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SJS

San Jose Sharks

Rated #30 of 32 by xG-Elo in 2026-27

2026-27 regular season

Record
7 wins, 6 regulation losses, 1 overtime losses
Points
15
Points rank
#21 of 32
Goal diff
−7

Every club’s 2026-27 standings

The model’s read · 2026-27

Rated 9 spots lower than the standings

  • #21 of 32In points
  • #30 of 32By xG-Elo
Where the model disagrees most
How the rating works — it is built from expected-goal margin, not from results

xG-Elo moves game by game with the model’s expected-goals margin, never with the score or the standings, which is why it can disagree with a club’s points. That disagreement is the sentence above. Under 7 spots apart the two are read as in line: a display rule for the wording, not a test of whether a gap is real.

This club’s rating is 1457. Every club starts from 1500 and is pulled back toward it between seasons, so above 1500 is stronger than an average team and below it weaker.

2026-27 · 14 games · goals

Goal margin: expected and actual

The standings count goals and the rating counts only chances; the San Jose Sharks scored more than their chances were worth, so the standings place them higher.

Expected goal margin−15.8 · #30 of 32

Actual goal margin, same attempts−7 · #24 of 32

Gap +8.8 goals, actual minus expected

Scoring, against the model
−2.8Fewer goals than its chances were worth
Goals allowed, against the model
−11.6Fewer than its opponents’ chances were worth
Compare with every club — where its two margins sit among the league’s

Expected goal margin−15.8 · #30 of 32

Actual goal margin, same attempts−7 · #24 of 32

  • Expected, from chance quality
  • Actual
  • Another club
Every club’s two margins, 2026-27, in order of expected margin
#ClubExpectedActualGap
1CAR+21.6+21−0.7
2MIN+18.1+12−6.0
3NJD+13.8+5−8.8
4UTA+13.7+5−8.7
5VGK+13.3+10−3.4
6TBL+9.8+6−3.8
7DET+6.1−1−7.1
8COL+5.1+13+7.9
9EDM+4.3+13+8.7
10FLA+3.9+2−1.9
11DAL+3.6+5+1.4
12NYR+2.3+2−0.3
13BUF+1.6−6−7.6
14STL+1.5+2+0.5
15MTL+0.5+3+2.5
16WSH+0.3+6+5.7
17BOS0.0+18+18.0
18OTT−0.9−2−1.1
19LAK−1.8−8−6.2
20NSH−2.7−4−1.3
21PIT−3.0−1+2.0
22NYI−3.5+5+8.6
23CGY−4.8−17−12.2
24TOR−5.2+7+12.2
25WPG−6.3−10−3.7
26CBJ−6.8−9−2.2
27ANA−7.9−7+0.9
28SEA−8.4+2+10.4
29CHI−13.6−21−7.4
30SJS−15.8−7+8.8
31PHI−16.2−21−4.8
32VAN−22.5−23−0.5

Every club’s expected goals and goals, for and against, are on the Teams page.

How these are counted — open-play unblocked attempts at every strength; not the standings’ goal totals

The reading in full

This points to goals rather than chances. The club’s goal margin ran 8.8 ahead of its expected-goal margin: measured against the model alone, it scored 2.8 fewer goals than its chances were worth and allowed 11.6 fewer than its opponents’ chances were worth. The standings are built from results, which count those goals; the rating counts only the chances, so it places the San Jose Sharks lower.

The rating is built from expected goals, the chances a club makes and allows. The standings are built from results, and results from goals. These are the two ledgers side by side, over the same attempts.

Both margins count the same pucks: open-play unblocked attempts, at every strength, with a recorded location. The actual margin is the goals on those attempts, so it leaves out penalty-shot goals and each shootout winner’s extra goal, which the standings count.

Raw, the San Jose Sharks scored 2.8 fewer goals than their chances were worth and allowed 11.6 fewer than their opponents’ chances were worth so far in 2026-27. The scoring and allowed bars are these raw figures, measured against the model alone: the model’s league-wide level for the season being played is read once, after the season, so no average club is measured until then, and the league’s own level stays in each club’s figure. Goals allowed beyond the chances is goaltending, and whatever else the model does not see.

Expected goals and goals, San Jose Sharks, 2026-27
LedgerForAgainst
Expected goals41.857.6
Goals on those attempts3946
Rating entering the season
1478 · #26 of 32
Rating entering its latest game
1457 · #30 of 32

The rating carries each club into a season from the last one, pulled back toward 1500, and then moves after every game by how far that game’s expected-goal margin beat or missed what the two clubs’ ratings predicted, home ice included. Its latest figure is the rating the club took into its most recent game.

What it cannot establish

Whether scoring or keeping out goals beyond expected is skill that will last, luck that will not, or chances the model reads wrong: nothing here tells the three apart. For skaters, finishing above expected has carried only partly into the following season — does finishing persist? These figures say which ledger the standings came from, not which ranking is right.

