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Ryder Kusch

#33Ryder Kusch

Ryder Kusch is a Red Zone Weapon TE for Temple.

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 Ryder Kusch'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
13 Receptions60 Rec yards3 Rec TD4.6 Yards/rec

Performance Analysis · 2025 · vs TE peers

  • Efficiency50
  • Volume12
  • Explosiveness0
  • Consistency0
  • Pass-Down0
Player type
Red Zone Weapon TE

Goes from good to great inside the 20 — high TD conversion on limited looks makes this receiver a scoring machine.

Red zone targetHigh TD rateSize/catch radius advantage
Peer percentiles
Opponent-adjusted EPA (WEPA/play)50th %ile · average
Game-to-game consistency0th %ile · below avg
Key findings
  • Limited usage share suggests a rotational or specialist role.
  • High game-to-game variance — boom-or-bust profile.
  • Strong second-half surge — EPA/play improved 0.14 from the first to second half of the season.
  • Peak game: 0.56 EPA/play in Wk 1 vs Massachusetts (SP+ -37).

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
Robby PreckelNorthwestern0.0600.12.5
A.J. DoyleMassachusetts0.0400.02.1
Blake BellOklahoma0.0700.14.4
Kaden FeaginIllinois0.1500.07.8
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

+0.9000.90Wk 1 vs Massachusetts: +0.56 EPA/play1Wk 2 vs Howard: +0.13 EPA/play2Wk 3 vs Oklahoma: -0.13 EPA/play3Wk 4 vs Georgia Tech: -0.48 EPA/play4Wk 7 vs Navy: -0.90 EPA/play7Wk 10 vs East Carolina: +0.22 EPA/play10Wk 13 vs Tulane: -0.04 EPA/play13Wk 14 vs North Texas: +0.29 EPA/play14
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1@MassachusettsW42-10-36.63144.7290.56
2vsHowardW55-7144.0040.13
3vsOklahomaL3-4218.32199.5019-0.13
4@Georgia TechL24-459.3210.502-0.48
7vsNavyL31-326.2100.000-0.90
10vsEast CarolinaL14-458.02126.0090.22
11@ArmyL13-140.8
13vsTulaneL13-376.3188.008-0.04
14@North TexasL25-5213.8122.0120.29

Usage & Situational · Pro

Snap-share proxy
Overall
4.3%
Passing plays
9.1%
Rushing plays
0.0%
Standard downs
4.0%
Passing downs
4.8%
EPA by down type
Standard downs
0.32
Passing downs
-0.30
Pass / Rush EPA
0.05 / —

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.