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

St. Louis Blues

5-on-5 · model-estimated

Blues: a balanced team (51.8% 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
51.8%
xGoals %
This team plays about even.
48.3%
Corsi %
0.03
xGF / 60
1.000
PDO
Luck is about neutral; the record is earned.
Strengths
  • Elite top-end talent: Dylan Holloway.
Weaknesses
  • Leaky penalty kill (77% kill rate).
Talent tiers · model impact
Elite01
  • Dylan Holloway
Top07
  • Robert Thomas
  • Jordan Kyrou
  • Jimmy Snuggerud
  • Jake Neighbours
  • Philip Broberg
  • Colton Parayko
  • Tyler Tucker
Middle04
  • Pavel Buchnevich
  • Pius Suter
  • Cam Fowler
  • Otto Stenberg
Depth09
  • Jonatan Berggren
  • Nathan Walker
  • Dalibor Dvorsky
  • Oskar Sundqvist
  • Jonathan Drouin
  • Matthew Kessel
  • Logan Mailloux
  • Jack Finley
  • Alexey Toropchenko
Rate-first

Special teams

Power play
0.00 xGF/60 · 17.6% SH%
5.28GF/60
Penalty kill
0.8 Kill%
8.53GA/60
Source · MoneyPuck 5-on-5 + ST splits
Team GSAx -0.5

Goaltending

Below expected
GoaltenderGP
Joel Hofer4690.960+0.0
Jordan Binnington4187.33066.670-0.5
Source · GSAx = xGoals − goals, computed in-app
Roster needs · ranked
01
Bottom-6 forward depthhigh
team 35.6 vs league 81.9
-46
02
Depth defensehigh
team 43.2 vs league 87.3
-44
03
Top-pair defensemanhigh
team 70.0 vs league 91.5
-22
04
Top-6 forwardhigh
team 68.5 vs league 86.1
-18

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

Top players · adjusted impact
PlayerGPTOI/GP
Robert ThomasC6414.3+74.30+3.2
Dylan HollowayL5914.9+76.20+2.6
Jordan KyrouR7213.1+72.00+2.4
Jimmy SnuggerudR7014.0+66.80+2.3
Jake NeighboursL6912.7+67.70+1.7
Pavel BuchnevichL8113.2+49.90+1.7
Philip BrobergD8118.8+60.50+1.3
Pius SuterC6412.7+53.80+1.1
Colton ParaykoD7718.8+67.10+1.0
Tyler TuckerD6913.2+72.90+0.9
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
84/ 100 · pctile vs league

The 6th-heaviest schedule in the league84 games, 1.3 days average rest.

Total travel
44,517
road miles flown
12
Back-to-backs
2 games, 0 days off
15
3-in-4s
3 games / 4 nights
14
4-in-6s
4 games / 6 nights
5
Longest road trip
3,958 mi
Rest-days distribution
0d12
1d50
2d14
3+d7

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

Rest advantage

23 games with more rest than the opponent · 23 with fewer · 37 even.

Hardest stretches
  • 6 games in 10 days with 4,112 travel miles

    Feb 12Feb 21

  • 6 games in 10 days with 3,831 travel miles

    Dec 27Jan 5

  • 6 games in 9 days with 2,806 travel miles

    Mar 20Mar 28

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