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Kam Mikell

#85Kam Mikell

Kam Mikell is a Versatile WR for Colorado.

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 Kam Mikell'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

Rushing
75 Rush yards0 Rush TD19 Carries3.9 Yards/carry
Receiving
2 Receptions5 Rec yards0 Rec TD2.5 Yards/rec
Returns
1 Punt returns6 PR yards0 PR TD

Performance Analysis · 2025 · vs WR peers

  • Efficiency0
  • Volume22
  • Explosiveness0
  • Consistency62
  • Pass-Down58
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)0th %ile · below avg
Game-to-game consistency62th %ile · average
Key findings
  • Below-average efficiency vs WR peers — value comes through volume, not per-play impact.
  • Limited usage share suggests a rotational or specialist role.

NIL Market Tier· 2025

On3 valuation ↗
Contributor

Rotational contributor. Smaller collective or local 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
De'Michael HarrisSouthern Miss0.1300.715.2
Amare JonesTulane0.1800.012.8
Hyleck FosterMarshall0.1800.518.2
Davis BrysonKennesaw State0.2000.019.4
Brandon ChatmanNavy0.2300.612.0

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

Game Log · box score + EPA, week by week

+0.3900.39Wk 7 vs Iowa State: -0.39 EPA/play7Wk 9 vs Utah: +0.05 EPA/play9Wk 10 vs Arizona: -0.03 EPA/play10Wk 11 vs West Virginia: -0.16 EPA/play11
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
3@HoustonL20-367.4
6@TCUL21-358.3
7vsIowa StateW24-179.9-0.39
9@UtahL7-5322.2252.5060.05
10vsArizonaL17-5212.0-0.03
11@West VirginiaL22-29-6.8-0.16

Usage & Situational · Pro

Snap-share proxy
Overall
7.6%
Passing plays
2.2%
Rushing plays
13.6%
Standard downs
9.3%
Passing downs
5.2%
EPA by down type
Standard downs
-0.20
Passing downs
-0.15
Pass / Rush EPA
0.09 / -0.26

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.