Kodo Hockey← Columbus Blue Jackets
2026-27 season preview
Columbus Blue Jackets
42-32-1094 pts15th of 32
Goals for
3.06
18th in the league
Goals against
3.01
16th in the league
Power play
18.9%
22nd in the league
Kodo projects the Columbus Blue Jackets for 42-32-10 (94 pts). The fantasy engine runs through Zach Werenski and Adam Fantilli on PP1. 2 core skaters project to rise and 4 to slip. Jet Greaves is the projected starter.
Your categories · using the preset above
The crease
Jet Greaves
Jet Greaves projects the crease (~47 starts), but Cam Talbot (~36) makes it more timeshare than lock
Contents · 12 sections
InjuryMikael Pyyhtia — Out — Lower Body · CBS2026-10-03
Injury noteIvan Provorov — Day-To-Day→Out · CBS2026-10-02
ReportedMatthew Knies — Changing the momentum 😤
Watch last night's 6-3 Opening Night win in all its glory! Hear Rick Bowness' message to the team and relive Matthew Knies’ record CBJ debut in a Victory Edition of Behind The Battle ⬇️
CBJ x @PNCBank https://t.co/t0Fwfui58x · @BlueJacketsNHL ↗2026-10-02
InjuryIvan Provorov — Out — Lower Body · CBS2026-10-01
InjuryIsac Lundestrom — Out — Achilles · CBS2026-10-01
Each item names its source. Kodo's own projected line changes are not reported here.
25-2626-27Change
Goals for318th3.0618th+0.06
Goals against3.0617th3.0117th-0.05
Power play18.922nd20.3122nd=+1.41~
Penalty kill7628th77.2928th+1.29
Faceoffs50.613th49.8616th-0.74▼3
Points percentage0.56116th0.56015th-0.001▲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 — -12 points of win percentage between the first quarter and the last.
Oct–Nov10-09 – 11-18
50%10-10
for3.10
against3.25
Nov–Jan11-20 – 01-04
38%8-13
for3.10
against3.43
Jan–Mar01-06 – 03-05
70%14-6
for3.45
against2.80
Mar–Apr03-07 – 04-14
38%8-13
for2.71
against2.86
Light nights
20.2%29th
17 of 84 games
Four-game weeks
715th
4 weeks of two or fewer
Back-to-backs
1323rd
roughly one backup start each
Playoff-week games
927th
over 3 weeks
Games per week
2026-09-28on light nights2027-04-05
Games by month
Oct*
12Nov
13Dec
15Jan
15Feb
11Mar
13Apr*
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.
Mikael Pyyhtia — Lower Body: IR. Expected to be out until at least Oct 18 · still projected 21 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
* — unsigned restricted free agent. His club holds his rights, so he is projected and dressed here, but he is not yet under contract.
0.57 points a game and 16.1 minutes walked out of the lineup — about 9 points over a season.
Stepped up without him
playerwithw/outswing
Faded 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 Knies, Nichushkin, Garland, Heinen, Andrae, Soucy, Lomberg, Talbot, Keskinen, White, Copley, Sillinger
Callup Belluz
Out Marchenko→TOR, Marchment, Chinakhov→PIT, Jenner, Wood→TOR, Gaunce→BOS, Aston-Reese→PHI, Zamula
17.8→20.7 +2.9
19.2→21.6 +2.4
10→12 +2
18.8→20.2 +1.4
18.9→20.2 +1.3
17.7→18.9 +1.2
26.6→27.7 +1.1
15.3→13.5 -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 — forward slotUNDERDEPLOYED4.1 pts at stake
holds it
44 proj pts · 17.3′ · 2′ PP
vs
pushing
51 proj pts · 18.9′ · 2.1′ PP
Sean Monahanmodel favours the challengerValeri Nichushkin
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.
Conversion
18.9%
on the man advantage
PP goals
47
555 shots
Expected goals
60.2
-13.2 vs actual
Shooting
8.5%
of PP shots go in
What left the power play
Chinakhov carried 4% of the power-play points on 1% of its minutes — a focal score of 6.67. He is not on this roster.
Who it runs through · last season
PlayerPP/gmSharePPGPPAPPP/60ixGIPPFocal
3′70%417215.63.665%1.09
2.08′49%76134.575.459%0.89
2.09′49%7294.15.557%0.8
2.05′48%19103.763.557%0.74
0.63′15%1233.80.5—0.74
2.6′61%49133.666.752%0.71
1.37′32%1452.671.357%0.52
1.36′32%1342.491.7—0.49
1.72′40%1120.92339%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.
