Per 82 GP	2026-27projection adj	2025-2668 GP	H237 GP	H131 GP	Prev 387 GP · 2y
TOI	20.02	19.67	18.91	19.08	18.35
PP TOI	2.20	1.24	1.24	1.24	1.07
G	▲11	13	10	17	5
A	▲18	18	12	25	17
PTS	▲29	31	23	41	22
PPP	▲8	2	2	3	4
SOG	131	122	116	129	93
PIM	35	31	20	45	44
Hits	31	28	31	24	23
Blocks	136	126	129	121	124

Sample: 68 GP · 1,184 5v5 min (2025-26) — how much ice time backs these rates

Positional rankingDefense · league-wide (of 303)

77Travis Sanheim12

78Hampus Lindholm12

79Connor Murphy12

80Simon Nemec11

81Arber Xhekaj10

82Nick Seeler10

83Gustav Forsling9

offenseperipheralsvalue over replacement

Counts = projected deployment × projected /60 rate, per 82 GP. Rates regress off the process metric (ixG, shot-driving), not last year's finishing %. PP points = projected PP rate (anchored on the 2nd-half PP1 role, finishing regressed) × projected PP TOI.

Context-adjusted (▲/▼ vs the rate-model baseline): REVISED 2026-07-27 (CGY pass). The engine had him at 9-12-21 — TEN POINTS BELOW his own last-year per-82 of 13-18-31 — while every process read on his card says boost (5v5 ixG/60 0.23 vs 3-yr 0.19, shot volume +26%, iCF/60 11.39 vs 8.85, PP ixG/60 1.03 vs 0.68, age 22 up-slope). Cause is structural, not a rate error: at 22.4 with 87 prior GP he misses the young_riser gate on BOTH conditions (needs age <=22 AND <50 prior GP), so he gets zero development curve, and the flat TOI hides the trade. The finishing/secondary-assist regression is defensible — the ROLE input was stale. Fixed with deploy first (pp 1.24 -> 2.2, ev -> 19.4), which alone only reached 24 — the binding constraint is the RATE, not the minutes, so the role bet cascades weakly here. Topped up with G x1.10 / A x1.35 and a PPP set of 8 (the deploy cascade left PPP at 2 on 2.2 PP TOI, i.e. 0.66 PPP/60, which is not a PP-unit D). Lands 11-18-29. NOTE: 29 is a hard ceiling — G x1.15 / A x1.40 returns the same line, so a rail is binding above it. The original 32-35 target was NOT reached; getting there needs a full `set` line overriding the engine, which was not done deliberately: his 2 PPP on ~84 PP minutes and career 0.38 P/GP are real evidence for the engine's side. 29 is a ~36/64 lean back toward our own model vs the outside 43.7.

Metrics worth highlighting

The charts that move this projection — where he broke from his own 3-year rate, where the season split in half, and where we’re taking the number next year

We project it up

PP TOI

We’re projecting PP TOI 77% above last season, 1.24 to 2.20.

Second-half fade

iSH%

iSH% finished the year 80% below where it started (12.01 in the first half, 2.43 in the second). The projection leans on how the season ended, so the second half counts for more.

Second-half fade

all5v5

Assists ran at a 12 pace in the second half after 25 in the first, per 82 games.

Below his 3-year rate

1A/60

1A/60 ran 37% below his 3-year rate last season (0.30 against 0.48).

Picked automatically from every chart on this card, ranked by how far each number moved. Each claim is measured off the chart drawn beside it.

