← New York Rangers
2026-27 season preview
New York Rangers
41-33-1092 pts18th of 32
Goals for
2.89
23rd in the league
Goals against
2.95
15th in the league
Power play
24.7%
5th in the league
Kodo projects the New York Rangers for 41-33-10 (92 pts), carried by 5th-ranked power play. In a norfolk in chance league, the fantasy value runs through Will Cuylle and J.T. Miller. 2 core skaters project to rise and 2 to slip. Igor Shesterkin is the projected starter.
Your categories · using the preset above
The crease
Igor Shesterkin
Igor Shesterkin projects the crease (~54 starts), but Joonas Korpisalo (~30) makes it more timeshare than lock
Contents · 12 sections
TransactionMatthew Robertson added to NYR roster · NHL transactions2026-08-13
TransactionMarcus Pettersson added to NYR roster · NHL transactions2026-08-13
TransactionDrew Fortescue added to NYR roster · NHL transactions2026-08-13
TransactionAdam Fox added to NYR roster · NHL transactions2026-08-13
TransactionVladislav Gavrikov added to NYR roster · NHL transactions2026-08-13
Each item names its source. Kodo's own projected line changes are not reported here.
Carried into camp
Matt Rempe — Probable for start of season — Thumb · CBS2026-03-23 · 150d
Reported more than 30 days ago, so listed as a standing condition rather than news. The date is the one the injury feed carries, which is the game the player was expected to miss — not the day anything was last checked.
25-2626-27Change
Goals for2.8723rd2.8926th+0.02▼3
Goals against3.0415th2.959th-0.09▲6
Power play24.75th28.521st+3.82▲4
Penalty kill79.915th79.5617th-0.34▼2
Faceoffs54.52nd52.702nd-1.80
Points percentage0.46930th0.54818th+0.079▲12
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–Nov10-07 – 11-16
50%10-10
for2.55
against2.45
Nov–Jan11-18 – 12-29
43%9-12
for2.62
against2.90
Jan–Mar12-31 – 03-05
25%5-15
for3.00
against4.10
Mar–Apr03-07 – 04-15
48%10-11
for3.43
against2.76
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
34.5%2nd
29 of 84 games
Four-game weeks
712th
6 weeks of two or fewer
Back-to-backs
1112th
roughly one backup start each
Playoff-week games
107th
over 3 weeks · 1 back-to-back
Games per week
2026-09-28on light nights2027-04-05
Games by month
Sep*
1Oct
13Nov
13Dec
14Jan
15Feb
10Mar
14Apr*
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
Life after Panarin
Artemi Panarin played his last game for this club on 2026-01-26 and is now in LAK. Team scoring went 2.66 → 3.24 goals a game over the 29 games after.
Defence — who took the minutes
toiafterΔp/gmafterΔ
Forwards
toiafterΔp/gmafterΔ
Not a controlled experiment — the same window also saw Edstrom leave 2026-03-23, Trocheck leave 2026-04-15, Carrick leave 2026-03-02, Sheary leave 2026-04-15, Borgen leave 2026-04-15, Othmann leave 2026-02-26, Kartye arrive 2026-02-28, Sykora arrive 2026-03-25. Read the deltas as role changes, not pure cause and effect.
