← San Jose Sharks
2026-27 season preview
San Jose Sharks
39-36-987 pts28th of 32
Goals for
2.97
17th in the league
Goals against
3.28
30th in the league
Power play
21.2%
16th in the league
Kodo projects the San Jose Sharks for 39-36-9 (87 pts), carried by 16th-ranked power play. The fantasy engine runs through Macklin Celebrini and Kiefer Sherwood. 3 core skaters project to rise and 3 to slip. Alex Nedeljkovic is the projected starter.
Your categories · using the preset above
Regression watch
finishing/on-ice luck ran hot — expect some pullback off last year's line
The crease
Alex Nedeljkovic
Alex Nedeljkovic projects the crease (~40 starts)
Contents · 11 sections
TransactionDarnell Nurse added to SJS roster · NHL transactions2026-08-13
TransactionMichael Misa added to SJS roster · NHL transactions2026-08-13
TransactionMacklin Celebrini added to SJS roster · NHL transactions2026-08-13
TransactionCollin Graf added to SJS roster · NHL transactions2026-08-13
TransactionBarclay Goodrow added to SJS roster · NHL transactions2026-08-13
Each item names its source. Kodo's own projected line changes are not reported here.
25-2626-27Change
Goals for3.0417th2.9723rd-0.07▼6
Goals against3.5430th3.2828th-0.26▲2
Power play21.216th19.6120th-1.59▼4
Penalty kill76.426th78.9526th+2.55
Faceoffs47.826th48.3430th+0.54▼4
Points percentage0.52423rd0.51828th-0.006▼5
How these projections are made
Both bars share one scale per row — the wider of the two league ranges — so a bar that moves is a number that moved. The grey tick is that column's league average, and the arrow is the change in league rank. Goals-against and expected-goals-against rank ascending, so low is good and green always means improved.
Goals for, goals against and points percentage come from the roster: summed player projections over the real 26-27 schedule, last season's expected goals against regressed toward the league and adjusted for projected goaltending, and a game-by-game simulation of that schedule.
The special teams are projected from how much each number actually carries over year to year, measured across 298 team-season pairs going back to 2010-11. The power play uses one prior season shrunk 0.36; the penalty kill uses three, weighted .5/.3/.2 and shrunk 0.38. Special teams barely persist, so a league-worst penalty kill is mostly bad luck and comes most of the way back, while the faceoff dot is a repeatable team skill. How far a club moves depends on how far from the mean it started: Vancouver sits near league average on both the power play and the faceoff dot and barely shifts, while Edmonton's 30.6% power play comes back to the middle and this penalty kill, worst in the league, is the biggest riser of all 32.
The penalty kill averages three seasons because one measures it so badly. Split a single season's spread into ability and sampling noise — a kill rate is a binomial over about 250 opportunities, so the noise is known rather than guessed — and only 33% of the gap between clubs is real. Divide the year-over-year correlation by that and the ability underneath comes out near 1.0: penalty killing is almost perfectly persistent and merely hard to see in one season. The cure for a noisy measurement is more of it, and three seasons beat one by 4.3% out of sample. Because averaging reorders clubs, that row's rank is a real forecast; the rows marked = are single-season regressions, which compress the values but keep them in order, so their rank is last season's by construction and only the value is a prediction.
A roster-aware faceoff model was built and rejected on the evidence. Draw counts are not stored, but they can be recovered exactly from the per-game percentages, and with real draws a club's number reconstructs from its own centres. It still does not predict better: given a club's first half to learn from and even handed the second half's draw distribution, it scored 2.24 against the plain regression's 2.26 across 32 clubs, and the best blend of the two puts almost no weight on it. A team's faceoff percentage already carries who takes its draws. Expected goals has no team projection at all, so that row shows last season alone.
Goals for, goals against and points percentage come from the roster: summed player projections over the real 26-27 schedule, last season's expected goals against regressed toward the league and adjusted for projected goaltending, and a game-by-game simulation of that schedule.