Similar & complementary players

Similar makeup Defense

Closest across all seven categories — match % is the whole-profile fit

Vladislav GavrikovD · New York Rangers86% match
Travis SanheimD · Philadelphia Flyers81% match
shared: G
Owen PowerD · Buffalo Sabres80% match
Hampus LindholmD · Boston Bruins78% match

Complementary Defense

Same position, opposite profile — strong where Simon Nemec is weak or average, light where he’s strong

Dougie HamiltonD · New Jersey Devilscovers 4
fills: SOG, PPP, A, Hits
Rasmus DahlinD · Buffalo Sabrescovers 3
fills: A, SOG, PPP
Cale MakarD · Colorado Avalanchecovers 3
fills: A, PPP, SOG
Evan BouchardD · Edmonton Oilerscovers 3
fills: A, PPP, SOG

Profile shape — per-82 category values vs the league p99 for the position (same scale as the hero bars)

This player

the profile below

vs Similar

similar players hug the shape

vs Complementary

complements fill the gaps

Deployment

Deployment projection — next-year TOI & PP TOI vs the last two years (the role that drives every count)

Coaching

2026-27 · Ryan Huska (CGY)

Last year (NJD, traded): Sheldon Keefe full season — no change

Metric	26-27 proj	2025-26	Prev 3	Diff
TOI	20.02	19.67	18.35	+7%
5v5 TOI	17.82	17.43	15.89	+10%
PP TOI	2.20	1.24	1.07	+15%

By quarter — 2025-26 vs 3-yr baseline

GP 20 · 11 · 19 · 18

TOI*

5v5 TOI

PP TOI

*Total TOI by quarter reconstructs 5v5+PP quarterly totals plus a held-flat season PK/GP — NST has no all-strength quarterly export. Level is accurate; quarter-to-quarter movement comes from 5v5+PP.

By season — year over year

TOI

5v5 TOI

PP TOI

TOI → 20.02: holds last year's role; age 22 riser — small growth bias
5v5 TOI → 17.82: holds last year's role; age 22 riser — small growth bias
PP TOI → 2.20: PP1-QB SPLIT with Parekh (2.4 / 2.2). The engine froze him at 1.24 — his New Jersey PP role — which is stale: he was traded into a Calgary blue line that lost both Andersson and Weegar. Hedged under a full PP1 QB projection because he turned ~84 PP minutes into 2 PPP last year; the production isn't proven, only the opportunity.

Shooting — volume & conversion

How much and how well he shoots — volume (SOG), finishing (% vs chance quality), and the individual shot-quality funnel (iCF ⊇ iSCF ⊇ iHDCF).

Metric (5v5)	26-27 proj	2025-26	Prev 3	Diff
SOG/60	4.12	4.00	3.17	+26%
G/60	0.24	0.25	0.18	+42%
iSH%	5.89	6.25	5.54	+13%
ixG/60	–	0.23	0.19	+18%
iCF/60	–	11.39	8.85	+29%
iSCF/60	–	3.09	2.73	+13%
iHDCF/60	–	0.51	0.61	-16%

By quarter — 2025-26 vs 3-yr baseline

GP 20 · 11 · 19 · 18

SOG/60

iSH%

ixG/60

iCF/60

iSCF/60

iHDCF/60

By season — year over year

SOG/60

iSH%

ixG/60

iCF/60

iSCF/60

iHDCF/60

Per-82 count — 3 seasons, 2025-26 halves & 26-27 projection

all5v5

SOG

all5v5

The blue 26-27 point is our projection. The bold line is all situations; the muted line under it is 5v5 alone, so the gap between them is his special-teams production. Full seasons are true per-82 counts; the 2025-26 H1/H2 reconstruct all-strength from the 5v5+PP quarterly exports plus the season's held-flat PK/other. The projected 5v5 point is the engine's even-strength share of the total, converted to 5v5 by his own 5v5-to-even-strength split, so overtime and 4-on-4 scoring stays out of it.

Regression read — where next year's /60 rate lands and why (process over finishing)

TailwindProcess rising: 5v5 ixG/60 0.23 (25th pct league-wide) up on the 3-yr 0.19 — the shot quality is real, so the projected goal rate leans on chances, not a hot %.

TailwindShot volume up 26% (5v5 SOG/60 4.00 vs 3.17) — volume is the repeatable half, so this raises the goal ceiling.

TailwindTrajectory: 5v5 ixG/60 climbed 0.18→0.28 across the year — process trending the right way into next season.

TailwindAge 22 — still on the up-slope of the scoring curve.

