Kodo Hockey← Utah Mammoth
2026-27 season preview
Utah Mammoth
47-27-10104 pts4th of 32
Goals for
3.44
10th in the league
Goals against
2.98
10th in the league
Power play
20.0%
18th in the league
Kodo projects the Utah Mammoth for 47-27-10 (104 pts), carried by the 5th-ranked projected offense. The fantasy engine runs through Clayton Keller and Dylan Guenther on PP1. 1 core skater projects to rise and 0 to slip. Karel Vejmelka 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
Karel Vejmelka
Karel Vejmelka projects the crease (~48 starts), but Sebastian Cossa (~34) makes it more timeshare than lock
Contents · 12 sections
ReportedVincent Trocheck — Did former #NYR Vincent Trocheck think he’d ever be in MSG visiting locker room again?
“I think everywhere you go, you kind of envision that being your last stop. So yeah, I think that was kind of the plan when I signed there. It was just to be there for the rest of my career. Things happen. It's business and teams go in certain directions that you can't really avoid decisions being made.” · @MollieeWalkerr ↗2026-10-04
ReportedVincent Trocheck — The beauty of adding Trocheck & Lee is how they instantly elevated Utah’s top six into a top nine.
Teams won’t be able to relax regardless of which group is on the ice this season.
Arguably the 4 most dominant periods of play in the history of the club to start the season. · @BagleyKSLsports ↗2026-10-03
ReportedSebastian Cossa — #RedWings had 10 first-round picks in Yzerman era and 6 are in the lineup tonight (Seider, Raymond, Kasper, Danielson, Sandin-Pellikka, Brandsegg-Nygard). The others were Edvinsson (unsigned), Cossa/Hurlbert (U-M) essentially swapped, Bear (injured) and bound for @griffinshockey. · @AnsarKhanMLive ↗2026-10-02
ReportedJohn Marino — PP1: Cooley - Schmaltz - Keller
Guenther - Sergachev
PP2: Lee - Trocheck - Carcone
But - Marino
#TusksUp #Blackhawks · @BagleyKSLsports ↗2026-10-01
TransactionJoshua Roy moved to UTA (from MTL) · NHL transactions2026-09-30
Each item names its source. Kodo's own projected line changes are not reported here.
Carried into camp
Tij Iginla — Rookie roster is set.
Simashev, But, Iginla, Desnoyers, Hrabal and Gabe Smith all listed.
No Belchetz as he’s with his college squad.
Positive sign to see Desnoyers on there after the injury he suffered during the summer showcase. · @BagleyKSLsports · 33d
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 for3.2711th3.445th+0.17▲6
Goals against2.9310th2.9811th+0.05▼1
Power play2018th20.7018th=+0.70~
Penalty kill78.119th78.5713th+0.47▲6
Faceoffs49.223rd50.7811th+1.58▲12
Points percentage0.56117th0.6194th+0.058▲13
How these projections are made
Both bars share one scale per row — the wider of the two league ranges — so a bar that moves is a number that moved. The grey tick is that column's league average, and the arrow is the change in league rank. Goals-against and expected-goals-against rank ascending, so low is good and green always means improved.
Goals for, goals against and points percentage come from the roster: summed player projections over the real 26-27 schedule, last season's expected goals against regressed toward the league and adjusted for projected goaltending, and a game-by-game simulation of that schedule.
The special teams are projected from how much each number actually carries over year to year, measured across 298 team-season pairs going back to 2010-11. The power play uses one prior season shrunk 0.36; the penalty kill uses three, weighted .5/.3/.2 and shrunk 0.38. Special teams barely persist, so a league-worst penalty kill is mostly bad luck and comes most of the way back, while the faceoff dot is a repeatable team skill. How far a club moves depends on how far from the mean it started: Vancouver sits near league average on both the power play and the faceoff dot and barely shifts, while Edmonton's 30.6% power play comes back to the middle and this penalty kill, worst in the league, is the biggest riser of all 32.
The penalty kill averages three seasons because one measures it so badly. Split a single season's spread into ability and sampling noise — a kill rate is a binomial over about 250 opportunities, so the noise is known rather than guessed — and only 33% of the gap between clubs is real. Divide the year-over-year correlation by that and the ability underneath comes out near 1.0: penalty killing is almost perfectly persistent and merely hard to see in one season. The cure for a noisy measurement is more of it, and three seasons beat one by 4.3% out of sample. Because averaging reorders clubs, that row's rank is a real forecast; the rows marked = are single-season regressions, which compress the values but keep them in order, so their rank is last season's by construction and only the value is a prediction.
