Kodo Hockey← San Jose Sharks
2026-27 season preview
San Jose Sharks
40-35-989 pts22nd of 32
Goals for
3.1
17th in the league
Goals against
3.3
30th in the league
Power play
21.2%
16th in the league
Kodo projects the San Jose Sharks for 40-35-9 (89 pts). The fantasy engine runs through Macklin Celebrini and Kiefer Sherwood. 3 core skaters project to rise and 3 to slip. Yaroslav Askarov 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
Yaroslav Askarov
Yaroslav Askarov projects the crease (~49 starts), but Alex Nedeljkovic (~34) makes it more timeshare than lock
Contents · 12 sections
ReportedMichael Misa — I think Warsofsky played for overtime there and it almost cost him. He put Misa's line on the ice and they got pinned after a Misa turnover. · @Real_Max_Miller ↗2026-10-04
ReportedJacob Trouba — Askarov and Forsberg lead their teams onto the ice. #SJSharks #GoKingsGo
Jacob Trouba IS on the ice for warmups. · @Real_Max_Miller ↗2026-10-04
InjuryJacob Trouba — Day-To-Day — Illness · CBS2026-10-03
Each item names its source. Kodo's own projected line changes are not reported here.
25-2626-27Change
Goals for3.0417th3.1016th+0.06▲1
Goals against3.5430th3.3028th-0.24▲2
Power play21.216th21.1416th=-0.06~
Penalty kill76.426th77.6225th+1.22▲1
Faceoffs47.826th49.0422nd+1.24▲4
Points percentage0.52423rd0.53022nd+0.006▲1
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
Light nights
31%10th
26 of 84 games
Four-game weeks
102nd
7 weeks of two or fewer
Back-to-backs
1325th
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.
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.
Alex Barré-Boulet — Undisclosed: IR. Expected to be out until at least Oct 5
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
In Marchment, Sherwood, Trouba, Nurse, Kesselring, Leason, Allan, Barré-Boulet, Sahlin-Wallenius, Gasseau, Samsonov, Keyser
Callup Cagnoni, Stenberg, Giles, Allan, Musty
Out Eklund→OTT, Klingberg, Ferraro, Kurashev, Skinner→UFA, Mukhamadullin→EDM, Liljegren→WSH, Regenda
18.2→19.1 +0.9
21→21.8 +0.8
20.5→19.7 -0.8
14.2→13.4 -0.8
21.2→20.3 -0.9
13.4→12.5 -0.9
17.3→15.8 -1.5
8.6→6.8 -1.8
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 — quarterbackUNDERDEPLOYED8 pts at stake
holds it
22 proj pts · 17.7′ · 2.8′ PP
vs
pushing
30 proj pts · 20.3′ · 2.6′ PP
Luca Cagnonimodel favours the challengerDmitry Orlov
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 — forward slotUNDERDEPLOYED7.8 pts at stake
holds it
46 proj pts · 19.7′ · 3.3′ PP
vs
pushing
32 proj pts · 15.8′ · 1.9′ PP
Alexander Wennbergmodel favours the challengerKiefer Sherwood
Conversion
21.2%
on the man advantage
PP goals
63
559 shots
Expected goals
56
+7 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′67%825336.658.973%1.44
2.61′48%117185.041.255%1.09
1.16′21%3364.71.9—1
3.25′60%89174.555.653%0.99
1.33′25%2244.021.8—0.89
2.97′55%69153.8410.555%0.83
3.34′62%87153.375.350%0.73
0.62′11%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)Toffoli15 PPP (15 last yr)Smith21 PPP (17 last yr)Cagnoni8 PPP
Projected PP2Orlov11 PPP (18 last yr)Sherwood8 PPP (12 last yr)Chernyshov9 PPP (5 last yr)Marchment8 PPP (7 last yr)Stenberg11 PPP
Hits
18645th
projected, this roster · of 32
Blocks
120013th
projected, this roster · of 32
Shots
244010th
projected, this roster · of 32
Penalty minutes
71411th
projected, this roster · of 32
Faceoff wins
212121st
projected, this roster · of 32
H+B
30648th
projected, this roster · of 32
S+H+B
55046th
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.