Assists — playmaking & on-ice offense

Primary vs secondary helpers, plus the on-ice offense and luck signals underneath them.

Metric (5v5)	26-27 proj	2025-26	Prev 3	Diff
on-ice SH%	8.84	8.80	8.88	-1%
IPP	32.40	34.03	30.01	+13%
1A/60	0.31	0.30	0.48	-37%
2A/60	0.12	0.25	0.09	+183%

By quarter — 2025-26 vs 3-yr baseline

GP 20 · 11 · 19 · 18

1A/60

2A/60

By season — year over year

1A/60

2A/60

Per-82 count — 3 seasons, 2025-26 halves & 26-27 projection

all5v5

PTS

all5v5

Regression read — where next year's /60 rate lands and why (process over finishing)

ContextAssist environment looks normal (on-ice SH% 8.8%, IPP 34.0%).

TailwindShot-driving up (5v5 iCF/60 11.39 vs 8.85) — real creation underneath, so don't over-regress the helpers.

RiskAssist gain leans on secondary helpers (2A/60 0.25 vs 3-yr 0.09) — the least repeatable slice; projected back toward baseline.

ContextDefenseman — weight transition/PP facilitation over raw 2A; secondary assists read a touch better for D than forwards.

Power play

PP opportunity and finishing — modelled separately from 5v5.

Metric	26-27 proj	2025-26	Prev 3	Diff
PP TOI	2.20	1.24	1.07	+15%
PP iSH%	3.13	7.12	0.00
PP SOG/60	7.88	9.97	11.56	-14%
PP ixG/60	–	1.03	0.68	+52%

By quarter — 2025-26 vs 3-yr baseline

GP 20 · 11 · 19 · 18

PP TOI/GP

PP SOG/60

PP ixG/60

By season — year over year

PP TOI/GP

PP SOG/60

PP ixG/60

Per-82 count — 3 seasons, 2025-26 halves & 26-27 projection

PPP

PPG

PPA

The blue 26-27 point is our projection. PPG and PPA are the 2 halves of the PPP above them, split by the projection's own goal/assist mix. PP-only, so the halves are exact.

Regression read — where next year's /60 rate lands and why (process over finishing)

TailwindPP role projected up: 1.2→2.2 PP TOI/gp (PP1-QB SPLIT with Parekh (2.4 / 2.2). The engine froze him at 1.24 — his New Jersey PP role — which is stale: he was traded into a Calgary blue line that lost both Andersson and Weegar. Hedged under a full PP1 QB projection because he turned ~84 PP minutes into 2 PPP last year; the production isn't proven, only the opportunity.) — the biggest PPP lever.

TailwindPP chance quality up (PP ixG/60 1.03 vs 0.68) — a real process gain, the kind that sustains PP points.

Physical — PIM · hits · blocks

Peripheral counting categories. Noisier and scorer-biased — read as role over the years, not pure talent.

Metric (5v5)	26-27 proj	2025-26	Prev 3	Diff
PIMs/60	1.14	1.22	1.69	-28%
Hits/60	1.22	1.16	0.91	+27%
Blks/60	4.98	4.96	4.77	+4%

By quarter — 2025-26 vs 3-yr baseline

GP 20 · 11 · 19 · 18

PIMs/60

Hits/60

Blks/60

By season — year over year

PIMs/60

Hits/60

Blks/60

Per-82 count — 3 seasons, 2025-26 halves & 26-27 projection

PIM

all5v5

HITS

all5v5

BLK

all5v5

Regression read — where next year's /60 rate lands and why (process over finishing)

TailwindHits surging late: Q4 1.68/60 and 2nd-half 1.31 vs a 1.16 season rate (0.91 3-yr) (11th pct league-wide) — reads as a real role/confidence step, so projected UP to 1.22/60, weighting the finish over the 3-yr baseline.

TailwindBlocks surging late: Q4 6.15/60 and 2nd-half 5.14 vs a 4.96 season rate (4.77 3-yr) (92nd pct league-wide) — reads as a real role/confidence step, so projected UP to 4.98/60, weighting the finish over the 3-yr baseline.