Kodo Hockey← Detroit Red Wings
2026-27 season preview
Detroit Red Wings
38-37-985 pts29th of 32
Goals for
2.79
22nd in the league
Goals against
3.08
19th in the league
Power play
22.6%
12th in the league
Kodo projects the Detroit Red Wings for 38-37-9 (85 pts). The fantasy engine runs through Alex DeBrincat and Moritz Seider on PP1. 2 core skaters project to rise and 2 to slip. John Gibson 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
John Gibson
John Gibson projects the crease (~43 starts), but Daniil Tarasov (~40) makes it more timeshare than lock
Contents · 12 sections
InjuryMarco Kasper — Out — Upper Body · CBS2026-10-03
ReportedMarco Kasper — Should read Kasper out tomorrow. Injury not from the fight. · @AnsarKhanMLive ↗2026-10-03
ReportedDylan Larkin — #RedWings McLellan said Kasper put tomorrow with an upper body injury, or from his fight. Will be re-evaluated in a couple of days. Finnie moves to center, Mazur in lineup tomorrow. Said Larkin more active in practice and hope is still he’ll be ready for Tuesday. · @AnsarKhanMLive ↗2026-10-03
ReportedMarco Kasper — Per McLellan, Kasper out Sunday against Jets with upper body injury, being evaluated. Not fight related. · @HeleneStJames ↗2026-10-03
ReportedMarco Kasper — McLellan: Marco Kasper is out for tomorrow’s game against Winnipeg, upper body injury · @m_bultman ↗2026-10-03
Each item names its source. Kodo's own projected line changes are not reported here.
25-2626-27Change
Goals for2.9122nd2.7929th-0.12▼7
Goals against3.119th3.0822nd-0.02▼3
Power play22.612th21.6412th=-0.96~
Penalty kill77.123rd77.2030th+0.10▼7
Faceoffs5110th51.287th+0.28▲3
Points percentage0.56115th0.50629th-0.055▼14
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 faded from where they started — -31 points of win percentage between the first quarter and the last.
Oct–Nov10-09 – 11-18
60%12-8
for3.10
against3.10
Nov–Jan11-20 – 12-31
57%12-9
for3.10
against3.24
Jan–Mar01-01 – 03-02
55%11-9
for2.75
against2.65
Mar–Apr03-04 – 04-15
29%6-15
for2.81
against3.57
Light nights
28.6%16th
24 of 84 games
Four-game weeks
627th
3 weeks of two or fewer
Back-to-backs
1218th
roughly one backup start each
Playoff-week games
1020th
over 3 weeks · 2 back-to-back
Games per week
2026-09-28on light nights2027-04-05
Games by month
Oct*
14Nov
12Dec
15Jan
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.
Dylan Larkin — Upper Body: out for the first two games, Oct 2 vs NYR and Oct 4 vs WPG; expected to be out until at least Oct 5 · still projected 79 games
Jacob Bernard-Docker — Upper Body: IR. Expected to be out until at least Oct 9 · still projected 69 games
Andreas Englund — Not Injury Related: IR. Expected to be out until at least Oct 2 · still projected 18 games
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
Larkin 74 ptsKasper 29 ptsBernard-Docker 8 ptsEdvinsson* 7 ptsBear 9 ptsEnglund 1 ptsStillman 1 ptsWallinder 0 pts
* — unsigned restricted free agent. His club holds his rights, so he is projected and dressed here, but he is not yet under contract.
0.85 points a game and 17.7 minutes walked out of the lineup — about 13 points over a season.
Stepped up without him
playerwithw/outswing
Points per game with him in the lineup against the games he missed. Not a controlled experiment — absences cluster around injuries, so some of these games were missing other players too. Every absence for this club →
In Arvidsson, Faulk, Kolesar, Englund, Johansson, Muzzatti, Lagesson, Skoog, Becher, Butler, Nilsson, James
Callup Stillman, Mazur, Johansson, Brandsegg-Nygård, Bear, Wallinder
Out Kane, Riemsdyk, Perron, Berggren→STL, Soderblom→PIT, Leonard→UFA, Shine→UFA, Hamonic
12.5→14.5 +2
16.6→18.1 +1.5
15.7→17.1 +1.4
14.6→15.7 +1.1
18.5→19.5 +1
12.7→13.7 +1
15.5→16.5 +1
11.5→10.4 -1.1
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 slotUNDERDEPLOYED7.1 pts at stake
holds it
42 proj pts · 18.1′ · 1.5′ PP
vs
pushing
40 proj pts · 16.5′ · 1.5′ PP
Andrew Coppmodel favours the challengerEmmitt Finnie
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.
