Skip to content
George Dimopoulos
George Dimopoulos

#9George Dimopoulos

George Dimopoulos is a Versatile WR for Northern Illinois.

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 George Dimopoulos'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
11 Receptions58 Rec yards0 Rec TD5.3 Yards/rec

Performance Analysis · 2025 · vs WR peers

  • Efficiency33
  • Volume14
  • Explosiveness0
  • Consistency0
  • Pass-Down0
Player type
Versatile WR

Balanced profile without a single dominant trait — contributes across multiple dimensions.

Balanced usageMulti-role
Peer percentiles
Opponent-adjusted EPA (WEPA/play)33th %ile · below avg
Game-to-game consistency0th %ile · below avg
Key findings
  • Below-average efficiency vs WR peers — value comes through volume, not per-play impact.
  • 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.
  • Production faded as the season progressed — 0.35 EPA/play decline from first to second half.
  • Peak game: 0.62 EPA/play in Wk 4 vs Mississippi State (SP+ 4).

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

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

Game Log · box score + EPA, week by week

+0.6200.62Wk 2 vs Maryland: +0.40 EPA/play2Wk 4 vs Mississippi State: +0.62 EPA/play4Wk 5 vs San Diego State: -0.23 EPA/play5Wk 6 vs Miami (OH): +0.60 EPA/play6Wk 7 vs Eastern Michigan: -0.55 EPA/play7Wk 8 vs Ohio: -0.29 EPA/play8
EPA per play · x-axis: weekabove 0 = added points · below = lost
WkOpponentResultOpp SP+RecRec YdsAvgRec TDLongEPA/play
2@MarylandL9-200.62147.0080.40
4@Mississippi StateL10-384.1273.50110.62
5vsSan Diego StateL3-66.7144.004-0.23
6vsMiami (OH)L14-25-3.411919.00190.60
7@Eastern MichiganL10-16-14.7133.003-0.55
8@OhioL21-48-4.04112.807-0.29

Usage & Situational · Pro

Snap-share proxy
Overall
5.0%
Passing plays
11.8%
Rushing plays
0.0%
Standard downs
5.3%
Passing downs
4.4%
EPA by down type
Standard downs
0.24
Passing downs
-0.24
Pass / Rush EPA
0.10 / —

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.