Regular season · per game · same attempts

Season trends

  • Expected
  • Actual
  • 2021-22: expected goal difference −0.51 per game, goal difference on the same attempts −0.61 per game, over 82 games
  • 2022-23: expected goal difference −0.42 per game, goal difference on the same attempts −1.01 per game, over 82 games
  • 2023-24: expected goal difference −1.34 per game, goal difference on the same attempts −1.78 per game, over 82 games
  • 2024-25: expected goal difference −0.89 per game, goal difference on the same attempts −1.23 per game, over 82 games
  • 2025-26: expected goal difference −0.38 per game, goal difference on the same attempts −0.50 per game, over 82 games
  • 2026-27 to date: expected goal difference −1.13 per game, goal difference on the same attempts −0.50 per game, over 14 games
Goal difference per game, expected (xGD) and actual (GD), using the same open-play attempts
How it is counted — not the standings’ goal difference

Each regular season, both figures count the same open-play unblocked attempts: expected goals for minus against, and the goals scored on those attempts for minus against, each divided by games played (GP). That is not the standings’ goal difference, which also counts penalty-shot goals and each shootout winner’s extra goal. The season to date is drawn on its own, joined to nothing.

Next game

Ottawa Senators at San Jose Sharks

Sat, Oct 31 · 4:00 PM ET

Pre-game win chance

OTT 56%SJS 44%

Set Oct 30, 8:55 AM ET, before puck drop.

The model’s projection, not a betting recommendation. Its accuracy is modest (AUC 0.600, on a scale where 0.5 is a coin flip and 1.0 is perfect). That figure is measured on 2025-26, the season the rest adjustment was first seen in, so it describes that season rather than testing the model on games it had not seen.

Game preview

Last game · numbered by five-on-five time together

Lines from last game

Oct 29 vs VAN (Vancouver Canucks). These are the lines that game was played with, not a projection of the next one.

Forwards

  1. Line 1StenbergCelebriniGraf7:26 together at five-on-five
  2. Line 2DellandreaGoodrowGaudette6:09 together at five-on-five
  3. Line 3MarchmentSmithMisa5:31 together at five-on-five
  4. Line 4SherwoodBystedtToffoli4:48 together at five-on-five

Defence

  1. Pair 1NurseKesselring12:43 together at five-on-five
  2. Pair 2TroubaDickinson12:03 together at five-on-five
  3. Pair 3AllanOrlov9:35 together at five-on-five

Penalty kill

  1. PK1GrafDellandreaTroubaNurse2:05 together killing a penalty
  2. PK2GoodrowSherwoodKesselringDickinson0:35 together killing a penalty

Goalie

Box score

Skaters
G
A
+/-
PIM
SH%
PPG
SHG
HIT
BLK
GV
TK
FOW
FOL
FO%
SHFT
TOI PP
TOI SH
0
0
0
0
0
—
0
0
3
2
1
0
0
0
—
23
24:00
3:10
4:36
0
0
+1
0
1
0.0
0
0
6
1
0
0
0
0
—
23
23:01
0:11
4:34
0
1
+1
0
6
0.0
0
0
0
1
3
0
8
7
53.3
19
22:56
6:09
1:16
0
1
0
2
0
—
0
0
0
2
2
0
0
0
—
22
19:01
0:12
1:21
0
1
−1
0
1
0.0
0
0
1
1
0
0
0
0
—
18
18:11
5:51
0:08
0
1
+1
0
3
0.0
0
0
4
0
1
0
0
0
—
22
18:01
2:45
1:54
0
0
−1
0
1
0.0
0
0
0
1
1
1
0
0
—
16
17:59
6:19
0:00
2
0
0
0
3
66.7
1
0
0
0
1
0
0
1
0.0
17
17:44
6:09
0:07
0
0
+1
0
1
0.0
0
0
3
0
1
0
0
1
0.0
19
17:42
0:00
3:27
1
1
0
0
3
33.3
0
0
0
0
1
0
0
0
—
17
17:36
6:05
0:04
0
0
0
2
0
—
0
0
0
2
0
0
0
0
—
20
14:27
0:03
0:42
0
0
0
2
2
0.0
0
0
0
1
2
0
5
4
55.6
16
14:13
2:49
0:00
0
1
0
0
0
—
0
0
0
0
0
0
0
0
—
16
13:06
0:00
0:43
0
0
0
0
0
—
0
0
1
0
3
0
0
0
—
17
12:29
2:41
0:00
0
0
0
0
0
—
0
0
5
1
1
1
0
0
—
15
11:41
0:00
2:26
0
0
−1
0
0
—
0
0
4
1
0
0
4
5
44.4
17
11:36
0:00
2:42
0
0
−1
0
2
0.0
0
0
0
0
0
1
3
4
42.9
15
10:28
0:12
0:00
0
0
0
0
1
0.0
0
0
0
0
0
0
1
0
100.0
12
10:12
2:29
0:00
Goaltending
SOGA
MIN
PIM
25
2
23
.920
59:37
0
How these lines are found — from the shift charts, numbered by time together, not a coach’s depth chart

Every shift each player took in the game is lined up against everyone else’s, using the NHL’s own shift charts. Only five-on-five time with both goalies in net counts, so power plays and penalty kills do not split a line. Line 1 is the three forwards who spent the most of that time on the ice together, line 2 the most of the rest, and so on; defence pairs the same way.