A roster-aware faceoff model was built and rejected on the evidence. Draw counts are not stored, but they can be recovered exactly from the per-game percentages, and with real draws a club's number reconstructs from its own centres. It still does not predict better: given a club's first half to learn from and even handed the second half's draw distribution, it scored 2.24 against the plain regression's 2.26 across 32 clubs, and the best blend of the two puts almost no weight on it. A team's faceoff percentage already carries who takes its draws. Expected goals has no team projection at all, so that row shows last season alone.
Goals for, goals against and points percentage come from the roster: summed player projections over the real 26-27 schedule, last season's expected goals against regressed toward the league and adjusted for projected goaltending, and a game-by-game simulation of that schedule.
The special teams are projected from how much each number actually carries over year to year, measured across 298 team-season pairs going back to 2010-11. The power play uses one prior season shrunk 0.36; the penalty kill uses three, weighted .5/.3/.2 and shrunk 0.38. Special teams barely persist, so a league-worst penalty kill is mostly bad luck and comes most of the way back, while the faceoff dot is a repeatable team skill. How far a club moves depends on how far from the mean it started: Vancouver sits near league average on both the power play and the faceoff dot and barely shifts, while Edmonton's 30.6% power play comes back to the middle and this penalty kill, worst in the league, is the biggest riser of all 32.
The penalty kill averages three seasons because one measures it so badly. Split a single season's spread into ability and sampling noise — a kill rate is a binomial over about 250 opportunities, so the noise is known rather than guessed — and only 33% of the gap between clubs is real. Divide the year-over-year correlation by that and the ability underneath comes out near 1.0: penalty killing is almost perfectly persistent and merely hard to see in one season. The cure for a noisy measurement is more of it, and three seasons beat one by 4.3% out of sample. Because averaging reorders clubs, that row's rank is a real forecast; the rows marked = are single-season regressions, which compress the values but keep them in order, so their rank is last season's by construction and only the value is a prediction.
A roster-aware faceoff model was built and rejected on the evidence. Draw counts are not stored, but they can be recovered exactly from the per-game percentages, and with real draws a club's number reconstructs from its own centres. It still does not predict better: given a club's first half to learn from and even handed the second half's draw distribution, it scored 2.24 against the plain regression's 2.26 across 32 clubs, and the best blend of the two puts almost no weight on it. A team's faceoff percentage already carries who takes its draws. Expected goals has no team projection at all, so that row shows last season alone.
They held about the same pace all year — +2 points of win percentage between the first quarter and the last.
Oct–Nov10-09 – 11-18
50%10-10
for3.10
against3.05
Nov–Jan11-20 – 01-01
43%9-12
for3.05
against2.71
Jan–Mar01-03 – 03-03
65%13-7
for3.35
against2.55
Mar–Apr03-05 – 04-16
52%11-10
for3.57
against3.38
Light nights
34.5%3rd
29 of 84 games
Four-game weeks
717th
7 weeks of two or fewer
Back-to-backs
108th
roughly one backup start each
Playoff-week games
929th
over 3 weeks · 1 back-to-back
Games per week
2026-09-28on light nights2027-04-05
Games by month
Oct*
14Nov
15Dec
13Jan
13Feb
11Mar
12Apr*
6* 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.
Maveric Lamoureux — Shoulder: IR. Expected to be out until at least Feb 12 · still projected 15 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
0.47 points a game and 19.1 minutes walked out of the lineup — about 11 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 Trocheck, Lee, Weegar, Peeke, Hrabal, Cossa
Callup Lamoureux
Out Peterka→BOS, Durzi→NYR, Cole, Maatta→MIN, Kerfoot, Tanev→UFA, Rooney, Vanecek
15.6→14.6 -1
16.6→15.5 -1.1
15.1→13.7 -1.4
20.2→18.8 -1.4
14.5→13 -1.5
15.1→13.6 -1.5
20.6→19 -1.6
19.4→16.4 -3
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 slotUNDERDEPLOYED7.8 pts at stake
holds it
59 proj pts · 17.3′ · 2.6′ PP
vs
pushing
63 proj pts · 19′ · 3′ PP
Logan Cooleymodel favours the challengerVincent Trocheck
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.
Second power-play unit — forward slotUNDERDEPLOYED6 pts at stake
holds it
16 proj pts · 12.4′ · 1.5′ PP
vs
pushing
37 proj pts · 15.5′ · 1.2′ PP
Daniil Butmodel favours the challengerLawson Crouse
Conversion
20%
on the man advantage
PP goals
53
599 shots
Expected goals
59.9
-6.9 vs actual
Shooting
8.8%
of PP shots go in
Who it runs through · last season
PlayerPP/gmSharePPGPPAPPP/60ixGIPPFocal
3.19′64%521266.262.658%1.33
3.32′67%324275.956.558%1.26
3.24′65%915245.629.654%1.19
3.2′65%119204.589.350%0.97
2.62′53%64104.244.346%0.89
1.51′30%1234.111.3—0.86
1.12′23%3142.71253%0.57
2.12′43%4262.533.249%0.53
1.17′24%1010.642.245%0.13
0.8′16%00000.5—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.
