Kodo Hockey← Calgary Flames
2026-27 season preview
Calgary Flames
33-42-975 pts31st of 32
Goals for
2.28
32nd in the league
Goals against
3
21st in the league
Power play
16.2%
31st in the league
Kodo projects the Calgary Flames for 33-42-9 (75 pts). In a categories league, the fantasy value runs through Matt Coronato and Morgan Frost on PP1. 2 core skaters project to rise and 6 to slip. Dustin Wolf is the projected starter.
Your categories · using the preset above
The crease
Dustin Wolf
Dustin Wolf projects the crease (~43 starts), but Devin Cooley (~40) makes it more timeshare than lock
Contents · 12 sections
ReportedMikael Backlund — Huska on decision to scratch Zary: “It's just understanding the importance of taking care of the puck. Connor's a good player -he'll get himself right back in there.”
The lines for tonight here in Van:
Gridin-Frost-Coronato
Honzek-Backlund-Farabee
Othmann-Strome-Sharangovich
Tsyplakov-Pospisil-Klapka
Bahl - Whitecloud
Hanley-Nemec
Middleton-Parekh
Cooley starts
Othmann will also see time on PP2. · @EricFrancis ↗2026-10-03
TransactionJonathan Castagna off CGY roster · NHL transactions2026-10-02
ReportedMikael Backlund — #Flames tonight in game one vs. Seattle:
Gridin-Frost-Coronato
Honzek-Backlund-Farabee
Zary-Strome-Sharangovich
Tsyplakov-Pospisil-Klapka
Bahl-Nemec
Kuznetsov-Whitecloud
Middleton-Parekh
Wolf · @steinbergyyc ↗2026-10-01
ReportedBrennan Othmann — Brennan Othmann, Joel Hanley, and Brayden Pachal on the ice late after an optional morning skate. They’ll be healthy scratches vs. Seattle tonight.
Dustin Wolf in net.
Home opener goes at 7 pm. Pregame on @QRcalgary at 5:30 pm. · @SteinbergYYC ↗2026-10-01
TransactionBen Jones moved to CGY (from MIN) · NHL transactions2026-09-30
Each item names its source. Kodo's own projected line changes are not reported here.
Carried into camp
Jonathan Huberdeau — Flames’ Huberdeau suffers setback in recovery from hip resurfacing surgery https://t.co/zAJUtqzD4W · @DailyFaceoff · 32d
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 for2.5432nd2.2832nd-0.26
Goals against3.1222nd3.0015th-0.12▲7
Power play16.231st19.3431st=+3.14~
Penalty kill80.412th77.8922nd-2.51▼10
Faceoffs49.321st49.5419th+0.24▲2
Points percentage0.46929th0.44631st-0.023▼2
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 finished stronger than they started — +23 points of win percentage between the first quarter and the last.
Oct–Nov10-08 – 11-15
25%5-15
for2.15
against3.10
Nov–Jan11-18 – 01-03
62%13-8
for3.24
against2.81
Jan–Mar01-05 – 03-05
30%6-14
for1.95
against3.20
Mar–Apr03-07 – 04-16
48%10-11
for2.95
against3.52
Light nights
26.2%23rd
22 of 84 games
Four-game weeks
626th
6 weeks of two or fewer
Back-to-backs
82nd
roughly one backup start each
Playoff-week games
1018th
over 3 weeks
Games per week
2026-09-28on light nights2027-04-05
Games by month
Oct*
14Nov
14Dec
13Jan
14Feb
10Mar
14Apr*
5* 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.
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
* — unsigned restricted free agent. His club holds his rights, so he is projected and dressed here, but he is not yet under contract.
Life after Andersson
Rasmus Andersson played his last game for this club on 2026-01-17 and is now in VGK. Team scoring went 2.58 → 2.47 goals a game over the 34 games after.