The special teams are projected from how much each number actually carries over year to year, measured across 298 team-season pairs going back to 2010-11. The power play uses one prior season shrunk 0.36; the penalty kill uses three, weighted .5/.3/.2 and shrunk 0.38. Special teams barely persist, so a league-worst penalty kill is mostly bad luck and comes most of the way back, while the faceoff dot is a repeatable team skill. How far a club moves depends on how far from the mean it started: Vancouver sits near league average on both the power play and the faceoff dot and barely shifts, while Edmonton's 30.6% power play comes back to the middle and this penalty kill, worst in the league, is the biggest riser of all 32.
The penalty kill averages three seasons because one measures it so badly. Split a single season's spread into ability and sampling noise — a kill rate is a binomial over about 250 opportunities, so the noise is known rather than guessed — and only 33% of the gap between clubs is real. Divide the year-over-year correlation by that and the ability underneath comes out near 1.0: penalty killing is almost perfectly persistent and merely hard to see in one season. The cure for a noisy measurement is more of it, and three seasons beat one by 4.3% out of sample. Because averaging reorders clubs, that row's rank is a real forecast; the rows marked = are single-season regressions, which compress the values but keep them in order, so their rank is last season's by construction and only the value is a prediction.
A roster-aware faceoff model was built and rejected on the evidence. Draw counts are not stored, but they can be recovered exactly from the per-game percentages, and with real draws a club's number reconstructs from its own centres. It still does not predict better: given a club's first half to learn from and even handed the second half's draw distribution, it scored 2.24 against the plain regression's 2.26 across 32 clubs, and the best blend of the two puts almost no weight on it. A team's faceoff percentage already carries who takes its draws. Expected goals has no team projection at all, so that row shows last season alone.
They held about the same pace all year — -2 points of win percentage between the first quarter and the last.
Oct–Nov10-09 – 11-18
45%9-11
for2.95
against3.25
Nov–Jan11-20 – 01-03
52%11-10
for3.24
against3.86
Jan–Mar01-06 – 03-07
50%10-10
for3.10
against3.30
Mar–Apr03-10 – 04-16
43%9-12
for2.95
against3.81
Why the shape matters more than the total
Every rate on the board above is a season average, and an average cannot tell a club that was good all year from one that was excellent for six weeks and ordinary after. Those are different teams to draft into: the second one probably changed — an injury, a call-up, a coach — and whatever changed is likelier to still be true in October than the average is.
Read it against the roster movement section. A club that faded and then lost its best defenceman is not going to bounce; one that faded while carrying injuries and got everybody back is a different case entirely.
Quarters are equal shares of the games actually played, so the windows differ slightly by club depending on scheduling.
Read it against the roster movement section. A club that faded and then lost its best defenceman is not going to bounce; one that faded while carrying injuries and got everybody back is a different case entirely.
Quarters are equal shares of the games actually played, so the windows differ slightly by club depending on scheduling.
Light nights
31%9th
26 of 84 games
Four-game weeks
102nd
7 weeks of two or fewer
Back-to-backs
1322nd
roughly one backup start each
Playoff-week games
115th
over 3 weeks · 2 back-to-back
Games per week
2026-09-28on light nights2027-04-05
Games by month
Oct*
15Nov
14Dec
14Jan
13Feb
8Mar
16Apr*
4* part of a month — the season opens and closes mid-month.
Why these three and not a schedule score
They answer different formats, so combining them would hide the answer rather than give it. Four-game weeks decide weekly-lineup leagues: the same player produces a third more in a four-game week than a three-game one, and clubs differ by several such weeks across a season. Light nights decide daily leagues and streaming — a light night is one carrying no more than the league's median number of games (6 this season), so your man is a far larger share of everything available than he is on a sixteen-game Tuesday. Playoff-week games counts the last three weeks of the season excluding the final one — most head-to-head leagues have crowned a champion before the NHL finishes, and that last week is short and full of rested stars. Back-to-backs decide the crease, because a starter rarely takes both ends, so each one is roughly a start handed to the backup.
