Backcheck
Scouting desk · breakouts & market gaps
2025-26last seasonAs of Sun, Sep 20

The Value Board

Two lists: tomorrow's stars and today's bargains. The first ranks every under-23 player by how likely he is to take a leap next season. The second finds players who help their team far more than their point totals suggest — the ones the market underpays attention to. Read both as scouting leads, not a cap sheet.

How to read this

Act I is a breakout board: for every under-23 skater we blend how good he already is, how fast his chance creation is growing year-over-year, and what the trained development model projects next — that's the . It's built to spot players becoming good before the goal totals announce it.

Act II hunts bargains without contract data — because we don't have any. Instead we compare each player's on-ice impact against his (the counting stats and spotlight the market actually pays for) and flag the biggest , weighted by an estimated . When both bars are long, everyone already knows; when the top bar runs far past the bottom one, that's the blind spot. Treat every number here as a scouting lead, not a cap sheet.

IMP
on-ice impact, as a 0–100 percentile of the league
MKT
market visibility — counting stats, minutes, spotlight
GAP
IMP minus MKT: how under-noticed the play is
PHASE
contract stage estimated from age and experience
Fine print · verbatim from the model
  • breakoutScore = 100·(0.45·impact + 0.30·yoyGrowth + 0.25·modelUpside). impact = position-relative percentile of warProxy; yoyGrowth = xG/game vs MoneyPuck 2025-26, mapped 0.5+Δ% saturating at ±50% (rookies — no ≥20 GP prior season — reuse impact and are flagged); modelUpside = our development model's projected xG growth (player_seasons_v2, r2=0.83), clamped −30%..+50%, falls back to impact outside its training window. Age ≤ 23 from NHL API rosters (unknown age → excluded); gates ≥15 GP and ≥8 5v5 min/game.
  • valueGap = impactPctile (position-relative percentile of warProxy) − marketVisibility (0.40·points/60 pctile + 0.25·goals pctile + 0.20·TOI/game pctile + 0.15·age premium, all-situations); valueScore = valueGap × contract-phase weight (ELC 1.5 / RFA 1.2 / UFA 1.0). Phase is an age/experience ESTIMATE. Gates: ≥15 GP, ≥8 5v5 min/game, impactPctile ≥ 60.
  • No real contract data (PuckPedia/CapWages are paid). Contract phase is an age/experience estimate; market visibility proxies what contracts pay for.
Act I · Emerging — the U-23 board
6 ranked
#TmPlayer
1MTL
D · age 22 · 82 GPXG rate up 0% year over year; too new for the development model's training window.
+0%
STRONG84model-est.
2NYI
D · age 19 · 82 GPXG rate up 0% year over year; too new for the development model's training window.
+0%
STRONG82model-est.
3LAK
D · age 23 · 82 GPXG rate up 0% year over year; too new for the development model's training window.
+0%
STRONG77model-est.
4CBJ
D · age 22 · 75 GPXG rate up 0% year over year; too new for the development model's training window.
+0%
STRONG75model-est.
5NJD
D · age 23 · 68 GPXG rate up 0% year over year; too new for the development model's training window.
+0%
AVERAGE68model-est.
6BUFstats: ANA
D · age 23 · 76 GPXG rate up 0% year over year; too new for the development model's training window.
+0%
AVERAGE64model-est.

◆ Method · breakoutScore = 100·(0.45·impact + 0.30·yoyGrowth + 0.25·modelUpside). impact = position-relative percentile of warProxy; yoyGrowth = xG/game vs MoneyPuck 2025-26, mapped 0.5+Δ% saturating at ±50% (rookies — no ≥20 GP prior season — reuse impact and are flagged); modelUpside = our development model's projected xG growth (player_seasons_v2, r2=0.83), clamped −30%..+50%, falls back to impact outside its training window. Age ≤ 23 from NHL API rosters (unknown age → excluded); gates ≥15 GP and ≥8 5v5 min/game. · proxy, not official WAR

◆ Rosters · teams reflect NHL rosters as of 2026-09-19; players who moved show their stats team below the chip