First lineUNDERDEPLOYED5.4 pts at stake
holds it
42 proj pts · 18.1′ · 1.5′ PP
vs
pushing
47 proj pts · 15.7′ · 2.1′ PP
Andrew Coppmodel favours the challengerViktor Arvidsson
Conversion
22.6%
on the man advantage
PP goals
66
657 shots
Expected goals
72.6
-6.6 vs actual
Shooting
10%
of PP shots go in
What left the power play
Perron carried 4% of the power-play points on 1% of its minutes — a focal score of 3.73. He is not on this roster.
Berggren carried 2% of the power-play points on 1% of its minutes — a focal score of 2.29. He is not on this roster.
Leonard carried 1% of the power-play points on 1% of its minutes — a focal score of 1.67. He is not on this roster.
Who it runs through · last season
PlayerPP/gmSharePPGPPAPPP/60ixGIPPFocal
3.22′61%720276.295.875%1.18
3.28′62%325286.262.873%1.17
3.21′61%1410246.0713.669%1.13
3.27′62%158235.1414.764%0.96
1.58′30%0334.061.255%0.8
1.48′28%1784.13.164%0.76
1.48′28%4483.952.967%0.73
1.89′36%1673.271.270%0.61
0.88′17%1121.681.5—0.3
0.8′15%0110.911.2—0.17
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
17909th
projected, this roster · of 32
Blocks
12936th
projected, this roster · of 32
Shots
235717th
projected, this roster · of 32
Penalty minutes
64122nd
projected, this roster · of 32
Faceoff wins
237615th
projected, this roster · of 32
H+B
30836th
projected, this roster · of 32
S+H+B
54397th
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.
$89.0Mcommitted · 27 of 28 on file
11reach the market after this season
Pending free agents · this summer
| Alex DeBrincatR | UFA | $7.88M | 82 pts |
|---|---|---|---|
| Justin FaulkD | UFA | $6.50M | 32 pts |
| John GibsonG | UFA | $6.40M | |
| Andrew CoppC | UFA | $5.63M | 42 pts |
| Mason AppletonR | UFA | $2.90M | 14 pts |
| Daniil TarasovG | UFA | $2.00M | |
| Albert JohanssonD | RFA | $1.13M | 9 pts |
| Andreas EnglundD | UFA | $0.90M | 1 pts |
| Marco KasperC | RFA | $0.89M | 29 pts |
| Chase StillmanR | RFA | $0.85M | 1 pts |
| Simon EdvinssonD | RFA | — | 7 pts |
Free the summer after
| J.T. CompherC | $5.10M | 31 pts |
|---|---|---|
| Viktor ArvidssonL | $5.00M | 47 pts |
| Michael RasmussenC | $3.20M | 18 pts |
| Keegan KolesarR | $2.50M | 17 pts |
| Jacob Bernard-DockerD | $1.60M | 8 pts |
| Michael Brandsegg-NygårdR | $0.95M | 25 pts |
| Axel Sandin-PellikkaD | $0.94M | 23 pts |
| Emmitt FinnieL | $0.92M | 40 pts |
| Anton JohanssonD | $0.92M | 0 pts |
| Nate DanielsonC | $0.91M | 5 pts |
| William WallinderD | $0.88M | |
| Carter MazurL | $0.88M | 0 pts |
Biggest cap hits
| Dylan LarkinC | $8.70M | 4y left · NTC |
|---|---|---|
| Moritz SeiderD | $8.55M | 4y left |
| Lucas RaymondL | $8.07M | 5y left |
| Alex DeBrincatR | $7.88M | final yr · M-NTC |
