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Data + sources
RAS by Kent Lee Platte·Consensus Big Board (Arif Hasan)·nflverse·PFR·CFBD·All sources & credits·Methodology
Career-value model, live
Privacy·Terms·Press ⌘K to search·Draftanomics2026
  1. Draftanomics/
  2. Prospects/
  3. 2016 board/
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Side-by-side comparison

Backtest

2 prospects · 2016 draft

Each spoke: percentile within the prospect's own position (EDGE)
YN
Yannick Ngakoue
EDGE · Maryland
21.1 yo · 6'2" · 252 lb
EO
Emmanuel Ogbah
EDGE · Oklahoma St.
22.5 yo · 6'4" · 273 lb
Tiebreaker
High confidence
Take Emmanuel Ogbah over Yannick Ngakoue.

Decisive factor: 85-pick gap on Draftanomics projection. Reinforced by a bust risk 7 points lower.

Headline metrics

The four numbers a GM checks first.

Yannick Ngakoue
Predicted pick
#165
range #155–#175
Bust probability
7%
Consensus rank
#129
on 1 board
Career arc
arrives after the combineThe forecast needs his measurables first.
2nd-deal AAV
$20Mmodel
Emmanuel Ogbah
Predicted pick
#80
range #73–#87
Bust probability
0%
Consensus rank
#33
on 18 boards
Career arc
arrives after the combineThe forecast needs his measurables first.
2nd-deal AAV
$17Mmodel

Feature radar

Each axis is the prospect's percentile among prospects at his position (0–100). Bigger shape = stronger across the board.

ProductionAthleticismAgeConsensusPer-play impact (PPA)Breakout ageWorkloadRAS × production
Prospects
  • Yannick Ngakoue· EDGE / DL / LB
  • Emmanuel Ogbah· EDGE / DL / LB
Axis leaders
  • ProductionYannick Ngakoue50
  • AthleticismEmmanuel Ogbah75
  • AgeYannick Ngakoue90
  • ConsensusEmmanuel Ogbah44
  • Per-play impact (PPA)Yannick Ngakoue50
  • Breakout ageYannick Ngakoue50
  • WorkloadYannick Ngakoue50
  • RAS × productionYannick Ngakoue100

Comp overlap

Top five historical comps per prospect. Shared comps highlighted.

No shared comps in the top five. Each prospect has a distinct historical profile.
Yannick Ngakoue
  • comps arrive after the combineBefore the combine there isn't enough measured data to match him confidently against past players. Comparisons draw on recent NFL careers (2021–2024) and fill in once combine numbers and game-by-game stats land.
Emmanuel Ogbah
  • comps arrive after the combineBefore the combine there isn't enough measured data to match him confidently against past players. Comparisons draw on recent NFL careers (2021–2024) and fill in once combine numbers and game-by-game stats land.

Risk and opportunity flags

Top three concerns and top two upside signals from the model.

Yannick Ngakoue
Risks
No major risks flagged.
Opportunities
  • Consensus says higher
    2 boards have him at mean rank #122; model has him #165 — a 44-spot fade. Analysts converging this much above the model is a tell that the model is undervaluing him.
Emmanuel Ogbah
Risks
No major risks flagged.
Opportunities
  • Consensus says higher
    19 boards have him at mean rank #36; model has him #80 — a 44-spot fade. Analysts converging this much above the model is a tell that the model is undervaluing him.

Career trajectory forecast

Projected share of his team's snaps across the first four NFL seasons. Solid = seasons already played, dashed = the forecast.

0%25%50%75%100%Y1Y2Y3Y4Snap share
Yannick NgakoueEmmanuel Ogbah· solid = actuals, dashed = forecast

Verdict

The model's plain-English read.

Yannick Ngakoue

Draftanomics projects Yannick Ngakoue (age 21.1 at draft) as a mid-round flyer at #165 (range #155-#175, moderate band). Bust probability sits at 7% — low. The call is a genuine fade: the room's consensus prices him around #122 and the model won't take him before #165 — a 44-spot markdown. The 44-spot markdown rests on thin verified signal rather than a named flaw — the model hasn't seen enough yet to match the room's conviction.

Emmanuel Ogbah

Draftanomics projects Emmanuel Ogbah (age 22.5 at draft) as a third-round / early Day 3 pick at #80 (range #73-#87, moderate band). Bust probability sits at 0% — low. The call is a genuine fade: the room's consensus prices him around #36 and the model won't take him before #80 — a 44-spot markdown. The 44-spot markdown rests on thin verified signal rather than a named flaw — the model hasn't seen enough yet to match the room's conviction.