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

Seattle Kraken

5-on-5 · model-estimated

Kraken: a balanced team (49.6% 5v5 xG) with league-average goaltending.

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 · balanced
49.6%
xGoals %
This team plays about even.
46.0%
Corsi %
0.03
xGF / 60
1.011
PDO
The bounces are flattering them — expect some giveback.
Strengths
  • No standout edges flagged.
Weaknesses
  • Leaky penalty kill (72% kill rate).
Talent tiers · model impact
Elite00
  • None
Top04
  • Jared McCann
  • Brandon Montour
  • Shane Wright
  • Ben Meyers
Middle14
  • Jordan Eberle
  • Matty Beniers
  • Bobby McMann
  • Kaapo Kakko
  • Vince Dunn
  • Ryker Evans
  • Jaden Schwartz
  • Ryan Winterton
  • Berkly Catton
  • Jamie Oleksiak
  • Jani Nyman
  • Jacob Melanson
  • Cale Fleury
  • Joshua Mahura
Depth05
  • Chandler Stephenson
  • Eeli Tolvanen
  • Adam Larsson
  • Frederick Gaudreau
  • Ryan Lindgren
Rate-first

Special teams

Power play
0.00 xGF/60 · 19.5% SH%
5.86GF/60
Penalty kill
0.7 Kill%
10.79GA/60
Source · MoneyPuck 5-on-5 + ST splits
Team GSAx +0.3

Goaltending

Above expected
GoaltenderGP
Joey Daccord4789.750100.000+1.6
Philipp Grubauer3290.880+0.0
Source · GSAx = xGoals − goals, computed in-app
Roster needs · ranked
01
Depth defensehigh
team 49.9 vs league 87.3
-37
02
Bottom-6 forward depthhigh
team 47.8 vs league 81.9
-34
03
Top-pair defensemanhigh
team 65.2 vs league 91.5
-26
04
Top-6 forwardhigh
team 60.9 vs league 86.1
-25

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

Top players · adjusted impact
PlayerGPTOI/GP
Jordan EberleR8014.8+59.60+2.1
Jared McCannL5212.7+65.00+1.8
Matty BeniersC8215.2+50.80+1.7
Bobby McMannC7814.3+48.00+1.7
Brandon MontourD6419.3+70.50+1.6
Kaapo KakkoR6512.6+52.00+1.5
Vince DunnD8118.0+51.10+1.3
Shane WrightC7411.9+60.50+1.3
Ben MeyersC5210.2+67.30+0.8
Ryker EvansD6716.0+59.90+0.8
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

ELITE
Load index
100/ 100 · pctile vs league

The 1st-heaviest schedule in the league84 games, 1.3 days average rest.

Total travel
50,229
road miles flown
13
Back-to-backs
2 games, 0 days off
20
3-in-4s
3 games / 4 nights
22
4-in-6s
4 games / 6 nights
6
Longest road trip
4,484 mi
Rest-days distribution
0d13
1d51
2d12
3+d7

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

Rest advantage

16 games with more rest than the opponent · 21 with fewer · 46 even.

Hardest stretches
  • 6 games in 9 days with 3,485 travel miles

    Jan 18Jan 26

  • 6 games in 11 days with 4,807 travel miles

    Dec 12Dec 22

  • 6 games in 11 days with 4,661 travel miles

    Feb 19Mar 1

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