Backcheck
Team diagnostic
2025-26last seasonAs of Sun, Sep 20

New Jersey Devils

5-on-5 · model-estimated

Devils: a outshot/run-and-gun team (0.0% 5v5 xG) with league-average goaltending, flagged for regression (3 signals).

How to read this

This is one team through the publication's three layers. The identity row separates process — the chances a club creates and allows (, ) — from luck, which is what measures. Process predicts the future; luck predicts a correction.

Everything below follows that split: regression flags mark places where results have outrun (or trailed) the underlying play, talent tiers and the player table rank the roster by a model-estimated , and roster needs compare each position group against league average. The proxies rank things usefully but share credit with linemates — read orderings as solid and exact magnitudes as approximate.

PROCESS
chance creation and possession — xG%, Corsi. The repeatable part.
RESULT
goals, wins, special-teams conversion. What the standings see.
LUCK
PDO, finishing vs expected, GSAx — the part that mean-reverts.
GOLD/RED
regression flags: gold = watch it, red = act on it
Fine print · verbatim from the model
  • WAR / Impact are model-estimated proxies, not cap-validated
  • Roster-need gaps are ordinal — ranked correctly, magnitudes relative
Identity · outshot/run-and-gun
0.0%
xGoals %
This team gets buried territorially.
51.3%
Corsi %
0.00
xGF / 60
0.971
PDO
The bounces have been cruel — results should improve.
Strengths
  • Elite top-end talent: Jack Hughes, Dougie Hamilton.
Weaknesses
  • Out-chanced at 5v5 (0.0% xG share).
  • Leaky penalty kill (79% kill rate).
Regression watch · luck-reconciliation

PDO 0.971 — running cold at 5v5; due for positive regression.

Team finishing is -29 goals below xG — finishing should rebound.

Jacob Markstrom sv% 88.3% is below replacement — goaltending should improve or change.

Flags mark gaps between process and result likely to move toward the mean.

Talent tiers · model impact
Elite02
  • Jack Hughes
  • Dougie Hamilton
Top05
  • Jesper Bratt
  • Nico Hischier
  • Timo Meier
  • Luke Hughes
  • Cody Glass
Middle09
  • Dawson Mercer
  • Arseny Gritsyuk
  • Connor Brown
  • Simon Nemec
  • Brenden Dillon
  • Brett Pesce
  • Colton White
  • Johnathan Kovacevic
  • Jonas Siegenthaler
Depth07
  • Lenni Hameenaho
  • Nick Bjugstad
  • Maxim Tsyplakov
  • Stefan Noesen
  • Juho Lammikko
  • Paul Cotter
  • Evgenii Dadonov
Rate-first

Special teams

Power play
0.00 xGF/60 · 22.0% SH%
6.59GF/60
Penalty kill
0.8 Kill%
7.41GA/60
Source · MoneyPuck 5-on-5 + ST splits
Team GSAx +0.0

Goaltending

Above expected
GoaltenderGP
Jacob Markstrom4488.320+0.0
Jake Allen3790.400+0.0
Source · GSAx = xGoals − goals, computed in-app
Roster needs · ranked
01
Bottom-6 forward depthhigh
team 35.8 vs league 81.9
-46
02
Depth defensehigh
team 47.8 vs league 87.3
-40
03
Top-pair defensemanhigh
team 70.9 vs league 91.5
-21
04
Top-6 forwardhigh
team 66.4 vs league 86.1
-20

Gaps are ordinal — needs are ranked correctly; treat the magnitude as relative, not absolute.

Top players · adjusted impact
PlayerGPTOI/GP
Jack HughesC6117.0+75.90+3.6
Jesper BrattL8214.2+66.50+3.3
Nico HischierC8215.0+68.10+3.1
Dougie HamiltonD7717.2+81.00+2.3
Timo MeierR7715.5+69.20+2.2
Dawson MercerC8214.3+54.10+1.7
Arseny GritsyukR6613.6+55.90+1.3
Luke HughesD6819.2+60.80+1.3
Cody GlassC7012.1+62.70+1.3
Connor BrownR7513.7+42.90+1.3
Schedule load · logistics

How hard is this team’s season, logistically? This grades the schedule itself — travel, rest, and clustering — separate from how good the team is.

Logistics, not talent

Schedule load

STRONG
Load index
77/ 100 · pctile vs league

The 8th-heaviest schedule in the league84 games, 1.3 days average rest.

Total travel
36,760
road miles flown
13
Back-to-backs
2 games, 0 days off
17
3-in-4s
3 games / 4 nights
16
4-in-6s
4 games / 6 nights
7
Longest road trip
6,625 mi
Rest-days distribution
0d13
1d46
2d17
3+d7

Nights of rest before each game · 0 days = red, 3+ = green

Rest advantage

21 games with more rest than the opponent · 21 with fewer · 41 even.

Hardest stretches
  • 6 games in 9 days with 2,710 travel miles

    Jan 23Jan 31

  • 6 games in 12 days with 5,415 travel miles

    Dec 27Jan 7

  • 6 games in 11 days with 3,947 travel miles

    Feb 13Feb 23

Source · NHL schedule · arena coordinates · rest & travel model
By the Model5-on-5 aggregates · MoneyPuck* WAR / Impact are model-estimated proxies, not cap-validated