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Semaj Morgan

#4Semaj Morgan

WR·UCLA·2026

Semaj Morgan is a Slot Specialist WR for UCLA.

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 Semaj Morgan'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.

2026 Production

Receiving
8 Receptions86 Rec yards0 Rec TD10.8 Yards/rec
Returns
1 Punt returns25 PR yards0 PR TD

Air yards

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

10
Targets
10.0y
Avg depth
3.2y
After catch
80%
Catch rate

The ball travels 10.0 yards in the air on an average target 1.1 yards deeper than the median. Another 3.2 comes after the catch.

Where he is targeted

targets · completion rate · yards
deep
0
2
50% · 20y
1
100% · 31y
short
1
100% · 3y
4
75% · 17y
2
100% · 15y
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 · 2026 · vs WR peers

  • Efficiency100
  • Volume24
  • Explosiveness25
  • Consistency50
  • Pass-Down100
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)100th %ile · elite
Key findings
  • Top-10% efficiency among WRs — elite opponent-adjusted EPA rate.
  • Limited usage share suggests a rotational or specialist role.
  • Particularly dangerous on passing downs — efficiency spikes in obvious pass situations.
  • Peak game: 0.36 EPA/play in Wk 2 vs San Diego State (SP+ 3).

NIL Market Tier· 2026

On3 valuation ↗
Star

Top-10 nationally. Multiple mid-to-large collective deals expected.

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
Keytaon ThompsonVirginia0.3200.412.5
Keytaon ThompsonVirginia0.3700.615.2
Micah DavisAir Force0.3700.616.3
Cade HarrisAir Force0.3300.514.8
Dane KinamonAir Force0.4100.716.0

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

Game Log · box score + EPA, week by week

+0.3600.36Wk 1 vs California: +0.34 EPA/play1Wk 2 vs San Diego State: +0.36 EPA/play2
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1@CaliforniaW45-24-3.42136.5070.34
2vsSan Diego StateW28-103.167312.20310.36

Usage & Situational · Pro

Snap-share proxy
Overall
8.3%
Passing plays
20.4%
Rushing plays
0.0%
Standard downs
3.3%
Passing downs
23.3%
EPA by down type
Standard downs
0.10
Passing downs
0.47
Pass / Rush EPA
0.36 / —

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.