Defence — who took the minutes
toiafterΔp/gmafterΔ
Forwards
toiafterΔp/gmafterΔ
Not a controlled experiment — the same window also saw Kadri leave 2026-03-05, Coleman leave 2026-04-16, Strome arrive 2026-03-07, Maatta leave 2026-04-16, Weegar leave 2026-03-03, Kirkland leave 2026-01-29, Olofsson leave 2026-04-16, Lomberg leave 2026-03-24. Read the deltas as role changes, not pure cause and effect.
In Strome, Nemec, Middleton, Tsyplakov, Whitecloud, Othmann, Nylander, Basha, Benning, Englund, Sergeev, Castagna
Callup Othmann, Honzek
Out Kadri→COL, Andersson→VGK, Coleman→MIN, Olofsson, Weegar→UTA, Maatta→MIN, Lomberg, Beecher
15.5→18 +2.5
15.2→17.5 +2.3
16.7→18.9 +2.2
16→17.5 +1.5
12.3→13.7 +1.4
10.2→11.6 +1.4
20.3→21.6 +1.3
17.5→16.1 -1.4
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 →
Second power-play unit — forward slotUNDERDEPLOYED6.5 pts at stake
holds it
0 proj pts · 0.3′ PP
vs
pushing
36 proj pts · 18.9′ · 1.2′ PP
Brennan Othmannmodel favours the challengerMikael Backlund
1.75 more min/game on PP2 (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 slot6.5 pts at stake
holds it
33 proj pts · 17.5′ · 2′ PP
vs
pushing
32 proj pts · 17.5′ · 1.6′ PP
Matvei Gridintoo close to callYegor Sharangovich
Conversion
16.2%
on the man advantage
PP goals
45
538 shots
Expected goals
52.2
-7.2 vs actual
Shooting
8.4%
of PP shots go in
What left the power play
Olofsson carried 6% of the power-play points on 1% of its minutes — a focal score of 4. He is not on this roster.
Kadri carried 15% of the power-play points on 10% of its minutes — a focal score of 1.51. He is not on this roster.
Andersson carried 10% of the power-play points on 7% of its minutes — a focal score of 1.51. He is not on this roster.
Who it runs through · last season
PlayerPP/gmSharePPGPPAPPP/60ixGIPPFocal
1.99′42%1675.7163%1.47
2.68′57%88164.376.158%1.11
2.75′59%78154.097.766%1.04
2.48′53%3363.93155%1
1.63′35%4373.3460%0.85
1.73′37%2573.282.860%0.84
2.92′62%3472.883.366%0.74
1.16′25%1231.91.6—0.49
1.17′25%1231.871.555%0.48
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.
Hits
156018th
projected, this roster · of 32
Blocks
111414th
projected, this roster · of 32
Shots
197831st
projected, this roster · of 32
Penalty minutes
68710th
projected, this roster · of 32
Faceoff wins
186224th
projected, this roster · of 32
H+B
267419th
projected, this roster · of 32
S+H+B
465227th
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.