Projected PP1Werenski23 PPP (21 last yr)Fantilli13 PPP (13 last yr)Coyle10 PPP (13 last yr)Monahan12 PPP (10 last yr)Knies21 PPP (16 last yr)
Projected PP2Voronkov10 PPP (9 last yr)Johnson6 PPP (2 last yr)Mateychuk3 PPP (3 last yr)Garland11 PPP (10 last yr)Nichushkin7 PPP (4 last yr)
Hits
164013th
projected, this roster · of 32
Blocks
113720th
projected, this roster · of 32
Shots
225520th
projected, this roster · of 32
Penalty minutes
67414th
projected, this roster · of 32
Faceoff wins
202420th
projected, this roster · of 32
H+B
277714th
projected, this roster · of 32
S+H+B
503117th
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.
$91.0Mcommitted · 25 of 26 on file
10reach the market after this season
Pending free agents · this summer
| Dmitri VoronkovL | RFA | $4.17M | 39 pts |
|---|---|---|---|
| Kent JohnsonR | RFA | $1.80M | 34 pts |
| Erik GudbransonD | UFA | $1.75M | 5 pts |
| Isac LundeströmC | UFA | $1.30M | 1 pts |
| Danton HeinenL | UFA | $1.00M | 17 pts |
| Jake ChristiansenD | UFA | $0.97M | 4 pts |
| Cam TalbotG | UFA | $0.95M | |
| Denton MateychukD | RFA | $0.89M | 25 pts |
| Luca Del Bel BelluzC | RFA | $0.86M | 2 pts |
| Adam FantilliC | RFA | — | 65 pts |
Free the summer after
| Zach WerenskiD | $9.58M | 84 pts |
|---|---|---|
| Emil AndraeD | $1.55M | 10 pts |
| Ryan LombergL | $1.30M | 6 pts |
| Mikael PyyhtiaL | $0.88M | 2 pts |
Biggest cap hits
| Zach WerenskiD | $9.58M | 1y left · NMC |
|---|---|---|
| Ivan ProvorovD | $8.50M | 5y left · NMC |
| Matthew KniesL | $7.75M | 4y left |
| Damon SeversonD | $6.25M | 4y left · NTC |
| Valeri NichushkinR | $6.13M | 3y left · M-NTC |
| Charlie CoyleC | $6.00M | 5y left · NMC |
| Conor GarlandR | $6.00M | 5y left · NMC |
| Sean MonahanC | $5.50M | 2y left · NMC |
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
Greaves
53 NHL starts last season
GSAx / start
0.31lg -0.040581st pctile
Shot quality faced
0.1029lg 0.10437th hardest
10-start rolling GSAx · appearance 1-82 · shared scale
2026-27 projection
47 GS24 W (14–31)0.900 SV%3.02 GAA
2025-26 actual · NHL
53 GS26 W0.908 SV%2.60 GAA
Talbot
25 NHL starts last season, with DET
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
36 GS18 W (11–24)0.889 SV%3.51 GAA
2025-26 actual · NHL
25 GS12 W0.883 SV%3.19 GAA
Merzlikinsgone
29 NHL starts last season
GSAx / start
-0.258lg -0.040521st pctile
Shot quality faced
0.1078lg 0.10469th hardest
10-start rolling GSAx · appearance 1-82 · shared scale
2026-27 projection
— GS— W— SV%— GAA
2025-26 actual · NHL
29 GS14 W0.883 SV%3.40 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 · 15
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Adam Fantilli* | L1·PP1 | 22 | +1.50 | 118.5 | 83 | 29 | 36 | 65 | 13 | 1 | 218 | 126 | 49 | 32 | -6 | 515 | 175 | 393 | PP1 | |
| Matthew Knies | L1·PP1 | 24 | +1.43 | 129.9 | 81 | 26 | 43 | 69 | 21 | 1 | 150 | 164 | 35 | 35 | -12 | 1 | 199 | 349 | ascendingPP1 | |
| Charlie Coyle | L3·PP1 | 34 | +0.40 | 230.2 | 80 | 18 | 31 | 48.7 | 10 | 1 | 121 | 104 | 53 | 18 | +1 | 556 | 157 | 278 | PP1 | |
| Valeri Nichushkin | L2·PP2 | 31 | +0.38 | 189.7 | 68 | 22 | 29 | 51.1/61 | 7 | 1 | 152 | 60 | 28 | 22 | +8 | 2 | 88 | 240 | bounce-back | |