A group that never took a full shift together, about a minute, is not called a line: its players are listed as also played. A game is used only when every skater’s time in the shift chart is within 30 seconds of the box score, so a chart the NHL published incomplete is skipped rather than read.

A power-play unit is the same five skaters on the ice together while the club had five skaters to the other side’s four or three; a penalty-kill unit is four skaters together while short a man. The second unit is the best group that shares at most one player with the first, since one player often plays on both. Power-play and short-handed time follow the penalties, not an empty net: a goalie pulled for an extra attacker at even strength is still even strength.

The box score is the NHL’s own, except four figures it does not publish. Short-handed goals and faceoffs won and lost come from the play-by-play, which is used only when it reproduces the box score’s goals, power-play goals and faceoff percentages exactly; power-play and short-handed ice time come from the shift chart.

From the last 7 days · newest first

News

  1. Transaction

    Former Sharks Defenseman Placed on Waivers by Vancouver (external site)

    The Hockey News - San Jose Sharks via Yahoo Sports ·

    In this story: Jack Thompson, San Jose Sharks, Vancouver Canucks

All San Jose Sharks news

Current roster · clubs updated weekly

Roster

Per-game rates, recency-weighted over every season since 2021-22. Skaters are listed by Adj. P, points per game pulled toward the league average when the sample is small; the other rates are the recorded ones.

Forwards: per-game rates
Forwards#Adj. PPTSGA
Macklin Celebrini711.251.270.480.793.58164
Will Smith20.750.760.340.422.28157
Logan Couture390.700.730.290.442.35165
Tyler Toffoli730.660.660.310.352.47423
Ivar Stenberg410.630.640.210.431.3614
Mason Marchment270.600.600.280.331.88400
Igor Chernyshov920.540.540.260.281.4039
Alexander Wennberg210.530.530.160.371.17437
Kiefer Sherwood440.490.490.250.242.05281
Collin Graf510.460.460.220.241.40135
Michael Misa770.450.450.180.271.2159
Adam Gaudette810.340.340.230.111.31216
Cam Lund460.320.270.180.091.0911
Alex Barré-Boulet160.300.280.130.150.9657
Martin Kaut870.280.210.100.101.1442
Brett Leason170.250.250.100.151.15227
Filip Bystedt180.230.000.000.002.001
Tye Felhaber280.200.000.000.000.405
Ty Dellandrea100.200.200.040.160.97268
Ethan Cardwell560.200.150.150.000.8513
Patrick Giles540.180.130.040.090.8320
Barclay Goodrow230.160.150.070.090.87446
Zack Ostapchuk630.100.100.060.040.72135
Defence: per-game rates
Defence#Adj. PPTSGA
Dmitry Orlov90.420.420.070.351.30435
Jacob Trouba650.370.370.090.281.81459
Darnell Nurse250.320.320.090.232.15484
Nolan Allan50.280.270.120.150.6551
Michael Kesselring70.240.230.040.191.41205
Luca Cagnoni420.230.210.080.131.4018
Libor Hajek240.220.060.040.020.7833
Sam Dickinson60.190.190.010.180.9484
Goalies: per-game rates
Goalies#
Eric Comrie1.8962.7123.326.054:3996
Alex Nedeljkovic33.8972.8324.727.657:04195
Yaroslav Askarov30.8893.2025.628.856:1772
How these rates are built — the club is their current one, re-checked weekly; the rates span every season since 2021-22

Each player is listed under the club the NHL had them on when we last checked, and clubs are re-checked once a week: a player traded in the last few days can still appear under their previous club, while the rates beside them span every season since 2021-22, wherever they played them. Each rate is a recency-weighted per-game average, and Sample is the number of games it rests on.

Skaters are ordered by a points rate shrunk toward the league average in proportion to how little a player has played, so a one-game sample does not out-rank a career. That rate is Adj. P; PTS and the other columns are the recorded rates, and for anyone with a real sample Adj. P and PTS are nearly the same number. Save percentage is total saves over total shots, weighted, not the average of per-game save percentages, which would count a two-shot relief appearance as heavily as a forty-shot start.

2026-27 cap · 2025-26 production

Best cap efficiency

Best cap efficiency on this club: points per $1M of cap hit
#SkaterCap hitClub controlPTSPTS / $1M
1Macklin CelebriniC82$0.97M6 yrsUFA115117.9
2Will SmithC69$0.95M1 yrRFA5962.1
3Michael MisaC45$0.99M2 yrsRFA2121.3
4Igor ChernyshovL28$0.95M2 yrsRFA1920.1
5Sam DickinsonD72$0.95M2 yrsRFA1414.7

Club control counts every season a player is signed through, extensions included, so it can describe a different contract from the cap hit beside it. A player measured against the season that earned his contract looks expensive, so recent signings sit low and entry-level deals sit high, by construction. Cap efficiency across the league.