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Bauer Sharp

#10Bauer Sharp

TE·LSU·2025

Bauer Sharp is a Versatile TE for LSU. Bauer's 2025 season produced -2.3 total EPA across 33 plays.

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 Bauer Sharp'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
24 Receptions252 Rec yards2 Rec TD10.5 Yards/rec
Returns
1 Kick returns5 KR yards0 KR TD

Performance Analysis · 2025 · vs TE peers

  • Efficiency0
  • Volume14
  • Explosiveness23
  • Consistency0
  • Pass-Down100
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 consistency0th %ile · below 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 variance — boom-or-bust profile.
  • Particularly dangerous on passing downs — efficiency spikes in obvious pass situations.
  • Strong second-half surge — EPA/play improved 0.25 from the first to second half of the season.
  • Peak game: 2.94 EPA/play in Wk 12 vs Arkansas (SP+ 5).

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

+2.9402.94Wk 1 vs Clemson: -0.40 EPA/play1Wk 2 vs Louisiana Tech: -0.15 EPA/play2Wk 3 vs Florida: +0.29 EPA/play3Wk 4 vs SE Louisiana: +1.00 EPA/play4Wk 5 vs Ole Miss: +0.02 EPA/play5Wk 7 vs South Carolina: -0.28 EPA/play7Wk 9 vs Texas A&M: -0.67 EPA/play9Wk 11 vs Alabama: -0.09 EPA/play11Wk 12 vs Arkansas: +2.94 EPA/play12Wk 13 vs Western Kentucky: +0.40 EPA/play13Wk 14 vs Oklahoma: -0.89 EPA/play14
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1@ClemsonW17-109.55183.608-0.40
2vsLouisiana TechW23-7-1.3188.008-0.15
3vsFloridaW20-103.537123.70650.29
4vsSE LouisianaW56-1057314.61231.00
5@Ole MissL19-2424.0199.0090.02
7vsSouth CarolinaW20-105.911111.0011-0.28
9vsTexas A&ML25-4920.7200.002-0.67
11@AlabamaL9-2014.83227.3013-0.09
12vsArkansasW23-225.111212.01122.94
13vsWestern KentuckyW13-101.611111.00110.40
14@OklahomaL13-1718.311717.0017-0.89
20vsHoustonL35-387.4

Usage & Situational · Pro

Snap-share proxy
Overall
4.8%
Passing plays
8.4%
Rushing plays
0.3%
Standard downs
6.1%
Passing downs
2.4%
EPA by down type
Standard downs
-0.17
Passing downs
0.38
Pass / Rush EPA
-0.08 / 0.20

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.