| Dmitri Voronkov | L4·PP2 | 26 | +0.34 | 266.1 | 74 | 19 | 20 | 38.9 | 10 | 0 | 134 | 105 | 38 | 61 | +2 | 41 | 143 | 277 | ||
| Mathieu Olivier | L3 | 29 | +0.31 | 284.8 | 71 | 13 | 11 | 23.7 | 0 | 1 | 96 | 242 | 54 | 109 | +5 | 6 | 295 | 391 | ||
| Conor Garland | L2·PP2 | 30 | +0.25 | 293.4 | 80 | 18 | 25 | 43 | 11 | 1 | 152 | 50 | 40 | 40 | -12 | 11 | 90 | 241 | declining | |
| Cole Sillinger | L3 | 23 | +0.23 | 293 | 78 | 15 | 26 | 40.9 | 2 | 1 | 145 | 106 | 45 | 38 | -2 | 155 | 151 | 296 | bounce-back | |
| Sean Monahan | L2·PP1 | 32 | 0.00 | 292.1 | 72 | 17 | 27 | 43.9 | 12 | 2 | 125 | 40 | 34 | 15 | +3 | 631 | 74 | 199 | decliningPP1 | |
| Kent Johnson | L1·PP2 | 24 | -0.43 | 289.5 | 74 | 13 | 21 | 34.2 | 6 | 1 | 120 | 13 | 35 | 20 | -4 | 2 | 49 | 168 | decliningbounce-back | |
| Danton Heinen | L4 | 31 | -1.03 | — | 64 | 7 | 10 | 17 | 1 | 0 | 64 | 71 | 32 | 23 | +6 | 13 | 103 | 167 | declining | |
| Ryan Lomberg | L4 | 32 | -1.13 | 292.6 | 63 | 3 | 3 | 5.8 | 0 | 0 | 56 | 125 | 18 | 57 | -3 | 5 | 144 | 199 | ||
| Mikael Pyyhtia | 25 | -2.02 | 292.7 | 21 | 1 | 1 | 1.6 | 0 | 0 | 20 | 11 | 15 | 4 | -2 | 1 | 26 | 45 | |||
| Luca Del Bel Belluz | 23 | -2.06 | 292.6 | 13 | 1 | 1 | 2.2/14 | 0 | 0 | 16 | 5 | 8 | 4 | 0 | 33 | 13 | 29 | — | ||
| Isac Lundeström | 27 | -2.10 | 291.7 | 16 | 0 | 1 | 1.1 | 0 | 0 | 12 | 9 | 10 | 2 | -1 | 51 | 19 | 31 |
Shading is that man's percentile among all projected forwards in the league, not among these 15. 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 · 9
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Zach Werenski | LD1·PP1 | 29 | +2.07 | 13.6 | 80 | 20 | 64 | 83.8 | 23 | 2 | 273 | 28 | 112 | 23 | +7 | 0 | 140 | 413 | PP1 | |
| Denton Mateychuk | RD1·PP2 | 22 | -0.64 | 235.5 | 72 | 8 | 18 | 25.3 | 3 | 0 | 89 | 30 | 92 | 17 | +5 | 0 | 122 | 211 | ascendingice time ↑ | |
| Carson Soucy | LD3 | 32 | -0.84 | — | 78 | 3 | 6 | 8.6 | 0 | 0 | 65 | 111 | 98 | 41 | -4 | 0 | 209 | 274 | ||
| Emil Andrae | RD3 | 24 | -1.13 | 292.1 | 67 | 2 | 8 | 10.4 | 1 | 0 | 51 | 75 | 72 | 27 | +5 | 2 | 146 | 197 | ice time ↓ | |
| Erik Gudbranson | RD2 | 34 | -1.15 | 292.6 | 59 | 1 | 4 | 4.8 | 0 | 0 | 53 | 74 | 101 | 34 | +3 | 0 | 175 | 228 | ice time ↑ | |
| Ivan Provorov | 29 | -1.52 | 221.1 | 29 | 3 | 8 | 10.1 | 2 | 0 | 46 | 12 | 47 | 11 | +3 | 0 | 59 | 104 | |||
| Damon Severson | 32 | -1.58 | 293.1 | 27 | 3 | 8 | 10.7/32 | 1 | 0 | 33 | 15 | 34 | 17 | +4 | 0 | 50 | 83 | |||
| Jake Christiansen | LD2 | 27 | -1.65 | — | 53 | 1 | 3 | 3.6 | 0 | 0 | 39 | 40 | 44 | 12 | -2 | 0 | 84 | 123 | ice time ↑ | |
| Dante Fabbro | 28 | -1.77 | 292.7 | 26 | 1 | 2 | 3/9 | 0 | 0 | 26 | 24 | 45 | 13 | -1 | 0 | 69 | 96 |
Shading is that man's percentile among all projected defencemen in the league, not among these 9. 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Jet Greaves | 47 | 24 | 18 | 6 | 0.900 | 3.02 | 1244 | 1382 | 138 | 2.2 | +16.4 | 0.31 |
| Cam Talbot | 36 | 18 | 14 | 4 | 0.889 | 3.51 | 983 | 1106 | 123 | 0.9 | -8.5 | -0.342 |
| Elvis Merzlikins | — | — | — | — | — | — | — | — | — | — | -7.5 | -0.258 |
Projected record and projected ice time are model estimates; ranks, lines, projections and trend signals trace to real data.