Act II · Value picks — the market gap
24 flagged
#TmPlayer
1NYI
Adam PelechD · age 32 · +1.10Strong on-ice impact (72nd percentile) the box score keeps quiet — badly underpriced by the market.
72nd percentile impact, but market visibility of only 35badly underpriced by the market
BADLY UNDERPRICED+37UFA (est.)
BADLY UNDERPRICED37model-est.
2UTA
Nate SchmidtD · age 35 · +1.48Strong on-ice impact (80th percentile) the box score keeps quiet — badly underpriced by the market.
80th percentile impact, but market visibility of only 45badly underpriced by the market
BADLY UNDERPRICED+35UFA (est.)
BADLY UNDERPRICED35model-est.
3PITstats: VGK
Kaedan KorczakD · age 25 · +0.87Middle-of-the-pack on-ice impact (65th percentile) the box score keeps quiet — badly underpriced by the market.
65th percentile impact, but market visibility of only 38badly underpriced by the market
BADLY UNDERPRICED+27RFA (est.)
BADLY UNDERPRICED33model-est.
4CHIstats: UTA
Ian ColeD · age 37 · +1.18Strong on-ice impact (73rd percentile) the box score keeps quiet — badly underpriced by the market.
73rd percentile impact, but market visibility of only 43badly underpriced by the market
BADLY UNDERPRICED+29UFA (est.)
BADLY UNDERPRICED29model-est.
5STL
Colton ParaykoD · age 33 · +1.03Middle-of-the-pack on-ice impact (69th percentile) the box score keeps quiet — badly underpriced by the market.
69th percentile impact, but market visibility of only 39badly underpriced by the market
BADLY UNDERPRICED+29UFA (est.)
BADLY UNDERPRICED29model-est.
6STL
Tyler TuckerD · age 26 · +0.94Middle-of-the-pack on-ice impact (67th percentile) the box score keeps quiet — underpriced by the market.
67th percentile impact, but market visibility of only 43underpriced by the market
UNDERPRICED+24RFA (est.)
UNDERPRICED29model-est.
7OTT
Jordan SpenceD · age 25 · +2.37Elite on-ice impact (90th percentile) the box score keeps quiet — underpriced by the market.
90th percentile impact, but market visibility of only 66underpriced by the market
UNDERPRICED+24RFA (est.)
UNDERPRICED29model-est.
8TBL
J.J. MoserD · age 26 · +1.94Strong on-ice impact (86th percentile) the box score keeps quiet — underpriced by the market.
86th percentile impact, but market visibility of only 62underpriced by the market
UNDERPRICED+24RFA (est.)
UNDERPRICED29model-est.
9COL
Josh MansonD · age 34 · +1.80Strong on-ice impact (85th percentile) the box score keeps quiet — badly underpriced by the market.
85th percentile impact, but market visibility of only 56badly underpriced by the market
BADLY UNDERPRICED+29UFA (est.)
BADLY UNDERPRICED29model-est.
10ANA
Ian MooreD · age 24 · +0.80Middle-of-the-pack on-ice impact (63rd percentile) the box score keeps quiet — underpriced by the market.
63rd percentile impact, but market visibility of only 39underpriced by the market
UNDERPRICED+24RFA (est.)
UNDERPRICED29model-est.
11COL
Sam MalinskiD · age 28 · +2.66Elite on-ice impact (93rd percentile) the box score keeps quiet — badly underpriced by the market.
93rd percentile impact, but market visibility of only 65badly underpriced by the market
BADLY UNDERPRICED+28UFA (est.)
BADLY UNDERPRICED28model-est.
12TORstats: PHI
Emil AndraeD · age 24 · +0.71Middle-of-the-pack on-ice impact (61st percentile) the box score keeps quiet — underpriced by the market.
61st percentile impact, but market visibility of only 37underpriced by the market
UNDERPRICED+23RFA (est.)
UNDERPRICED28model-est.
13WPG
Dylan DeMeloD · age 33 · +0.89Middle-of-the-pack on-ice impact (66th percentile) the box score keeps quiet — badly underpriced by the market.
66th percentile impact, but market visibility of only 38badly underpriced by the market
BADLY UNDERPRICED+28UFA (est.)
BADLY UNDERPRICED28model-est.
14DAL
Esa LindellD · age 32 · +1.97Strong on-ice impact (87th percentile) the box score keeps quiet — badly underpriced by the market.