Hits
16927th
projected, this roster · of 32
Blocks
118112th
projected, this roster · of 32
Shots
23926th
projected, this roster · of 32
Penalty minutes
7116th
projected, this roster · of 32
Faceoff wins
29541st
projected, this roster · of 32
H+B
28739th
projected, this roster · of 32
S+H+B
52655th
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.
$97.1Mcommitted · 23 of 23 on file
6reach the market after this season
Pending free agents · this summer
| Barrett HaytonC | UFA | $4.78M | 37 pts |
|---|---|---|---|
| Lawson CrouseL | UFA | $4.30M | 37 pts |
| Kevin StenlundC | UFA | $2.75M | 18 pts |
| Liam O'BrienC | UFA | $1.00M | 1 pts |
| Andrew PeekeD | UFA | $1.00M | 9 pts |
| Maveric LamoureuxD | RFA | $0.89M | 3 pts |
Free the summer after
| Clayton KellerR | $7.15M | 95 pts |
|---|---|---|
| Nate SchmidtD | $3.50M | 14 pts |
| Sebastian CossaG | $2.00M | |
| Michael CarconeL | $1.75M | 15 pts |
| Kailer YamamotoR | $1.75M | 18 pts |
| Nick DeSimoneD | $1.00M | 3 pts |
| Daniil ButL | $0.95M | 16 pts |
Biggest cap hits
| Logan CooleyC | $10.00M | 7y left |
|---|---|---|
| Mikhail SergachevD | $8.50M | 4y left · NTC |
| Nick SchmaltzC | $8.00M | 7y left · NMC |
| Clayton KellerR | $7.15M | 1y left · NMC |
| Dylan GuentherR | $7.14M | 6y left |
| MacKenzie WeegarD | $6.25M | 4y left · NTC |
| Vincent TrocheckC | $5.63M | 2y left · M-NTC |
| Anders LeeL | $5.40M | 2y left · NMC |
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
Vejmelka
63 NHL starts last season
GSAx / start
0.099lg -0.040561st pctile
Shot quality faced
0.1078lg 0.10467th hardest
10-start rolling GSAx · appearance 1-79 · shared scale
2026-27 projection
48 GS28 W (17–38)0.891 SV%3.00 GAA
2025-26 actual · NHL
63 GS38 W0.897 SV%2.75 GAA
Cossa
no games last season
GSAx / start
—Shot quality faced
—10-start rolling GSAx · appearance 1-1 · shared scale
2026-27 projection
34 GS17 W (10–22)0.897 SV%3.12 GAA
Vanecekgone
19 NHL starts last season
GSAx / start
-0.346lg -0.040513th pctile
Shot quality faced
0.1036lg 0.10448th hardest
10-start rolling GSAx · appearance 1-82 · shared scale
2026-27 projection
— GS— W— SV%— GAA
2025-26 actual · NHL
19 GS5 W0.883 SV%2.93 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Clayton Keller | L1·PP1 | 28 | +2.04 | 43 | 83 | 31 | 65 | 95.3 | 32 | 0 | 226 | 11 | 33 | 36 | +7 | 14 | 44 | 270 | sell-highPP1 | |
| Dylan Guenther | L2·PP1 | 23 | +1.91 | 54.1 | 79 | 37 | 39 | 75.3 | 29 | 0 | 252 | 65 | 34 | 29 | +2 | 22 | 99 | 351 | ascendingPP1 | |
| Vincent Trocheck | L1·PP2 | 33 | +1.45 | 119.2 | 78 | 22 | 41 | 63.2 | 10 | 3 | 149 | 206 | 61 | 61 | -8 | 772 | 267 | 416 | ice time ↓ | |
| Nick Schmaltz | L1·PP1 | 30 | +1.05 | 104.1 | 79 | 27 | 41 | 68 | 21 | 1 | 180 | 20 | 46 | 23 | +5 | 338 | 66 | 246 | PP1 | |
| Logan Cooley | L2·PP1 | 22 | +0.79 | 117.6 | 70 | 26 | 33 | 58.6/68 | 18 | 2 | 150 | 64 | 33 | 36 | +1 | 331 | 97 | 246 | PP1 | |
