced 19.9 total EPA across 33 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 Baylin Brooks'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
27 Receptions372 Rec yards0 Rec TD13.8 Yards/rec

Performance Analysis · 2025 · vs WR peers

  • Efficiency100
  • Volume12
  • Explosiveness45
  • Consistency65
  • Pass-Down96
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 consistency65th %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.
  • Peak game: 1.72 EPA/play in Wk 5 vs Rhode Island.

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

+1.7201.72Wk 1 vs Michigan State: +1.66 EPA/play1Wk 2 vs North Texas: +0.09 EPA/play2Wk 3 vs Illinois: +0.54 EPA/play3Wk 4 vs Toledo: -0.95 EPA/play4Wk 5 vs Rhode Island: +1.72 EPA/play5Wk 6 vs Massachusetts: +1.18 EPA/play6Wk 7 vs Ball State: +0.21 EPA/play7Wk 9 vs Miami (OH): +0.44 EPA/play9Wk 10 vs Central Michigan: +0.12 EPA/play10Wk 12 vs Ohio: +1.38 EPA/play12Wk 14 vs Eastern Michigan: +0.15 EPA/play14Wk 15 vs Miami (OH): +1.17 EPA/play15
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1@Michigan StateL6-23-3.438528.30421.66
2vsNorth TexasL30-3313.8155.0050.09
3@IllinoisL0-3812.93248.00150.54
4vsToledoW14-136.0155.005-0.95
5vsRhode IslandW47-1423216.00161.72
6@MassachusettsW21-3-36.633612.00151.18
7vsBall StateW42-0-23.03279.00180.21
9@Miami (OH)L17-26-3.422010.00120.44
10vsCentral MichiganW24-21-8.824221.00300.12
12vsOhioW17-13-4.022613.00161.38
14@Eastern MichiganW31-21-14.712222.00220.15
15vsMiami (OH)W23-13-3.423216.00241.17
20vsKennesaw StateW41-6-5.42168.009

Usage & Situational · Pro

Snap-share proxy
Overall
4.2%
Passing plays
11.7%
Rushing plays
0.0%
Standard downs
3.6%
Passing downs
5.5%
EPA by down type
Standard downs
0.49
Passing downs
0.77
Pass / Rush EPA
0.60 / —

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.