87th percentile impact, but market visibility of only 60badly underpriced by the market
BADLY UNDERPRICED+28UFA (est.)
BADLY UNDERPRICED28model-est.
15FLA
Gustav ForslingD · age 30 · +1.32Strong on-ice impact (76th percentile) the box score keeps quiet — badly underpriced by the market.
76th percentile impact, but market visibility of only 49badly underpriced by the market
BADLY UNDERPRICED+27UFA (est.)
BADLY UNDERPRICED27model-est.
16EDM
Mattias EkholmD · age 36 · +2.42Elite on-ice impact (92nd percentile) the box score keeps quiet — badly underpriced by the market.
92nd percentile impact, but market visibility of only 66badly underpriced by the market
BADLY UNDERPRICED+25UFA (est.)
BADLY UNDERPRICED25model-est.
17LAK
Drew DoughtyD · age 36 · +1.38Strong on-ice impact (77th percentile) the box score keeps quiet — badly underpriced by the market.
77th percentile impact, but market visibility of only 52badly underpriced by the market
BADLY UNDERPRICED+25UFA (est.)
BADLY UNDERPRICED25model-est.
18CAR
Sean WalkerD · age 31 · +2.06Strong on-ice impact (88th percentile) the box score keeps quiet — badly underpriced by the market.
88th percentile impact, but market visibility of only 63badly underpriced by the market
BADLY UNDERPRICED+25UFA (est.)
BADLY UNDERPRICED25model-est.
19MIN
Jonas BrodinD · age 33 · +1.15Strong on-ice impact (72nd percentile) the box score keeps quiet — badly underpriced by the market.
72nd percentile impact, but market visibility of only 47badly underpriced by the market
BADLY UNDERPRICED+25UFA (est.)
BADLY UNDERPRICED25model-est.
20COL
Devon ToewsD · age 32 · +1.34Strong on-ice impact (76th percentile) the box score keeps quiet — underpriced by the market.
76th percentile impact, but market visibility of only 51underpriced by the market
UNDERPRICED+25UFA (est.)
UNDERPRICED25model-est.
21UTA
John MarinoD · age 29 · +1.70Strong on-ice impact (83rd percentile) the box score keeps quiet — underpriced by the market.
83rd percentile impact, but market visibility of only 59underpriced by the market
UNDERPRICED+25UFA (est.)
UNDERPRICED25model-est.
22CBJ
Damon SeversonD · age 32 · +2.14Strong on-ice impact (90th percentile) the box score keeps quiet — underpriced by the market.
90th percentile impact, but market visibility of only 65underpriced by the market
UNDERPRICED+24UFA (est.)
UNDERPRICED24model-est.
23VGK
Noah HanifinD · age 29 · +1.45Strong on-ice impact (79th percentile) the box score keeps quiet — underpriced by the market.
79th percentile impact, but market visibility of only 55underpriced by the market
UNDERPRICED+24UFA (est.)
UNDERPRICED24model-est.
24SJS
Dmitry OrlovD · age 35 · +1.52Strong on-ice impact (81st percentile) the box score keeps quiet — underpriced by the market.
81st percentile impact, but market visibility of only 57underpriced by the market
UNDERPRICED+24UFA (est.)
UNDERPRICED24model-est.

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

◆ Method · valueGap = impactPctile (position-relative percentile of warProxy) − marketVisibility (0.40·points/60 pctile + 0.25·goals pctile + 0.20·TOI/game pctile + 0.15·age premium, all-situations); valueScore = valueGap × contract-phase weight (ELC 1.5 / RFA 1.2 / UFA 1.0). Phase is an age/experience ESTIMATE. Gates: ≥15 GP, ≥8 5v5 min/game, impactPctile ≥ 60. · proxy, not official WAR

◆ Rosters · teams reflect NHL rosters as of 2026-09-19; players who moved show their stats team below the chip

Act III · The team lens

Value in the abstract is half the job — the Trade Board crosses this same value model with each club's biggest roster need and ranks the best value fits per team.

Open the Trade Board

* WAR is a model-estimated proxy from on-ice impact, not official WAR. Contract phases are age/experience estimates — every "(est.)" means exactly that. Source: MoneyPuck aggregates + NHL API rosters via the analytics sidecar.