In Robertson, Pettersson, Fortescue, Fox, Gavrikov, Iorio, Shesterkin, Garand, Vaakanainen, Schneider, Bjorkstrand, Veleno
Callup Greentree, Fortescue, Emery, Smits, Beaudoin, Sykora
Out Panarin→LAK, Trocheck→UTA, Sheary, Brodzinski, Carrick→BUF, Borgen→BOS, Soucy→UFA, Edstrom→NSH
16→16.9 +0.9
12→12.6 +0.6
19.3→19.9 +0.6
16.9→16.5 -0.4
21.5→21.1 -0.4
23.7→23 -0.7
20.1→18.5 -1.6
20.4→18.5 -1.9
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 slotUNDERDEPLOYED5.3 pts at stake
holds it
57 proj pts · 17.8′ · 2.6′ PP
vs
pushing
40 proj pts · 16.5′ · 1.5′ PP
Alexis Lafrenièremodel favours the challengerWill Cuylle
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 — quarterback5.8 pts at stake
holds it
70 proj pts · 23.3′ · 3.8′ PP
vs
pushing
29 proj pts · 19.9′ · 1.4′ PP
Adam Foxmodel favours the incumbentSean Durzi
Conversion
24.7%
on the man advantage
PP goals
84
726 shots
Expected goals
69.9
+14.1 vs actual
Shooting
11.6%
of PP shots go in
Who it runs through · last season
PlayerPP/gmSharePPGPPAPPP/60ixGIPPFocal
3.28′57%1619357.899.264%1.16
3.35′58%519247.821.661%1.15
0.72′12%3255.60.9—0.83
3.14′54%613195.337.457%0.79
1.42′25%3365.191.352%0.76
2.47′43%96154.445.446%0.65
1.57′27%4483.745.661%0.55
1.18′20%3363.731.252%0.54
0.55′10%00000.1—0
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 PP1Zibanejad30 PPP (35 last yr)Miller24 PPP (19 last yr)Lafrenière13 PPP (15 last yr)Fox29 PPP (24 last yr)Dorofeyev24 PPP (30 last yr)
Projected PP2Cuylle7 PPP (8 last yr)Perreault7 PPP (6 last yr)Laba4 PPP (5 last yr)Bjorkstrand13 PPP (14 last yr)Durzi6 PPP (4 last yr)
Hits
21464th
projected, this roster · of 32
Blocks
13838th
projected, this roster · of 32
Shots
228326th
projected, this roster · of 32
Penalty minutes
69919th
projected, this roster · of 32
Faceoff wins
176329th
projected, this roster · of 32
H+B
35294th
projected, this roster · of 32
S+H+B
58128th
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.
$107.1Mcommitted · 27 of 29 on file
14reach the market after this season
Pending free agents · this summer
| Braden SchneiderD | RFA | $5.50M | 17 pts |
|---|---|---|---|
| Oliver BjorkstrandR | UFA | $4.50M | 38 pts |
| Will CuylleL | RFA | $3.90M | 40 pts |
| Urho VaakanainenD | UFA | $1.55M | 11 pts |
| Taylor RaddyshR | UFA | $1.50M | 18 pts |
| Tye KartyeL | RFA | $1.25M | 18 pts |
| Joe VelenoC | UFA | $1.20M | 6 pts |
| Matt RempeC | RFA | $0.97M | 2 pts |
| Gabe PerreaultR | RFA | $0.94M | 31 pts |
| Noah LabaC | RFA | $0.91M | 28 pts |
| Jaroslav ChmelarR | RFA | $0.89M | 6 pts |
| Adam SykoraL | RFA | $0.88M | 3 pts |
| Matthew RobertsonD | RFA | $0.81M | 10 pts |