$81.3Mcommitted · 25 of 26 on file
6reach the market after this season
Pending free agents · this summer
| Dmitry OrlovD | UFA | $6.50M | 30 pts |
|---|---|---|---|
| Barclay GoodrowC | UFA | $3.64M | 4 pts |
| Adam GaudetteR | UFA | $2.00M | 19 pts |
| Yaroslav AskarovG | RFA | $2.00M | |
| Will SmithR | RFA | $0.95M | 68 pts |
| Luca CagnoniD | RFA | $0.92M | 22 pts |
Free the summer after
| Tyler ToffoliR | $6.00M | 48 pts |
|---|---|---|
| Alex NedeljkovicG | $3.00M | |
| Ty DellandreaC | $1.63M | 11 pts |
| Michael MisaC | $0.99M | 31 pts |
| Sam DickinsonD | $0.95M | 19 pts |
| Igor ChernyshovL | $0.95M | 37 pts |
| Quentin MustyC | $0.91M | 2 pts |
| Patrick GilesR | $0.88M | |
| Nolan AllanD | $0.88M | 6 pts |
Biggest cap hits
| Darnell NurseD | $9.25M | 3y left · NMC |
|---|---|---|
| Jacob TroubaD | $8.25M | 3y left · NTC |
| Mason MarchmentL | $6.75M | 4y left · NTC |
| Dmitry OrlovD | $6.50M | final yr · M-NTC |
| Tyler ToffoliR | $6.00M | 1y left · NTC |
| Alexander WennbergC | $6.00M | 2y left · NTC |
| Kiefer SherwoodL | $5.75M | 4y left · NTC |
| Michael KesselringD | $4.50M | 2y left |
Cap hits from CapWages for the 25 men on file. A dash is a figure we do not have, usually a restricted free agent whose entry-level deal has lapsed, and those are left out of the committed total rather than counted as nothing.
GSAx view
Askarov
47 NHL starts last season
GSAx / start
-0.437lg -0.04059th pctile
Shot quality faced
0.1017lg 0.10430th hardest
10-start rolling GSAx · appearance 1-74 · shared scale
2026-27 projection
49 GS23 W (13–29)0.887 SV%3.33 GAA
2025-26 actual · NHL
47 GS21 W0.884 SV%3.63 GAA
Nedeljkovic
34 NHL starts last season
GSAx / start
-0.064lg -0.040548th pctile
Shot quality faced
0.1018lg 0.10431st hardest
10-start rolling GSAx · appearance 1-81 · shared scale
2026-27 projection
34 GS17 W (10–22)0.890 SV%3.45 GAA
2025-26 actual · NHL
34 GS18 W0.896 SV%2.87 GAA
Brossoitgone
1 NHL start last season
GSAx / start
—Shot quality faced
0.1496lg 0.104100th hardest
10-start rolling GSAx · appearance 1-6 · shared scale
2026-27 projection
— GS— W— SV%— GAA
2025-26 actual · NHL
1 GS0 W0.783 SV%6.09 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.
Projections — Every Skater
the reference table · Overall is value as a z-score against the league, so +1.00 is a standard deviation above average‹ 12 / 12Forwards · 18
| Player | Role | Age | Overall | ADP | GP | G | A | P / if fit | PPP | SHP | SOG | HIT | BLK | PIM | +/- | FOW | H+B | S+H+B | Trajectory | Signals |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Macklin Celebrini | L1·PP1 | 20 | +3.45 | 4.2 | 79 | 43 | 76 | 119.3 | 37 | 0 | 314 | 51 | 52 | 40 | -1 | 600 | 103 | 417 | ascendingsell-highPP1 | |
| Kiefer Sherwood | L4·PP2 | 31 | +1.02 | 191.6 | 75 | 17 | 15 | 31.9 | 8 | 1 | 152 | 360 | 30 | 46 | -9 | 18 | 390 | 541 | ice time ↓ | |
| Will Smith | L1·PP1 | 21 | +0.97 | 118 | 77 | 25 | 43 | 68.2 | 21 | 0 | 188 | 19 | 18 | 21 | -2 | 32 | 37 | 225 | ascendingsell-highPP1 | |
| Ivar Stenberg | L3·PP2 | 19 | +0.43 | 179.7 | 87 | 16 | 32 | 48.5 | 11 | 0 | 181 | 60 | 32 | 19 | 0 | 14 | 91 | 272 | — | |
| Tyler Toffoli | L2·PP1 | 34 | +0.42 | 223.9 | 77 | 22 | 27 | 48.3 | 15 | 0 | 173 | 50 | 28 | 12 | -11 | 9 | 78 | 251 | PP1 | |
| Mason Marchment | L1·PP2 | 31 | +0.40 | 208 | 73 | 20 | 27 | 47.2/53 | 8 | 0 | 127 | 90 | 29 | 58 | +13 | 14 | 120 | 247 | sell-high | |
| Alexander Wennberg | L3·PP1 | 32 | +0.01 | 255.8 | 81 | 14 | 32 | 46.1 | 13 | 1 | 87 | 31 | 87 | 18 | -19 | 622 | 118 | 205 | PP1 | |
