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Ian Strong

#9Ian Strong

Ian Strong is a Vertical Threat WR for California.

What projects, and what doesn't · WRs · 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)0.47
    Beats guessing the WR average by 16%. n=6,302 WR seasons
  • EPA per play (efficiency)0.09
    Not projectable — we do not forecast this. n=5,767 WR seasons
  • Total EPA (value)0.45
    Beats guessing the WR average by 12%. n=5,767 WR seasons

Across all positions, usage carries year to year at 0.47 and efficiency at 0.07. So Ian Strong'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
8 Receptions158 Rec yards0 Rec TD19.8 Yards/rec

Air yards

Where the ball goes, and how much of the gain is the throw rather than the run after it.

17
Targets
14.1y
Avg depth
3.4y
After catch
47%
Catch rate

The ball travels 14.1 yards in the air on an average target 5.2 yards deeper than the median. Another 3.4 comes after the catch.

Where he is targeted

targets · completion rate · yards
deep
1
100% · 49y
2
50% · 43y
0
short
6
33% · 33y
1
0% · 0y
7
57% · 33y
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 WR peers

  • Efficiency67
  • Volume31
  • Explosiveness85
  • Consistency50
  • Pass-Down100
Player type
Vertical Threat WR

Elite deep receiver who stretches the field. Wins downfield, commands safety attention, and creates the threat that opens underneath routes.

Downfield threatYAC upsideCreates space for teammates
Peer percentiles
Opponent-adjusted EPA (WEPA/play)67th %ile · average
Key findings
  • Particularly dangerous on passing downs — efficiency spikes in obvious pass situations.
  • Peak game: 0.47 EPA/play in Wk 1 vs UCLA (SP+ 9).

NIL Market Tier· 2026

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 · WR · 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
Brandon ChatmanNavy0.2300.612.0
Amare JonesTulane0.1800.012.8
Tyler BuchnerNotre Dame0.2500.312.8
Justin LynchTemple0.2400.315.1
Amare JonesTulane0.2300.813.3

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

Game Log · box score + EPA, week by week

+0.4700.47Wk 1 vs UCLA: +0.47 EPA/play1Wk 2 vs Syracuse: -0.36 EPA/play2
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
1vsUCLAL24-459.259418.80490.47
2@SyracuseW21-182.736421.3043-0.36

Usage & Situational · Pro

Snap-share proxy
Overall
10.7%
Passing plays
19.8%
Rushing plays
0.0%
Standard downs
7.6%
Passing downs
15.8%
EPA by down type
Standard downs
-0.11
Passing downs
0.37
Pass / Rush EPA
0.16 / —

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.