$85.3Mcommitted · 23 of 24 on file
8reach the market after this season
Pending free agents · this summer
| Ryan StromeC | UFA | $5.00M | 31 pts |
|---|---|---|---|
| Morgan FrostC | UFA | $4.38M | 40 pts |
| Maxim TsyplakovR | UFA | $2.25M | 11 pts |
| Joel HanleyD | UFA | $1.75M | 2 pts |
| Adam KlapkaR | RFA | $1.25M | 16 pts |
| Brayden PachalD | UFA | $1.19M | 2 pts |
| Yan KuznetsovD | RFA | $0.81M | 10 pts |
| Brennan OthmannL | RFA | — | 0 pts |
Free the summer after
| Joel FarabeeL | $5.00M | 35 pts |
|---|---|---|
| Connor ZaryC | $3.77M | 29 pts |
| Mikael BacklundC | $3.25M | 36 pts |
| Zach WhitecloudD | $2.75M | 11 pts |
| Devin CooleyG | $1.35M | |
| Zayne ParekhD | $0.95M | 17 pts |
| Matvei GridinR | $0.95M | 33 pts |
| Samuel HonzekL | $0.91M | 16 pts |
Biggest cap hits
| Jonathan HuberdeauL | $10.50M | 4y left · NMC |
|---|---|---|
| Dustin WolfG | $7.50M | 6y left |
| Simon NemecD | $7.25M | 4y left |
| Matt CoronatoR | $6.50M | 5y left |
| Yegor SharangovichC | $5.75M | 3y left · M-NTC |
| Kevin BahlD | $5.35M | 4y left |
| Ryan StromeC | $5.00M | final yr |
| Joel FarabeeL | $5.00M | 1y left |
Cap hits from CapWages for the 23 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
Wolf
55 NHL starts last season
GSAx / start
-0.018lg -0.040552nd pctile
Shot quality faced
0.1008lg 0.10418th hardest
10-start rolling GSAx · appearance 1-82 · shared scale
2026-27 projection
43 GS17 W (10–23)0.895 SV%3.09 GAA
2025-26 actual · NHL
55 GS23 W0.899 SV%3.01 GAA
Cooley
26 NHL starts last season
GSAx / start
0.56lg -0.040597th pctile
Shot quality faced
0.1081lg 0.10470th hardest
10-start rolling GSAx · appearance 1-80 · shared scale
2026-27 projection
40 GS15 W (9–20)0.898 SV%3.38 GAA
2025-26 actual · NHL
26 GS10 W0.909 SV%2.69 GAA
Sergeevgone
1 NHL start last season
GSAx / start
—Shot quality faced
0.1161lg 0.10497th hardest
10-start rolling GSAx · appearance 1-1 · shared scale
2026-27 projection
— GS— W— SV%— GAA
2025-26 actual · NHL
1 GS1 W0.964 SV%1.00 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 · 14
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Matt Coronato | L2·PP1 | 24 | +0.36 | 245.5 | 77 | 22 | 27 | 49.2 | 16 | 0 | 189 | 27 | 27 | 30 | -12 | 66 | 54 | 243 | ascendingice time ↑PP1 | |
| Morgan Frost | L2·PP1 | 27 | +0.20 | 277.3 | 80 | 18 | 22 | 40.2 | 16 | 0 | 135 | 62 | 45 | 26 | -11 | 558 | 107 | 242 | ice time ↑PP1 | |
| Joel Farabee | L3·PP1 | 26 | +0.15 | 292.9 | 82 | 17 | 18 | 35.2 | 14 | 3 | 144 | 63 | 51 | 31 | -9 | 25 | 113 | 258 | PP1 | |
| Adam Klapka | L4 | 26 | +0.02 | 292.2 | 73 | 7 | 9 | 15.8 | 1 | 0 | 78 | 264 | 46 | 89 | -5 | 11 | 311 | 388 | ||
| Mikael Backlund | L3 | 37 | -0.41 | 289.7 | 74 | 15 | 21 | 35.8 | 3 | 3 | 158 | 31 | 31 | 19 | +5 | 661 | 61 | 219 | ||
| Matvei Gridin | L2·PP1 | 20 | -0.45 | 269.6 | 55 | 11 | 22 | 32.5/48 | 13 | 0 | 115 | 22 | 37 | 12 | -5 | 5 | 58 | 173 | — | ice time ↑PP1 |
