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Gino Campiotti

#13Gino Campiotti

TE·Massachusetts·20220.7 pts line value1st Round

Gino Campiotti is a Workhorse Receiver TE for Massachusetts. Gino's 2022 season ranks in the 50th percentile nationally by opponent-adjusted EPA per play across 163 plays — a average rate for the TE.

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 Gino Campiotti'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.

2022 Production

Passing
33/74 Comp/Att257 Pass yards1 Pass TD6 INT44.6% Comp %
Rushing
390 Rush yards3 Rush TD102 Carries3.8 Yards/carry

Performance Analysis · 2022 · vs TE peers

  • Efficiency50
  • Volume77
  • Explosiveness0
  • Consistency31
  • Pass-Down67
Player type
Workhorse Receiver TE

The primary target in the offense — used across all situations and down-types at an elite volume.

Top target shareAll-down utilityOffense runs through this player
Peer percentiles
Opponent-adjusted EPA (WEPA/play)50th %ile · average
Game-to-game consistency31th %ile · below avg
Key findings
  • High game-to-game variance — boom-or-bust profile.
  • Production faded as the season progressed — 0.15 EPA/play decline from first to second half.

NIL Market Tier· 2022

On3 valuation ↗
Elite

Top-3 player at position nationally. Collective + national brand 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 LanganRutgers0.2400.028.1
Heinrich HaarbergNebraska0.2600.629.1
Connor BlumrickTexas A&M0.2300.024.4
Cole GautscheNew Mexico0.1701.017.7
Kaden FeaginIllinois0.1800.017.6

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

Game Log · EPA per play, week by week

+0.3100.31Wk 1 vs Tulane: +0.15 EPA/play1Wk 2 vs Toledo: +0.06 EPA/play2Wk 3 vs Stony Brook: +0.27 EPA/play3Wk 4 vs Temple: +0.15 EPA/play4Wk 5 vs Eastern Michigan: +0.13 EPA/play5Wk 6 vs Liberty: -0.31 EPA/play6Wk 7 vs Buffalo: +0.18 EPA/play7Wk 10 vs UConn: -0.20 EPA/play10Wk 12 vs Texas A&M: +0.07 EPA/play12Wk 13 vs Army: +0.29 EPA/play13
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+EPA/playPassRush
1@TulaneL10-4212.40.150.15
2@ToledoL10-55-3.70.060.010.14
3vsStony BrookW20-30.270.410.09
4@TempleL0-28-12.90.15-0.180.48
5@Eastern MichiganL13-20-7.10.130.030.21
6vsLibertyL24-42-1.5-0.31-0.66-0.09
7vsBuffaloL7-34-9.20.180.18
10@UConnL10-27-14.6-0.20-0.20
12@Texas A&ML3-209.10.070.07
13vsArmyL7-44-1.50.290.29

Usage & Situational · Pro

Snap-share proxy
Overall
26.9%
Passing plays
33.6%
Rushing plays
24.2%
Standard downs
24.6%
Passing downs
31.2%
EPA by down type
Standard downs
0.07
Passing downs
0.17
Pass / Rush EPA
0.02 / 0.18

Usage = share of team plays (CFBD has no true snap counts).

Career · rolling EPA, game by game

Per-game EPA5-game avg
+0.350−0.37
EPA per play · 2022 · 10 games

Chart shows per-game EPA (bars) and rolling 5-game average (line). Season breaks marked with dashed lines. Line value = est. points over replacement per game.

EPA = expected points added (opponent-adjusted). NIL estimates are model-based ranges, not reported deals. Data: CollegeFootballData. Not betting advice.