| Anders Lee | L2·PP2 | 36 | +0.59 | 238.3 | 76 | 24 | 21 | 44.8 | 7 | 0 | 177 | 96 | 36 | 40 | +1 | 44 | 132 | 309 | bounce-back | |
| Lawson Crouse | L3 | 29 | +0.53 | 224.3 | 82 | 20 | 17 | 36.5 | 2 | 1 | 135 | 205 | 51 | 43 | +8 | 27 | 256 | 391 | ||
| Jack McBain | L3 | 26 | +0.35 | 285.2 | 76 | 13 | 15 | 27.6 | 1 | 1 | 95 | 268 | 44 | 76 | +7 | 304 | 313 | 408 | bounce-back | |
| Barrett Hayton | L3 | 26 | -0.05 | 293.5 | 74 | 18 | 19 | 36.6 | 8 | 0 | 132 | 37 | 34 | 52 | -1 | 474 | 72 | 204 | bounce-backice time ↓ | |
| Kevin Stenlund | L4 | 30 | -0.93 | 292.4 | 79 | 8 | 11 | 18.3 | 0 | 2 | 72 | 47 | 58 | 31 | -6 | 588 | 105 | 176 | bounce-backice time ↓ | |
| Kailer Yamamoto | L4 | 28 | -1.08 | 292.4 | 65 | 9 | 9 | 18.3 | 1 | 0 | 66 | 52 | 23 | 17 | +7 | 24 | 75 | 141 | ||
| Michael Carcone | 30 | -1.14 | 292.9 | 43 | 8 | 8 | 15.2/29 | 2 | 0 | 70 | 67 | 10 | 16 | 0 | 7 | 77 | 148 | |||
| Daniil But | L4·PP2 | 21 | -1.15 | 292.6 | 43 | 8 | 8 | 15.6/29 | 4 | 0 | 78 | 27 | 25 | 16 | 0 | 11 | 52 | 130 | — | |
| Liam O'Brien | 32 | -2.10 | 291.8 | 7 | 0 | 0 | 0.5 | 0 | 0 | 6 | 21 | 3 | 9 | 0 | 1 | 24 | 30 |
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MacKenzie Weegar | RD1·PP2 | 32 | +0.97 | 90.5 | 81 | 7 | 27 | 34.3 | 10 | 1 | 157 | 182 | 175 | 69 | -13 | 0 | 356 | 513 | bounce-back | |
| Mikhail Sergachev | LD1·PP1 | 28 | +0.93 | 86.8 | 78 | 10 | 47 | 56.5 | 25 | 2 | 144 | 50 | 130 | 41 | -1 | 0 | 180 | 324 | PP1 | |
| Andrew Peeke | RD3 | 28 | -0.71 | 292.3 | 78 | 2 | 7 | 8.7 | 0 | 0 | 79 | 112 | 136 | 22 | -9 | 0 | 249 | 328 | ice time ↓ | |
| John Marino | RD2·PP2 | 29 | -0.92 | 203.2 | 71 | 4 | 25 | 28.5 | 0 | 1 | 50 | 26 | 69 | 17 | +22 | 0 | 95 | 145 | sell-high | |
| Nate Schmidt | LD2 | 35 | -0.93 | 292.9 | 76 | 3 | 11 | 14.2 | 1 | 0 | 71 | 71 | 82 | 22 | +18 | 0 | 153 | 225 | ||
| Dmitri Simashev | LD3 | 21 | -1.32 | 292.9 | 53 | 2 | 5 | 6.6 | 0 | 0 | 67 | 30 | 61 | 37 | -4 | 0 | 91 | 158 | — | |
| Nick DeSimone | 32 | -1.93 | 291.8 | 27 | 1 | 2 | 2.5 | 0 | 0 | 23 | 15 | 24 | 7 | +1 | 0 | 38 | 62 | |||
| Maveric Lamoureux | 22 | -1.93 | — | 15 | 1 | 2 | 3.1/14 | 0 | 0 | 14 | 20 | 14 | 15 | 0 | 0 | 34 | 48 | — |
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 · 4
| Goalie | GS | W | L | OTL | SV% | GAA | SV | SA | GA | SHO | GSAx | GSAx/GS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Karel Vejmelka | 48 | 28 | 15 | 5 | 0.891 | 3.00 | 1147 | 1288 | 141 | 1.5 | +6.2 | 0.099 |
| Sebastian Cossa | 34 | 17 | 11 | 4 | 0.897 | 3.12 | 900 | 1003 | 104 | 1.0 | 0 | — |
| Vitek Vanecek | — | — | — | — | — | — | — | — | — | — | -6.6 | -0.346 |
| Matt Villalta | — | — | — | — | — | — | — | — | — | — | 0 | — |
Projected record and projected ice time are model estimates; ranks, lines, projections and trend signals trace to real data.