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Kayden McGee

#6Kayden McGee

WR·UNLV·2025

Kayden McGee is a Versatile WR for UNLV.

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 Kayden McGee'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

Returns
1 Punt returns29 PR yards1 PR TD

Air yards

Where the ball goes, and how much of the gain is the throw rather than the run after it.

8
Targets
18.1y
Avg depth
4.0y
After catch
63%
Catch rate

The ball travels 18.1 yards in the air on an average target 9.2 yards deeper than the median. Another 4.0 comes after the catch.

Where he is targeted

targets · completion rate · yards
deep
1
100% · 44y
1
0% · 0y
0
short
0
5
60% · 37y
1
100% · 22y
leftmiddleright

Depth and direction come from CFBD’s passing detail, which is backfilled after the games: this covers weeks 1–2 only. Spikes, throwaways and intentional grounding are excluded before any average.

Performance Analysis · 2025 · vs WR peers

  • Efficiency0
  • Volume9
  • Explosiveness67
  • Consistency0
  • Pass-Down16
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 consistency0th %ile · below avg
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.
  • High game-to-game variance — boom-or-bust profile.
  • Peak game: 1.29 EPA/play in Wk 12 vs Utah State (SP+ -3).

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
Amare JonesTulane0.1800.012.8
Brandon ChatmanNavy0.2300.612.0
Amare JonesTulane0.2300.813.3
Eli HeidenreichNavy0.2200.815.4
Justin LynchNorthern Illinois0.2300.715.2

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

Game Log · EPA per play, week by week

+1.2901.29Wk 7 vs Air Force: -0.62 EPA/play7Wk 12 vs Utah State: +1.29 EPA/play12Wk 15 vs Boise State: -0.51 EPA/play15
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+EPA/playPassRush
6@WyomingW31-17-11.3
7vsAir ForceW51-48-3.2-0.62-1.16-0.08
10vsNew MexicoL35-400.9
12vsUtah StateW29-26-3.11.291.29
15@Boise StateL21-383.1-0.51-0.36-0.58
20vsOhioL10-17-4.0

Usage & Situational · Pro

Snap-share proxy
Overall
3.3%
Passing plays
1.4%
Rushing plays
6.1%
Standard downs
4.4%
Passing downs
1.1%
EPA by down type
Standard downs
0.05
Passing downs
-0.15
Pass / Rush EPA
-0.76 / 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.