Ranks are against all 32 clubs, and the whole thing is read off the published slate — no projection is involved.
Ranks are against all 32 clubs, and the whole thing is read off the published slate — no projection is involved.
Lineup notes
This is the lineup the club can ice on opening night, not its best-case group. A player expected to miss more than the opening month is left out and his slot goes to whoever takes it — he keeps his projection below, he is out of the lineup, not out of the season.
Projected ice time totals 298.1 of the 300 skater-minutes a game has. Power play 3.16′ on the first unit, 1.79′ on the second; the kill 2.35′, 1.67′ and 1′ across three units — all measured.Change this lineup →
Forward lines
L1
L2
L3
L4
Defence pairs
D1
D2
D3
Special teams
Scratches & depth
Goodrow 5 ptsOstapchuk 2 ptsBystedt 13 ptsMusty 8 ptsCagnoni 4 ptsVerhoeffunsigned 4 ptsLinunsigned 3 pts
unsigned — drafted property with no NHL contract. They carry a projection but are not dressed in a line.
In Nurse, Misa, Celebrini, Graf, Goodrow, Gaudette, Dellandrea, Marchment, Comrie, Trouba, Kesselring, Sherwood
Callup Allan, Cagnoni, Bystedt, Musty
Out Eklund→OTT, Klingberg, Ferraro, Kurashev, Skinner→UFA, Mukhamadullin→EDM, Liljegren→WSH, Leddy
16.8→18.8 +2
15.1→17.1 +2
21.2→22.4 +1.2
12.8→13.9 +1.1
17.4→16.2 -1.2
21→19.7 -1.3
20.5→19 -1.5
14.2→12.6 -1.6
Biggest projected minute changes either way — a club's ice time is a fixed budget, so the departures above are what free it up. Full board →
Top power-play unit — forward slotUNDERDEPLOYED7.7 pts at stake
holds it
46 proj pts · 19′ · 3.3′ PP
vs
pushing
33 proj pts · 17.1′ · 1.9′ PP
Alexander Wennbergmodel favours the challengerKiefer Sherwood
1.40 more min/game on PP1 (measured PP1 vs PP2 minutes), at the challenger's own scoring rate over a full season. Power-play gaps come from measured PP minutes last season; even-strength gaps from the role model that drives every projection on this page.
Top power-play unit — quarterback8.9 pts at stake
holds it
30 proj pts · 22.4′ · 2.6′ PP
vs
pushing
12 proj pts · 14′ · 0′ PP
Dmitry Orlovmodel favours the incumbentMichael Kesselring
Conversion
21.2%
on the man advantage
PP goals
63
559 shots
Expected goals
51.4
+11.6 vs actual
Shooting
11.3%
of PP shots go in
What left the power play
Regenda carried 2% of the power-play points on 2% of its minutes — a focal score of 1.33. He is not on this roster.
Who it runs through · last season
PlayerPP/gmSharePPGPPAPPP/60ixGIPPFocal
3.63′52%825336.658.572%1.44
2.61′37%117185.041.258%1.09
1.16′16%3364.71.764%1
3.25′46%89174.555.357%0.98
1.33′19%2244.021.660%0.89
2.97′42%69153.849.351%0.83
3.34′47%87153.374.946%0.73
0.62′9%0111.20.2—0.26
What IPP, focal and ixG mean
IPP is the share of the power-play goals he was on the ice for that he got a point on. It is the direct form of “the play runs through him”: two men can take the same unit minutes while one touches the puck on every goal and the other watches from the far circle. League average on the power play is 59%. It is computed from the on-ice record of every goal, corrected for the ~18% of goals with no on-ice row and shrunk toward the positional mean for thin samples, so it cannot exceed 1.
Focal is the older, cruder version — his share of the team's power-play points over his share of its minutes. It is team-relative, so it moves when a teammate is injured; IPP does not. ixG is the expected goals from his own shots on the man advantage. Minutes are measured, not modelled.