| Collin Graf | L3 | 24 | -0.20 | 279.3 | 71 | 17 | 22 | 38.5 | 2 | 3 | 104 | 73 | 40 | 15 | +2 | 7 | 114 | 218 | ascending | |
| Igor Chernyshov | L2·PP2 | 21 | -0.40 | 210.5 | 49 | 16 | 21 | 36.7/61 | 9 | 0 | 97 | 34 | 24 | 16 | +1 | 0 | 58 | 155 | — | |
| Michael Misa | L2 | 19 | -0.69 | 207.8 | 55 | 13 | 18 | 31/46 | 4 | 0 | 88 | 22 | 28 | 17 | -1 | 200 | 50 | 138 | — | |
| Adam Gaudette | 30 | -0.86 | 292.4 | 69 | 12 | 7 | 18.6 | 4 | 0 | 87 | 56 | 31 | 19 | -2 | 143 | 87 | 174 | |||
| Ty Dellandrea | L4 | 26 | -0.86 | 292.5 | 61 | 4 | 7 | 10.9 | 0 | 1 | 60 | 153 | 43 | 37 | -15 | 282 | 196 | 256 | ||
| Barclay Goodrow | 33 | -1.55 | 292.5 | 38 | 2 | 2 | 4.2 | 0 | 0 | 30 | 67 | 28 | 33 | -8 | 35 | 95 | 125 | declining | ||
| Brett Leason | L4 | 27 | -1.56 | — | 41 | 3 | 4 | 7.2/14 | 0 | 0 | 44 | 46 | 25 | 12 | -3 | 13 | 72 | 116 | ice time ↓ | |
| Zack Ostapchuk | 23 | -1.71 | 292.5 | 30 | 2 | 1 | 2.8 | 0 | 0 | 23 | 68 | 14 | 22 | -3 | 109 | 82 | 105 | |||
| Quentin Musty | 21 | -2.10 | 292.3 | 6 | 1 | 1 | 2.2/22 | 0 | 0 | 8 | 10 | 3 | 3 | 0 | 23 | 14 | 22 | — | ||
| Patrick Giles | 26 | -2.25 | — | 1 | 0 | 0 | 0/4 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | — | ||
| Alex Barré-Boulet | 29 | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — | — |
Shading is that man's percentile among all projected forwards in the league, not among these 18. top 10% top 20 top 30 bottom 20. H+B and S+H+B take the weaker leg, because good at both is a floor rather than an average. P, SHP, PIM, +/- and FOW are unshaded — the percentile block does not carry them.
Defence · 7
| Player | Role | Age | Overall | ADP | GP | G | A | P / if fit | PPP | SHP | SOG | HIT | BLK | PIM | +/- | FOW | H+B | S+H+B | Trajectory | Signals |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Darnell Nurse | LD1 | 31 | +0.56 | 162.4 | 81 | 7 | 19 | 25.5 | 1 | 2 | 162 | 144 | 157 | 91 | -3 | 0 | 301 | 464 | decliningbounce-back | |
| Jacob Trouba | RD2 | 32 | +0.37 | 138.2 | 80 | 6 | 20 | 25.9 | 1 | 1 | 143 | 153 | 165 | 44 | -2 | 0 | 318 | 460 | ||
| Dmitry Orlov | LD3·PP2 | 35 | -0.21 | 239.3 | 76 | 5 | 26 | 30.3 | 11 | 0 | 88 | 109 | 68 | 37 | -11 | 0 | 176 | 264 | bounce-back | |
| Luca Cagnoni | RD3·PP1 | 22 | -0.64 | 260.8 | 84 | 4 | 18 | 22.2 | 8 | 0 | 63 | 64 | 97 | 28 | 0 | 0 | 161 | 224 | — | PP1 |
| Michael Kesselring | RD1 | 26 | -0.65 | 278.5 | 71 | 3 | 9 | 12 | 1 | 0 | 105 | 69 | 72 | 83 | +2 | 0 | 141 | 246 | declining | |
| Sam Dickinson | LD2 | 20 | -0.68 | 279.7 | 76 | 4 | 15 | 18.7 | 0 | 0 | 91 | 79 | 82 | 28 | -2 | 0 | 161 | 252 | — | |
| Nolan Allan | 23 | -1.59 | 292.6 | 39 | 1 | 5 | 6.1/12 | 0 | 0 | 22 | 55 | 47 | 17 | -3 | 0 | 102 | 124 | — |
Shading is that man's percentile among all projected defencemen in the league, not among these 7. top 10% top 20 top 30 bottom 20. H+B and S+H+B take the weaker leg, because good at both is a floor rather than an average. P, SHP, PIM, +/- and FOW are unshaded — the percentile block does not carry them.
Goalies · 4
| Goalie | GS | W | L | OTL | SV% | GAA | SV | SA | GA | SHO | GSAx | GSAx/GS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Yaroslav Askarov | 49 | 23 | 21 | 5 | 0.887 | 3.33 | 1246 | 1405 | 159 | 0.0 | -20.6 | -0.437 |
| Alex Nedeljkovic | 34 | 17 | 14 | 4 | 0.890 | 3.45 | 931 | 1045 | 114 | 0.3 | -2.2 | -0.064 |
| Jakub Skarek | — | — | — | — | — | — | — | — | — | — | 0 | — |
| Laurent Brossoit | — | — | — | — | — | — | — | — | — | — | -2.6 | — |
Projected record and projected ice time are model estimates; ranks, lines, projections and trend signals trace to real data.