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

San Jose Sharks

5-on-5 · model-estimated

Sharks: a outshot/run-and-gun team (0.0% 5v5 xG) with league-average goaltending, flagged for regression (2 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.
47.0%
Corsi %
0.00
xGF / 60
0.998
PDO
Luck is about neutral; the record is earned.
Strengths
  • No standout edges flagged.
Weaknesses
  • Out-chanced at 5v5 (0.0% xG share).
  • Leaky penalty kill (76% kill rate).
Regression watch · luck-reconciliation

Team finishing is +13 goals above xG — shooting luck likely to cool.

Yaroslav Askarov sv% 88.4% 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
Elite00
  • None
Top08
  • Macklin Celebrini
  • Will Smith
  • Collin Graf
  • Dmitry Orlov
  • Adam Gaudette
  • Igor Chernyshov
  • Michael Misa
  • Pavol Regenda
Middle09
  • William Eklund
  • Tyler Toffoli
  • Alexander Wennberg
  • Kiefer Sherwood
  • John Klingberg
  • Jeff Skinner
  • Vincent Desharnais
  • Mario Ferraro
  • Sam Dickinson
Depth06
  • Philipp Kurashev
  • Shakir Mukhamadullin
  • Zack Ostapchuk
  • Barclay Goodrow
  • Ty Dellandrea
  • Nick Leddy
Rate-first

Special teams

Power play
0.00 xGF/60 · 21.2% SH%
6.35GF/60
Penalty kill
0.8 Kill%
8.44GA/60
Source · MoneyPuck 5-on-5 + ST splits
Team GSAx +0.0

Goaltending

Above expected
GoaltenderGP
Yaroslav Askarov4788.370+0.0
Alex Nedeljkovic4089.610+0.0
Source · GSAx = xGoals − goals, computed in-app
Roster needs · ranked
01
Depth defensehigh
team 45.4 vs league 87.3
-42
02
Top-pair defensemanhigh
team 60.9 vs league 91.5
-31
03
Bottom-6 forward depthhigh
team 53.2 vs league 81.9
-29
04
Top-6 forwardhigh
team 67.3 vs league 86.1
-19

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

Top players · adjusted impact
PlayerGPTOI/GP
Macklin CelebriniC8216.4+68.20+5.0
Will SmithC6914.2+63.40+2.5
Collin GrafR8113.0+69.00+2.2
William EklundL7814.5+54.60+1.9
Tyler ToffoliC7911.6+57.70+1.8
Alexander WennbergC8014.1+42.50+1.6
Kiefer SherwoodL7213.5+56.40+1.5
Dmitry OrlovD8217.4+63.20+1.5
Adam GaudetteR6610.4+63.10+1.2
John KlingbergD5616.8+58.50+1.1
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
90/ 100 · pctile vs league

The 4th-heaviest schedule in the league84 games, 1.3 days average rest.

Total travel
45,891
road miles flown
13
Back-to-backs
2 games, 0 days off
17
3-in-4s
3 games / 4 nights
19
4-in-6s
4 games / 6 nights
6
Longest road trip
3,619 mi
Rest-days distribution
0d13
1d49
2d13
3+d8

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

Rest advantage

18 games with more rest than the opponent · 23 with fewer · 42 even.

Hardest stretches
  • 6 games in 10 days with 4,147 travel miles

    Oct 31Nov 9

  • 6 games in 9 days with 3,197 travel miles

    Dec 3Dec 11

  • 6 games in 13 days with 5,651 travel miles

    Oct 3Oct 15

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