Focal is the older, cruder version — his share of the team's power-play points over his share of its minutes. It is team-relative, so it moves when a teammate is injured; IPP does not. ixG is the expected goals from his own shots on the man advantage. Minutes are measured, not modelled.
Projected PP1Celebrini37 PPP (33 last yr)Wennberg13 PPP (15 last yr)Toffoli14 PPP (15 last yr)Smith21 PPP (17 last yr)Orlov11 PPP (18 last yr)
Projected PP2Misa6 PPP (4 last yr)Sherwood8 PPP (12 last yr)Chernyshov1 PPP (5 last yr)Marchment9 PPP (7 last yr)Kesselring1 PPP
Hits
19706th
projected, this roster · of 32
Blocks
116529th
projected, this roster · of 32
Shots
239418th
projected, this roster · of 32
Penalty minutes
72615th
projected, this roster · of 32
Faceoff wins
216617th
projected, this roster · of 32
H+B
313415th
projected, this roster · of 32
S+H+B
552810th
projected, this roster · of 32
What actually moves these numbers
Penalty-kill time is the strongest driver of blocks we can measure. Across 1,504 consecutive season pairs, a skater who gains a minute of shorthanded time a game adds a median 9.5 blocks over 84 games — a defenceman 16 — and the effect runs monotonically the other way too: lose a minute and it is −15.6, or −25.9 for a defenceman. The PK column is here for that reason.
But it is mostly the minutes, not the man. Shorthanded time correlates +0.52 with blocks per 60 across players and only +0.14 within the same player year to year, which says coaches pick shot-blockers for the kill more than the kill turns anyone into one. A player promoted onto PK1 gains blocks because he is on the ice for more shots, not because he changed — so price the ice time, not a breakout.
Hits run opposite to ice time. Total minutes correlate −0.57 with hits per 60, and −0.60 among forwards: the fourth line hits, the first line does not. So a rate beats a total here more than in any other category, which is what the /60 columns are for — a checker on eleven minutes at 12 hits per 60 is worth more than a first-liner at 5 on nineteen, and their season totals can look identical.
Power-play time predicts the absence of all of it — −0.43 with hits per 60, −0.47 with blocks, −0.26 with penalty minutes. The men who run a power play are not the men who supply a banger roster, which is why the two sections of this page rarely name anybody twice.
Faceoff wins are a volume category. They track draws taken far more than win rate, so the man to want is the one taking the most — normally the first-line centre and whoever takes defensive-zone draws on the kill.
Measured on 2022-23 through 2025-26, skaters with 40+ games and 200+ minutes at both ends of each pair.
But it is mostly the minutes, not the man. Shorthanded time correlates +0.52 with blocks per 60 across players and only +0.14 within the same player year to year, which says coaches pick shot-blockers for the kill more than the kill turns anyone into one. A player promoted onto PK1 gains blocks because he is on the ice for more shots, not because he changed — so price the ice time, not a breakout.
Hits run opposite to ice time. Total minutes correlate −0.57 with hits per 60, and −0.60 among forwards: the fourth line hits, the first line does not. So a rate beats a total here more than in any other category, which is what the /60 columns are for — a checker on eleven minutes at 12 hits per 60 is worth more than a first-liner at 5 on nineteen, and their season totals can look identical.
Power-play time predicts the absence of all of it — −0.43 with hits per 60, −0.47 with blocks, −0.26 with penalty minutes. The men who run a power play are not the men who supply a banger roster, which is why the two sections of this page rarely name anybody twice.
Faceoff wins are a volume category. They track draws taken far more than win rate, so the man to want is the one taking the most — normally the first-line centre and whoever takes defensive-zone draws on the kill.
Measured on 2022-23 through 2025-26, skaters with 40+ games and 200+ minutes at both ends of each pair.
