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

Buffalo Sabres

5-on-5 · model-estimated

Sabres: 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.
49.6%
Corsi %
0.00
xGF / 60
1.020
PDO
The bounces are flattering them — expect some giveback.
Strengths
  • Elite top-end talent: Rasmus Dahlin, Josh Doan.
Weaknesses
  • Out-chanced at 5v5 (0.0% xG share).
Regression watch · luck-reconciliation

PDO 1.020 — running hot at 5v5; expect negative regression.

Team finishing is +26 goals above xG — shooting luck likely to cool.

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

Talent tiers · model impact
Elite02
  • Rasmus Dahlin
  • Josh Doan
Top06
  • Tage Thompson
  • Alex Tuch
  • Jason Zucker
  • Zach Benson
  • Mattias Samuelsson
  • Josh Norris
Middle10
  • Jack Quinn
  • Ryan McLeod
  • Bowen Byram
  • Noah Ostlund
  • Owen Power
  • Sam Carrick
  • Tanner Pearson
  • Josh Dunne
  • Zach Metsa
  • Michael Kesselring
Depth07
  • Peyton Krebs
  • Logan Stanley
  • Beck Malenstyn
  • Jordan Greenway
  • Tyson Kozak
  • Conor Timmins
  • Luke Schenn
Rate-first

Special teams

Power play
0.00 xGF/60 · 19.5% SH%
5.85GF/60
Penalty kill
0.8 Kill%
6.47GA/60
Source · MoneyPuck 5-on-5 + ST splits
Team GSAx +0.0

Goaltending

Above expected
GoaltenderGP
Alex Lyon3690.700+0.0
Ukko-Pekka Luukkonen3590.970+0.0
Source · GSAx = xGoals − goals, computed in-app
Roster needs · ranked
01
Depth defensehigh
team 50.0 vs league 87.3
-37
02
Bottom-6 forward depthhigh
team 47.9 vs league 81.9
-34
03
Top-pair defensemanhigh
team 75.1 vs league 91.5
-16
04
Top-6 forwardhigh
team 70.8 vs league 86.1
-15

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

Top players · adjusted impact
PlayerGPTOI/GP
Rasmus DahlinD7719.0+85.60+3.9
Tage ThompsonC8114.8+73.70+3.8
Alex TuchR7914.0+68.70+3.2
Josh DoanR8212.6+75.20+2.7
Jason ZuckerL6212.0+71.80+2.1
Zach BensonL6512.9+71.00+2.0
Jack QuinnR8212.9+52.30+1.8
Mattias SamuelssonD7818.9+64.60+1.8
Ryan McLeodC8112.8+44.70+1.7
Josh NorrisC4411.9+64.70+1.5
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
71/ 100 · pctile vs league

The 10th-heaviest schedule in the league84 games, 1.3 days average rest.

Total travel
33,042
road miles flown
14
Back-to-backs
2 games, 0 days off
17
3-in-4s
3 games / 4 nights
12
4-in-6s
4 games / 6 nights
6
Longest road trip
3,755 mi
Rest-days distribution
0d14
1d43
2d19
3+d7

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

Rest advantage

20 games with more rest than the opponent · 26 with fewer · 37 even.

Hardest stretches
  • 6 games in 11 days with 3,575 travel miles

    Nov 11Nov 21

  • 6 games in 11 days with 3,225 travel miles

    Jan 20Jan 30

  • 6 games in 14 days with 4,972 travel miles

    Oct 24Nov 6

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