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

Colorado Avalanche

5-on-5 · model-estimated

Avalanche: 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.
56.4%
Corsi %
0.00
xGF / 60
1.020
PDO
The bounces are flattering them — expect some giveback.
Strengths
  • Elite top-end talent: Nathan MacKinnon, Brock Nelson, Sam Malinski.
Weaknesses
  • Out-chanced at 5v5 (0.0% xG share).
Regression watch · luck-reconciliation

PDO 1.021 — running hot at 5v5; expect negative regression.

Team finishing is -11 goals below xG — finishing should rebound.

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

Talent tiers · model impact
Elite06
  • Nathan MacKinnon
  • Brock Nelson
  • Sam Malinski
  • Valeri Nichushkin
  • Josh Manson
  • Nick Blankenburg
Top08
  • Martin Necas
  • Cale Makar
  • Artturi Lehkonen
  • Brent Burns
  • Parker Kelly
  • Gabriel Landeskog
  • Devon Toews
  • Ross Colton
Middle04
  • Nazem Kadri
  • Jack Drury
  • Joel Kiviranta
  • Gavin Brindley
Depth02
  • Nicolas Roy
  • Brett Kulak
Rate-first

Special teams

Power play
0.00 xGF/60 · 17.1% SH%
5.13GF/60
Penalty kill
0.8 Kill%
5.43GA/60
Source · MoneyPuck 5-on-5 + ST splits
Team GSAx +0.0

Goaltending

Above expected
GoaltenderGP
Scott Wedgewood4592.130+0.0
Mackenzie Blackwood3990.350+0.0
Source · GSAx = xGoals − goals, computed in-app
Roster needs · ranked
01
Bottom-6 forward depthhigh
team 55.5 vs league 81.9
-26
02
Depth defensehigh
team 71.6 vs league 87.3
-16
03
Top-6 forwardhigh
team 75.4 vs league 86.1
-11
04
Top-pair defensemanmoderate
team 84.8 vs league 91.5
-7

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

Top players · adjusted impact
PlayerGPTOI/GP
Nathan MacKinnonC8016.9+81.10+6.2
Martin NecasC7816.6+66.90+4.5
Brock NelsonC8113.8+86.00+4.0
Cale MakarD7517.8+67.40+3.4
Sam MalinskiD8216.3+90.70+2.7
Valeri NichushkinR7213.5+76.50+2.6
Artturi LehkonenL7014.5+72.20+2.5
Brent BurnsD8215.6+73.90+2.1
Parker KellyC8210.4+69.50+1.9
Josh MansonD7915.4+77.20+1.8
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

DEPTH
Load index
32/ 100 · pctile vs league

The 22nd-heaviest schedule in the league84 games, 1.3 days average rest.

Total travel
50,591
road miles flown
10
Back-to-backs
2 games, 0 days off
10
3-in-4s
3 games / 4 nights
6
4-in-6s
4 games / 6 nights
5
Longest road trip
3,760 mi
Rest-days distribution
0d10
1d54
2d13
3+d6

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

Rest advantage

21 games with more rest than the opponent · 20 with fewer · 42 even.

Hardest stretches
  • 6 games in 11 days with 4,386 travel miles

    Feb 27Mar 9

  • 6 games in 11 days with 4,348 travel miles

    Feb 12Feb 22

  • 6 games in 11 days with 4,269 travel miles

    Nov 25Dec 5

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