| Justin FaulkD | $6.50M | final yr · M-NTC |
| John GibsonG | $6.40M | final yr · M-NTC |
| Andrew CoppC | $5.63M | final yr · M-NTC |
| J.T. CompherC | $5.10M | 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
Gibson
57 NHL starts last season
GSAx / start
0.207lg -0.040570th pctile
Shot quality faced
0.1066lg 0.10464th hardest
10-start rolling GSAx · appearance 1-81 · shared scale
2026-27 projection
43 GS23 W (13–29)0.894 SV%2.94 GAA
2025-26 actual · NHL
57 GS29 W0.901 SV%2.72 GAA
Tarasov
31 NHL starts last season, with FLA
GSAx / start
-0.129lg -0.040537th pctile
Shot quality faced
0.1006lg 0.10416th hardest
10-start rolling GSAx · appearance 1-82 · shared scale
2026-27 projection
40 GS19 W (11–25)0.890 SV%3.38 GAA
2025-26 actual · NHL
31 GS13 W0.895 SV%3.05 GAA
Talbotgone
25 NHL starts last season
GSAx / start
-0.342lg -0.040515th pctile
Shot quality faced
0.106lg 0.10463rd hardest
10-start rolling GSAx · appearance 1-81 · shared scale
2026-27 projection
— GS— W— SV%— GAA
2025-26 actual · NHL
25 GS12 W0.883 SV%3.19 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 · 16
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alex DeBrincat | L1·PP1 | 29 | +2.04 | 43.2 | 83 | 39 | 43 | 81.5 | 26 | 0 | 270 | 43 | 39 | 24 | +1 | 20 | 82 | 351 | PP1 | |
| Dylan Larkin | 30 | +1.74 | 83 | 79 | 36 | 38 | 74.1 | 27 | 1 | 231 | 47 | 34 | 46 | -2 | 838 | 81 | 312 | |||
| Lucas Raymond | L1·PP1 | 24 | +1.67 | 100.1 | 83 | 27 | 58 | 84.5 | 32 | 0 | 191 | 47 | 31 | 24 | -4 | 2 | 79 | 270 | ascendingPP1 | |
| Viktor Arvidsson | L2·PP1 | 33 | +0.30 | 240.3 | 72 | 22 | 25 | 47.4/53 | 9 | 0 | 168 | 27 | 35 | 25 | +11 | 11 | 62 | 230 | sell-highPP1 | |
| Emmitt Finnie | L2·PP2 | 21 | +0.30 | 291.3 | 80 | 20 | 21 | 40.2 | 9 | 0 | 137 | 128 | 40 | 13 | -4 | 47 | 169 | 305 | — | bounce-back |
| Marco Kasper | 22 | +0.20 | 290.2 | 79 | 15 | 13 | 28.8 | 3 | 1 | 144 | 175 | 49 | 34 | -8 | 243 | 223 | 368 | decliningbounce-back | ||
| Andrew Copp | L1·PP1 | 32 | -0.19 | 292.4 | 76 | 14 | 28 | 41.9 | 6 | 1 | 100 | 35 | 56 | 21 | +3 | 504 | 91 | 191 | bounce-backice time ↑PP1 | |
| Keegan Kolesar | L3 | 29 | -0.26 | 292.2 | 79 | 7 | 10 | 16.8 | 0 | 1 | 67 | 252 | 42 | 52 | -6 | 10 | 294 | 361 | bounce-back | |
| Michael Brandsegg-Nygård | L3·PP2 | 21 | -0.32 | 292.5 | 74 | 11 | 13 | 24.6 | 2 | 0 | 101 | 160 | 23 | 26 | 0 | 10 | 184 | 285 | — | ice time ↑ |
| J.T. Compher | L2·PP2 | 31 | -0.53 | 292.4 | 81 | 12 | 19 | 30.6 | 4 | 0 | 94 | 27 | 57 | 27 | -8 | 518 | 84 | 178 | ||
| Michael Rasmussen | L3·PP2 | 27 | -0.70 | 292.4 | 73 | 9 | 9 | 18.4 | 1 | 1 | 81 | 97 | 66 | 19 | -7 | 129 | 162 | 243 | declining | |
| Mason Appleton | L4 | 30 | -0.93 | 292.5 | 71 | 6 | 8 | 14.1 | 0 | 1 | 79 | 90 | 31 | 37 | 0 | 8 | 121 | 199 | ||