| Vincent IorioD | RFA | — | 2 pts |
Free the summer after
| Sean DurziD | $6.00M | 29 pts |
|---|---|---|
| Joonas KorpisaloG | $4.00M | |
| Drew FortescueD | $0.92M | 2 pts |
Biggest cap hits
| Igor ShesterkinG | $11.50M | 6y left · NMC |
|---|---|---|
| Pavel DorofeyevR | $11.00M | 6y left |
| Adam FoxD | $9.50M | 2y left · NMC |
| Mika ZibanejadC | $8.50M | 3y left · NMC |
| J.T. MillerC | $8.00M | 3y left · NMC |
| Alexis LafrenièreL | $7.45M | 5y left |
| Vladislav GavrikovD | $7.00M | 5y left · NMC |
| Sean DurziD | $6.00M | 1y left · M-NTC |
Cap hits from CapWages for the 27 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
Shesterkin
51 starts last season
GSAx / start
-0.517lg -0.858188th
Shot quality faced
0.0699lg 0.073119th hardest
10-start rolling GSAx · appearance 1-69 · shared scale
2026-27 projection
54 GS26 W (16–35)0.909 SV%2.66 GAA
Korpisalo
28 starts last season
GSAx / start
-1.04lg -0.858131th
Shot quality faced
0.0716lg 0.073134th hardest
10-start rolling GSAx · appearance 1-82 · shared scale
2026-27 projection
30 GS14 W (9–20)0.898 SV%3.15 GAA
Garand
3 starts last season
GSAx / start
—Shot quality faced
0.078lg 0.073191th hardest
10-start rolling GSAx · appearance 1-12 · shared scale
2026-27 projection
— GS— W— SV%— 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‹ 12 / 12Forwards · 16
| Player | Role | Overall | GP | G | A | P / if | PPP | SHP | SOG | HIT | BLK | PIM | +/- | FOW | H+B | S+H+B | Trajectory | Signals |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Will Cuylle | L2·PP2 | +1.70 | 80 | 20 | 20 | 40.2 | 7 | 1 | 152 | 289 | 57 | 56 | -3 | 30 | 346 | 498 | ||
| J.T. Miller | L2·PP1 | +1.68 | 75 | 23 | 45 | 68.6 | 24 | 2 | 150 | 150 | 42 | 43 | -17 | 622 | 192 | 341 | bounce-backice time ↓PP1 | |
| Mika Zibanejad | L1·PP1 | +1.48 | 79 | 27 | 43 | 70.6 | 30 | 3 | 192 | 80 | 47 | 17 | -13 | 554 | 127 | 318 | PP1 | |
| Pavel Dorofeyev | L2·PP1 | +0.86 | 75 | 32 | 23 | 54.5 | 24 | 0 | 206 | 26 | 29 | 24 | 0 | 1 | 55 | 262 | ascendingPP1 | |
| Alexis Lafrenière | L1·PP1 | +0.76 | 81 | 24 | 34 | 57.2 | 13 | 0 | 166 | 93 | 36 | 27 | -6 | 11 | 129 | 295 | ascendingPP1 | |
| Oliver Bjorkstrand | L3·PP2 | +0.01 | 78 | 16 | 22 | 37.7 | 13 | 0 | 131 | 77 | 34 | 16 | -8 | 7 | 111 | 242 | decliningbounce-back | |
| Tye Kartye | L3 | -0.18 | 67 | 7 | 11 | 18.3 | 0 | 0 | 64 | 183 | 38 | 40 | -5 | 6 | 220 | 284 | ||
| Noah Laba | L3·PP2 | -0.22 | 73 | 11 | 17 | 28.3 | 4 | 0 | 85 | 108 | 41 | 32 | +1 | 329 | 149 | 234 | — | |
| Gabe Perreault | L1·PP2 | -0.78 | 53 | 13 | 17 | 30.8/47 | 7 | 0 | 88 | 30 | 25 | 15 | 0 | 5 | 54 | 142 | — | |
