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Lofton Howard

#19Lofton Howard

Lofton Howard is a Versatile TE for Western Kentucky.

What projects, and what doesn't · TEs · 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)0.42
    Beats guessing the TE average by 9%. n=2,225 TE seasons
  • EPA per play (efficiency)0.04
    Not projectable — we do not forecast this. n=2,016 TE seasons
  • Total EPA (value)0.46
    Beats guessing the TE average by 13%. n=2,016 TE seasons

Across all positions, usage carries year to year at 0.47 and efficiency at 0.07. So Lofton Howard'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
3 Receptions10 Rec yards0 Rec TD3.3 Yards/rec

Air yards

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

6
Targets
0.8y
Avg depth
2.4y
After catch
83%
Catch rate

The ball travels 0.8 yards in the air on an average target 8.1 yards shorter than the median. Another 2.4 comes after the catch.

Where he is targeted

targets · completion rate · yards
deep
0
0
0
short
0
0
6
83% · 17y
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 TE peers

  • Efficiency0
  • Volume6
  • Explosiveness0
  • Consistency82
  • Pass-Down93
Player type
Versatile TE

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 consistency82th %ile · above avg
Key findings
  • Below-average efficiency vs TE peers — value comes through volume, not per-play impact.
  • Limited usage share suggests a rotational or specialist role.
  • High game-to-game consistency — reliable floor each week.
  • Particularly dangerous on passing downs — efficiency spikes in obvious pass situations.

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 · TE · 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
A.J. DoyleMassachusetts0.0400.02.1
Robby PreckelNorthwestern0.0600.12.5
Blake BellOklahoma0.0700.14.4
Kaden FeaginIllinois0.0400.04.6
Jaheim BellSouth Carolina0.1100.08.0

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

Game Log · box score + EPA, week by week

+0.6300.63Wk 8 vs Florida International: -0.14 EPA/play8Wk 9 vs Louisiana Tech: -0.63 EPA/play9Wk 10 vs New Mexico State: -0.27 EPA/play10
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
8vsFlorida InternationalL6-25-10.5133.003-0.14
9@Louisiana TechW28-27-1.3122.002-0.63
10vsNew Mexico StateW35-16-15.5155.005-0.27

Usage & Situational · Pro

Snap-share proxy
Overall
2.1%
Passing plays
3.8%
Rushing plays
0.0%
Standard downs
2.0%
Passing downs
2.3%
EPA by down type
Standard downs
-0.42
Passing downs
-0.16
Pass / Rush EPA
-0.32 / —

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.