| Carter Bear | 20 | -1.59 | 292.6 | 23 | 4 | 5 | 9.1/29 | 1 | 0 | 35 | 44 | 9 | 12 | 0 | 3 | 53 | 89 | — | ||
| Nate Danielson | L4 | 22 | -1.95 | 292.4 | 17 | 2 | 3 | 4.6/22 | 1 | 0 | 24 | 7 | 7 | 5 | -1 | 34 | 14 | 38 | — | |
| Chase Stillman | 23 | -2.18 | — | 3 | 0 | 1 | 0.8/15 | 0 | 0 | 4 | 8 | 1 | 3 | 0 | 1 | 9 | 13 | — | ||
| Carter Mazur | L4 | 24 | -2.22 | 292.5 | 2 | 0 | 0 | 0.4/14 | 0 | 0 | 3 | 4 | 1 | 1 | 0 | 0 | 5 | 7 | — |
Shading is that man's percentile among all projected forwards in the league, not among these 16. 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 · 10
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Moritz Seider | RD1·PP1 | 25 | +1.77 | 40.5 | 83 | 10 | 49 | 58.9 | 28 | 0 | 176 | 160 | 182 | 52 | +6 | 0 | 341 | 517 | ascendingPP1 | |
| Justin Faulk | LD1·PP2 | 34 | +0.11 | 187.9 | 76 | 9 | 24 | 32.2 | 9 | 1 | 129 | 73 | 120 | 37 | -6 | 0 | 193 | 321 | ||
| Ben Chiarot | LD2 | 35 | -0.36 | 286.9 | 77 | 3 | 6 | 8.6 | 0 | 0 | 91 | 146 | 143 | 65 | -9 | 0 | 289 | 380 | ||
| Axel Sandin-Pellikka | RD2 | 21 | -0.74 | 290.4 | 74 | 6 | 17 | 22.9 | 8 | 0 | 87 | 22 | 70 | 25 | -8 | 0 | 92 | 178 | — | |
| Albert Johansson | LD3 | 25 | -1.14 | 292.3 | 77 | 2 | 6 | 8.5 | 0 | 1 | 59 | 61 | 93 | 23 | -9 | 0 | 154 | 213 | bounce-back | |
| Jacob Bernard-Docker | 26 | -1.16 | — | 69 | 2 | 6 | 7.5 | 0 | 0 | 47 | 68 | 99 | 29 | 0 | 0 | 166 | 214 | |||
| Simon Edvinsson* | 23 | -1.58 | 216.1 | 24 | 2 | 5 | 7.4/25 | 0 | 0 | 30 | 30 | 46 | 22 | +2 | 0 | 76 | 105 | |||
| Andreas Englund | 30 | -1.91 | — | 18 | 0 | 1 | 1.3 | 0 | 0 | 10 | 40 | 19 | 19 | -2 | 0 | 59 | 68 | |||
| Anton Johansson | D3 | 22 | -2.25 | — | 1 | 0 | 0 | 0.1/16 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 2 | 2 | — | |
| William Wallinder | 24 | -2.26 | — | 0 | 0 | 0 | 0/7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | — |
Shading is that man's percentile among all projected defencemen in the league, not among these 10. 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 · 5
| Goalie | GS | W | L | OTL | SV% | GAA | SV | SA | GA | SHO | GSAx | GSAx/GS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| John Gibson | 43 | 23 | 16 | 5 | 0.894 | 2.94 | 1040 | 1163 | 123 | 2.3 | +11.8 | 0.207 |
| Daniil Tarasov | 40 | 19 | 17 | 4 | 0.890 | 3.38 | 1068 | 1200 | 132 | 0.4 | -4 | -0.129 |
| Sebastian Cossa | — | — | — | — | — | — | — | — | — | — | 0 | — |
| Michal Postava | — | — | — | — | — | — | — | — | — | — | 0 | — |
| Cam Talbot | — | — | — | — | — | — | — | — | — | — | -8.5 | -0.342 |
Projected record and projected ice time are model estimates; ranks, lines, projections and trend signals trace to real data.