| Yegor Sharangovich | L1·PP2 | 28 | -0.47 | 293.1 | 79 | 17 | 15 | 31.9 | 8 | 2 | 138 | 20 | 34 | 13 | -14 | 72 | 54 | 192 | decliningice time ↑ | |
| Connor Zary | L1 | 25 | -0.60 | 292.7 | 72 | 14 | 15 | 29.2 | 8 | 0 | 118 | 42 | 23 | 23 | -4 | 82 | 66 | 183 | declining | |
| Jonathan Huberdeau | 33 | -0.64 | 291.4 | 55 | 12 | 19 | 30.8/46 | 10 | 1 | 89 | 34 | 34 | 35 | -9 | 9 | 68 | 157 | declining | ||
| Ryan Strome | L1·PP2 | 33 | -0.83 | 292.4 | 72 | 10 | 21 | 31 | 5 | 0 | 91 | 37 | 24 | 55 | -9 | 317 | 61 | 152 | declining | |
| Samuel Honzek | L3 | 22 | -0.87 | 292.5 | 69 | 6 | 10 | 16.1 | 2 | 0 | 83 | 109 | 28 | 17 | 0 | 9 | 137 | 220 | — | |
| Martin Pospisil | L4 | 27 | -0.88 | 293 | 49 | 3 | 6 | 9/15 | 1 | 0 | 61 | 181 | 16 | 47 | 0 | 37 | 198 | 259 | declining | |
| Maxim Tsyplakov | L4·PP2 | 28 | -1.03 | 292.9 | 65 | 5 | 7 | 11.4 | 3 | 0 | 70 | 112 | 21 | 19 | -7 | 11 | 133 | 203 | declining | |
| Brennan Othmann* | ·PP2 | 23 | -2.37 | 292.5 | 4 | 0 | 0 | 0.2 | 0 | 0 | 3 | 8 | 2 | 2 | 0 | 0 | 9 | 12 |
Shading is that man's percentile among all projected forwards in the league, not among these 14. 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 · 8
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Jake Middleton | LD2 | 30 | -0.32 | 292.9 | 77 | 3 | 11 | 14 | 0 | 0 | 72 | 99 | 134 | 68 | +3 | 0 | 233 | 304 | ||
| Zach Whitecloud | RD3 | 30 | -0.34 | 293.3 | 77 | 2 | 9 | 10.8 | 0 | 0 | 65 | 123 | 127 | 33 | -2 | 0 | 250 | 315 | ||
| Simon Nemec | RD1·PP2 | 22 | -0.37 | 172.4 | 68 | 7 | 14 | 21.8 | 9 | 0 | 103 | 22 | 100 | 29 | -7 | 0 | 122 | 225 | ascending | |
| Kevin Bahl | LD1 | 26 | -0.48 | 293 | 77 | 2 | 11 | 13.3 | 1 | 1 | 64 | 117 | 104 | 48 | 0 | 0 | 222 | 285 | ||
| Yan Kuznetsov | LD3 | 24 | -0.50 | 292.6 | 70 | 3 | 7 | 9.8 | 0 | 2 | 78 | 96 | 117 | 36 | -3 | 0 | 212 | 290 | — | |
| Zayne Parekh | RD2·PP1 | 20 | -0.64 | 197 | 61 | 7 | 10 | 16.8 | 12 | 0 | 98 | 29 | 63 | 22 | -3 | 0 | 92 | 189 | — | PP1 |
| Brayden Pachal | 27 | -1.92 | — | 21 | 0 | 1 | 1.7 | 0 | 0 | 16 | 46 | 23 | 23 | 0 | 0 | 69 | 85 | |||
| Joel Hanley | 35 | -2.04 | 291.6 | 22 | 0 | 2 | 2 | 0 | 0 | 13 | 16 | 30 | 13 | 0 | 0 | 45 | 59 |
Shading is that man's percentile among all projected defencemen in the league, not among these 8. 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 · 3
| Goalie | GS | W | L | OTL | SV% | GAA | SV | SA | GA | SHO | GSAx | GSAx/GS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dustin Wolf | 43 | 17 | 22 | 4 | 0.895 | 3.09 | 1102 | 1232 | 130 | 1.8 | -1 | -0.018 |
| Devin Cooley | 40 | 15 | 18 | 4 | 0.898 | 3.38 | 1163 | 1295 | 132 | 0.0 | +14.6 | 0.56 |
| Arsenii Sergeev | — | — | — | — | — | — | — | — | — | — | +2.2 | — |
Projected record and projected ice time are model estimates; ranks, lines, projections and trend signals trace to real data.