Backcheck
Roster needs · candidate fits
As of Sun, Sep 20

The Trade Board

The model measures each club's roster gaps against the league, then ranks outside players on how well they'd plug the biggest one — on-ice value, style fit, and how gettable the seller looks. On-ice fit only; nothing here is cap-validated.

How to read this

First the diagnosis: we compare this club's impact in every roster bucket — top-six scoring, defensive D, and so on — against the league average, and rank the shortfalls. The biggest gap becomes the shopping brief.

Then the shortlist: outside players are scored on how well they'd plug that hole — their on-ice value (), their with how this club creates offense, and an that assumes struggling clubs sell and contenders don't. Know what this board is not: it has no salary data, no cap math, no term, and no idea whether a GM would return the call. It's a scouting shortlist built purely from on-ice fit — the phone call is your job.

NEED
roster bucket where the club trails league average, ranked by the gap
WAR*
on-ice value — a model proxy, not official WAR
STYLE
0–100: does the candidate score the way this club scores?
ACQ†
0–100: how gettable the seller looks, from points pace alone
FIT
value × need × style × acquirability — the shortlist order
Fine print · verbatim from the model
  • on-ice fit only; not cap-validated
  • acquirability is a PROXY (team points pace), not real availability; no contract/cap/AAV/term data exists.
  • No real contract data (PuckPedia/CapWages are paid). Contract phase is an age/experience estimate; market visibility proxies what contracts pay for.
ANAAnaheim Ducks
Biggest need

Depth defense

-48team 39.2 vs league 87.3 · high
All needs · ranked
4 flagged
01
Depth defensehigh
-48
02
Bottom-6 forward depthhigh
-39
03
Top-pair defensemanhigh
-18
04
Top-6 forwardhigh
-13
Best value fits
8 ranked · league board →
#FromPlayer
1CHIstats: UTA
Ian ColeD · +1.18Coming off a solid contributor season, brings a different stylistic profile (32/100) — and comes badly underpriced by the market.
54+29.4UFA (est.)
69/100 · model-est.
2COL
Josh MansonD · +1.80Coming off a solid contributor season, brings a different stylistic profile (32/100) — and comes badly underpriced by the market.
53+28.7UFA (est.)
68/100 · model-est.
3TBL
Charle-Edouard D'AstousD · +1.58Coming off a solid contributor season, brings a different stylistic profile (42/100) — and comes underpriced by the market.
56+23.4UFA (est.)
68/100 · model-est.
4OTT
Artem ZubD · +1.37Coming off a solid contributor season, brings a different stylistic profile (59/100) — and comes underpriced by the market.
57+20.2UFA (est.)
68/100 · model-est.
5OTT
Jordan SpenceD · +2.37Coming off a clear top-of-the-lineup season, brings a different stylistic profile (1/100) — and comes badly underpriced by the market.
51+28.9RFA (est.)
68/100 · model-est.
6VGK
Jeremy LauzonD · +0.42Coming off a depth season, brings a different stylistic profile (59/100) — and comes underpriced by the market.
54+23.3UFA (est.)
67/100 · model-est.
7PITstats: VGK
Kaedan KorczakD · +0.87Coming off a depth season, brings a different stylistic profile (25/100) — and comes badly underpriced by the market.
48+32.9RFA (est.)
67/100 · model-est.
8COL
Brent BurnsD · +2.07Coming off a clear top-of-the-lineup season, brings a different stylistic profile (42/100) — and comes underpriced by the market.
54+22.7UFA (est.)
67/100 · model-est.

◆ Value basis · No real contract data (PuckPedia/CapWages are paid). Contract phase is an age/experience estimate; market visibility proxies what contracts pay for.

Candidate fits · Depth defense
15 ranked
#FromPlayer
1OTT
Thomas ChabotD · 60.0Coming off a solid contributor season, brings a different stylistic profile (68/100).
+1.256847
AVERAGE FIT59/100 · model-est.
2VGK
Rasmus AnderssonD · 58.1Coming off a solid contributor season, brings a different stylistic profile (68/100).
+1.796848
AVERAGE FIT58/100 · model-est.
3UTA
Mikhail SergachevD · 47.8Coming off a solid contributor season, fits the style well (81/100).
+1.698147
AVERAGE FIT58/100 · model-est.
4OTT
Artem ZubD · 64.2Coming off a solid contributor season, brings a different stylistic profile (59/100) — and comes underpriced by the market.
+1.375947
AVERAGE FIT57/100 · model-est.
5PHI
Cam YorkD · 48.6Coming off a depth season, fits the style well (77/100).
+0.497750
AVERAGE FIT57/100 · model-est.
6SJSstats: ANA
Jacob TroubaD · 51.3Coming off a solid contributor season, brings a different stylistic profile (68/100).
+1.096854
AVERAGE FIT57/100 · model-est.
7DET
Justin FaulkD · 43.5Coming off a solid contributor season, fits the style well (81/100).
+1.028152
AVERAGE FIT57/100 · model-est.
8TBL
Emil LillebergD · 48.5Coming off a depth season, fits the style well (81/100).
+0.318144
AVERAGE FIT57/100 · model-est.
9CGYstats: NJD
Simon NemecD · 40.6Coming off a depth season, fits the style well (81/100).
+0.518155
AVERAGE FIT57/100 · model-est.
10COL
Cale MakarD · 67.4Coming off a clear top-of-the-lineup season, brings a different stylistic profile (59/100) — and comes underpriced by the market.
+3.395940
AVERAGE FIT57/100 · model-est.
11CHIstats: BUF
Bowen ByramD · 55.5Coming off a solid contributor season, brings a different stylistic profile (59/100).
+1.415956
AVERAGE FIT57/100 · model-est.
12LAK
Joel EdmundsonD · 44.7Coming off a depth season, fits the style well (77/100).
+0.387752
AVERAGE FIT56/100 · model-est.
13NSHstats: DAL
Ilya LyubushkinD · 32.5Coming off a below-replacement season, plays this club's style almost exactly (92/100).
-0.179252
AVERAGE FIT56/100 · model-est.
14CAR
K'Andre MillerD · 75.1Coming off a clear top-of-the-lineup season, brings a different stylistic profile (42/100) — and comes underpriced by the market.
+2.104245
AVERAGE FIT56/100 · model-est.
15EDM
Jake WalmanD · 31.6Coming off a depth season, plays this club's style almost exactly (96/100).
+0.199648
AVERAGE FIT56/100 · model-est.
◆ The Modelon-ice fit only; not cap-validated* WAR is a model-estimated proxy, not official WAR

acquirability is a PROXY (team points pace), not real availability; no contract/cap/AAV/term data exists.

Fit score blends on-ice value, positional need, style match and the acquirability proxy. It says nothing about contracts, cap space, term, or whether a seller would pick up the phone — read it as a scouting shortlist, not a trade machine.