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Brock Chappell

#83Brock Chappell

Brock Chappell is a Versatile TE for Louisiana.

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 Brock Chappell'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
12 Receptions108 Rec yards1 Rec TD9.0 Yards/rec

Performance Analysis · 2025 · vs TE peers

  • Efficiency100
  • Volume10
  • Explosiveness13
  • Consistency25
  • Pass-Down0
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)100th %ile · elite
Game-to-game consistency25th %ile · below avg
Key findings
  • Top-10% efficiency among TEs — elite opponent-adjusted EPA rate.
  • Limited usage share suggests a rotational or specialist role.
  • High game-to-game variance — boom-or-bust profile.
  • 3 high-impact games (EPA/play > 0.4) this season — elite ceiling.
  • Peak game: 1.88 EPA/play in Wk 7 vs James Madison (SP+ 12).

NIL Market Tier· 2025

On3 valuation ↗
Starter

Meaningful starter. Local collective + position-group 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
Johnny LanganRutgers0.2400.08.9
D'Vaughn PennamonOle Miss0.2000.011.0
Jordan MyersRice0.2700.215.9
Johnny LanganRutgers0.3000.017.7
Jackson AckerWisconsin0.2100.115.3

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

Game Log · box score + EPA, week by week

+1.8801.88Wk 2 vs McNeese: -0.20 EPA/play2Wk 4 vs Eastern Michigan: +1.03 EPA/play4Wk 5 vs Marshall: -0.63 EPA/play5Wk 7 vs James Madison: +1.88 EPA/play7Wk 10 vs South Alabama: +0.51 EPA/play10Wk 11 vs Texas State: -0.07 EPA/play11Wk 14 vs UL Monroe: -0.25 EPA/play14
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
2vsMcNeeseW34-10122.002-0.20
4@Eastern MichiganL31-34-14.733210.70111.03
5vsMarshallW54-51-4.5166.006-0.63
7@James MadisonL14-2412.3155.0151.88
10@South AlabamaW31-22-12.724422.00350.51
11vsTexas StateW42-392.33175.709-0.07
14vsUL MonroeW30-27-21.6122.002-0.25

Usage & Situational · Pro

Snap-share proxy
Overall
3.4%
Passing plays
8.4%
Rushing plays
0.0%
Standard downs
3.2%
Passing downs
3.9%
EPA by down type
Standard downs
0.61
Passing downs
-0.36
Pass / Rush EPA
0.24 / —

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.