GSAx view
Nedeljkovic
34 starts last season
GSAx / start
-0.96lg -0.858142th
Shot quality faced
0.0728lg 0.073146th hardest
10-start rolling GSAx · appearance 1-81 · shared scale
2026-27 projection
40 GS18 W (10–22)0.900 SV%3.18 GAA
Askarov
47 starts last season
GSAx / start
-1.254lg -0.858115th
Shot quality faced
0.0731lg 0.073149th hardest
10-start rolling GSAx · appearance 1-74 · shared scale
2026-27 projection
28 GS12 W (9–20)0.897 SV%3.04 GAA
Comrie
24 starts last season
GSAx / start
-1.141lg -0.858127th
Shot quality faced
0.0682lg 0.07316th hardest
10-start rolling GSAx · appearance 1-82 · shared scale
2026-27 projection
16 GS7 W (7–15)0.900 SV%2.76 GAA
GSAx is goals saved above expected — what the shots he faced were worth, minus what he actually allowed. Shown per start, because a backup cannot out-accumulate a starter but can out-perform him on a per-night basis. Shot quality faced is expected goals per shot: higher means he was hung out to dry more often, and the percentile is where that workload ranks among goalies with 15+ starts. Rolling form is a 10-start moving average — is he playing well lately; running total is the season's accumulated damage. Per-game GSAx is too noisy to read either from directly.
Every skater
the reference table · Overall is value as a z-score against the league, so +1.00 is a standard deviation above average‹ 11 / 11Forwards · 16
| Player | Role | Overall | GP | G | A | P / if | PPP | SHP | SOG | HIT | BLK | PIM | +/- | FOW | H+B | S+H+B | Trajectory | Signals |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Macklin Celebrini | L1·PP1 | +3.68 | 79 | 44 | 76 | 119.6 | 37 | 0 | 314 | 51 | 52 | 40 | -1 | 600 | 103 | 417 | ascendingsell-highPP1 | |
| Kiefer Sherwood | L2·PP2 | +1.32 | 74 | 18 | 15 | 33.3 | 8 | 1 | 150 | 355 | 30 | 45 | -9 | 18 | 385 | 534 | ||
| Will Smith | L1·PP1 | +1.32 | 77 | 27 | 43 | 70 | 21 | 0 | 189 | 19 | 18 | 21 | -2 | 32 | 37 | 226 | ascendingsell-highPP1 | |
| Mason Marchment | L3·PP2 | +0.75 | 74 | 20 | 28 | 48.2 | 9 | 0 | 129 | 92 | 30 | 59 | +13 | 15 | 122 | 251 | sell-high | |
| Tyler Toffoli | L3·PP1 | +0.64 | 74 | 21 | 26 | 46.9 | 14 | 0 | 166 | 48 | 27 | 12 | -10 | 9 | 75 | 241 | PP1 | |
| Ivar Stenberg | L4 | +0.41 | 65 | 13 | 27 | 40/45 | 5 | 0 | 167 | 82 | 36 | 5 | 0 | 0 | 118 | 285 | — | |
| Alexander Wennberg | L2·PP1 | +0.30 | 80 | 14 | 32 | 45.7 | 13 | 1 | 86 | 31 | 86 | 17 | -19 | 615 | 116 | 202 | ice time ↓PP1 | |
| Collin Graf | L2 | +0.15 | 72 | 17 | 22 | 39.1 | 3 | 3 | 106 | 74 | 41 | 15 | +2 | 7 | 115 | 221 | ascending | |
