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Eric Olsen

#80Eric Olsen

Eric Olsen is a Slot Specialist TE for Oregon State.

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 Eric Olsen'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.

2026 Production

Receiving
7 Receptions57 Rec yards0 Rec TD8.1 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
7.7y
Avg depth
7.8y
After catch
67%
Catch rate

The ball travels 7.7 yards in the air on an average target 1.3 yards shorter than the median. Another 7.8 comes after the catch.

Where he is targeted

targets · completion rate · yards
deep
0
0
0
short
0
4
75% · 30y
2
50% · 24y
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 · 2026 · vs TE peers

  • Efficiency100
  • Volume22
  • Explosiveness8
  • Consistency50
  • Pass-Down100
Player type
Slot Specialist TE

The offense's primary passing-down weapon — routes, separation, and reliability on 3rd down define this role.

3rd-down converterRoute technicianHigh passing-down share
Peer percentiles
Opponent-adjusted EPA (WEPA/play)100th %ile · elite
Key findings
  • Top-10% efficiency among TEs — elite opponent-adjusted EPA rate.
  • Limited usage share suggests a rotational or specialist role.
  • Particularly dangerous on passing downs — efficiency spikes in obvious pass situations.
  • Peak game: 0.89 EPA/play in Wk 1 vs Houston (SP+ 7).

NIL Market Tier· 2026

On3 valuation ↗
Star

Top-10 nationally. Multiple mid-to-large collective deals expected.

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
Johnny LanganRutgers0.3000.017.7
Jordan MyersRice0.2700.215.9
Evan SvobodaWyoming0.2900.521.8
D'Vaughn PennamonOle Miss0.2000.011.0

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

Game Log · box score + EPA, week by week

+0.8900.89Wk 1 vs Houston: +0.89 EPA/play1Wk 2 vs Texas Tech: -0.40 EPA/play2
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1@HoustonL20-336.545413.50240.89
2vsTexas TechL24-3518.9331.004-0.40

Usage & Situational · Pro

Snap-share proxy
Overall
7.6%
Passing plays
10.7%
Rushing plays
0.0%
Standard downs
3.4%
Passing downs
13.8%
EPA by down type
Standard downs
-0.74
Passing downs
0.69
Pass / Rush EPA
0.30 / —

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.