| Joe Veleno | -0.85 | 66 | 3 | 3 | 6 | 0 | 0 | 60 | 153 | 35 | 20 | -11 | 188 | 188 | 248 | decliningbounce-back | ||
| Taylor Raddysh | L4 | -1.08 | 71 | 7 | 11 | 17.8 | 1 | 1 | 68 | 55 | 36 | 15 | -1 | 6 | 91 | 159 | ||
| Matt Rempe | L4 | -1.18 | 40 | 1 | 1 | 1.5 | 0 | 0 | 29 | 121 | 13 | 36 | 0 | 4 | 134 | 163 | ||
| Jaroslav Chmelar | L4 | -1.42 | 41 | 3 | 3 | 6.1/12 | 0 | 0 | 44 | 75 | 22 | 18 | -1 | 0 | 97 | 141 | — | |
| Liam Greentree | -1.52 | 26 | 5 | 5 | 10/23 | 1 | 0 | 38 | 45 | 14 | 19 | 0 | 0 | 59 | 97 | — | ||
| Cole Beaudoin | -1.54 | 30 | 4 | 8 | 12/25 | 1 | 0 | 54 | 43 | 16 | 10 | 0 | 0 | 59 | 113 | — | ||
| Adam Sykora | -2.20 | 12 | 1 | 2 | 3/16 | 0 | 0 | 13 | 17 | 7 | 3 | 0 | 0 | 24 | 37 | — |
Defence · 11
| Player | Role | Overall | GP | G | A | P / if | PPP | SHP | SOG | HIT | BLK | PIM | +/- | FOW | H+B | S+H+B | Trajectory | Signals |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Adam Fox | D1·PP1 | +1.35 | 74 | 11 | 59 | 70.1/77 | 29 | 2 | 126 | 36 | 105 | 30 | +8 | 0 | 140 | 266 | PP1 | |
| Braden Schneider | D3 | +0.23 | 79 | 3 | 14 | 17.1 | 0 | 1 | 98 | 154 | 135 | 24 | -1 | 0 | 289 | 387 | ice time ↓ | |
| Sean Durzi | D2·PP2 | +0.22 | 67 | 6 | 23 | 28.7/35 | 6 | 0 | 92 | 44 | 106 | 52 | -6 | 0 | 150 | 242 | ||
| Marcus Pettersson | D2 | +0.17 | 80 | 3 | 16 | 18.5 | 0 | 0 | 67 | 83 | 136 | 54 | -7 | 0 | 219 | 286 | ||
| Vladislav Gavrikov | D1 | +0.14 | 80 | 8 | 20 | 27.5 | 4 | 1 | 107 | 50 | 110 | 41 | +2 | 0 | 161 | 267 | ||
| Matthew Robertson | D3 | -0.65 | 61 | 3 | 7 | 10.1 | 0 | 0 | 67 | 89 | 71 | 32 | 0 | 0 | 160 | 227 | — | |
| Alberts Smits | -0.72 | 47 | 8 | 10 | 18/28 | 2 | 0 | 84 | 58 | 73 | 15 | 0 | 0 | 131 | 215 | — | ||
| Urho Vaakanainen | -1.14 | 68 | 1 | 10 | 10.7 | 0 | 0 | 51 | 29 | 63 | 27 | +1 | 0 | 92 | 144 | |||
| Vincent Iorio | -1.62 | 38 | 0 | 2 | 2.2 | 0 | 0 | 25 | 23 | 53 | 18 | -1 | 0 | 76 | 101 | — | ||
| EJ Emery | -1.94 | 17 | 1 | 3 | 4/14 | 0 | 0 | 13 | 23 | 26 | 9 | 0 | 0 | 49 | 62 | — | ||
| Drew Fortescue | -2.13 | 9 | 1 | 1 | 2/13 | 0 | 0 | 13 | 14 | 14 | 7 | 0 | 0 | 28 | 41 | — |
Goalies · 3
| Goalie | GS | W | L | OTL | SV% | GAA | SV | SA | GA | SHO | GSAx | GSAx/GS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Igor Shesterkin | 54 | 26 | 22 | 6 | 0.909 | 2.66 | 1391 | 1531 | 140 | 2.5 | -26.4 | -0.517 |
| Joonas Korpisalo | 30 | 14 | 12 | 4 | 0.898 | 3.15 | 816 | 907 | 92 | 1.4 | -29.1 | -1.04 |
| Dylan Garand | — | — | — | — | — | — | — | — | — | — | +2.5 | — |
Projected record and projected ice time are model estimates; ranks, lines, projections and trend signals trace to real data.