| Michael Misa | L3·PP2 | -0.39 | 53 | 12 | 18 | 29.9/46 | 6 | 0 | 85 | 21 | 27 | 17 | -1 | 192 | 48 | 133 | — | |
| Adam Gaudette | L4 | -0.57 | 65 | 12 | 6 | 18.8 | 4 | 0 | 82 | 53 | 29 | 17 | -2 | 135 | 82 | 164 | ||
| Barclay Goodrow | -0.59 | 77 | 2 | 3 | 4.8 | 0 | 0 | 61 | 135 | 57 | 67 | -17 | 71 | 192 | 252 | declining | ||
| Ty Dellandrea | L4 | -0.73 | 56 | 2 | 5 | 7.1 | 0 | 1 | 55 | 140 | 39 | 34 | -14 | 259 | 180 | 235 | ice time ↓ | |
| Zack Ostapchuk | -0.92 | 60 | 1 | 1 | 2.1 | 0 | 0 | 46 | 134 | 28 | 43 | -5 | 216 | 163 | 208 | |||
| Filip Bystedt | -1.01 | 30 | 5 | 8 | 13/27 | 2 | 0 | 64 | 41 | 16 | 6 | 0 | 0 | 57 | 121 | — | ||
| Igor Chernyshov | L1·PP2 | -1.30 | 23 | 4 | 5 | 9/24 | 1 | 0 | 25 | 37 | 13 | 12 | 0 | 0 | 50 | 75 | — | ice time ↑ |
| Quentin Musty | -1.31 | 22 | 4 | 4 | 8/23 | 1 | 0 | 33 | 34 | 12 | 10 | 0 | 0 | 46 | 79 | — |
Defence · 9
| Player | Role | Overall | GP | G | A | P / if | PPP | SHP | SOG | HIT | BLK | PIM | +/- | FOW | H+B | S+H+B | Trajectory | Signals |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Darnell Nurse | D2 | +0.81 | 80 | 6 | 19 | 24.8 | 1 | 2 | 160 | 142 | 155 | 90 | -3 | 0 | 298 | 458 | decliningbounce-back | |
| Jacob Trouba | D1 | +0.59 | 78 | 6 | 19 | 25 | 1 | 1 | 139 | 149 | 161 | 43 | -2 | 0 | 310 | 449 | ||
| Dmitry Orlov | D1·PP1 | +0.07 | 75 | 4 | 26 | 29.6 | 11 | 0 | 87 | 108 | 67 | 37 | -11 | 0 | 174 | 261 | bounce-backPP1 | |
| Michael Kesselring | D3·PP2 | -0.30 | 72 | 3 | 9 | 12.2 | 1 | 0 | 107 | 70 | 73 | 84 | +2 | 0 | 143 | 249 | declining | |
| Sam Dickinson | D2 | -0.46 | 72 | 3 | 14 | 17 | 0 | 0 | 86 | 75 | 78 | 27 | -2 | 0 | 153 | 239 | — | ice time ↑ |
| Nolan Allan | D3 | -1.49 | 20 | 1 | 2 | 3/9 | 0 | 0 | 14 | 30 | 31 | 14 | 0 | 0 | 61 | 75 | — | |
| Luca Cagnoni | -1.55 | 15 | 1 | 3 | 4/17 | 0 | 0 | 20 | 17 | 23 | 3 | 0 | 0 | 40 | 60 | — | ||
| Keaton Verhoeff | unsigned | -1.58 | 13 | 1 | 3 | 4/23 | 0 | 0 | 13 | 18 | 20 | 6 | 0 | 0 | 38 | 51 | — | |
| Ryan Lin | unsigned | -1.64 | 11 | 1 | 2 | 3/17 | 0 | 0 | 11 | 14 | 17 | 4 | 0 | 0 | 31 | 42 | — |
Goalies · 3
| Goalie | GS | W | L | OTL | SV% | GAA | SV | SA | GA | SHO | GSAx | GSAx/GS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alex Nedeljkovic | 40 | 18 | 15 | 5 | 0.900 | 3.18 | 1107 | 1230 | 124 | 0.4 | -32.6 | -0.96 |
| Yaroslav Askarov | 28 | 12 | 11 | 3 | 0.897 | 3.04 | 721 | 803 | 83 | 0.0 | -59 | -1.254 |
| Eric Comrie | 16 | 7 | 7 | 2 | 0.900 | 2.76 | 395 | 439 | 43 | 0.4 | -27.4 | -1.141 |
Projected record and projected ice time are model estimates; ranks, lines, projections and trend signals trace to real data.