Category Week Planner · 2026-27

Fantasy hockey week 9 planner (Nov 23-29)

Games, off-nights and schedule strength by category for every NHL team, then a player rater with punt mode for your league. 10 teams play four or more games and there are 3 off-nights.

Data updated: Oct 4, 11:01 AM ET

Mon7off-night
Tue1off-night
Wed14games
Thu0no games
Fri15games
Sat13games
Sun2off-night

Schedule strength by category

Category columns are effective games: each game weighted by how much the opponent allows in that category (for saves, how many shots the opponent takes). Green is at least 5% better per game than average, gold at least 5% worse. Click a category for its own page.

Early season: teams have played about 2 games, so the opponent factors are mostly 2025-26 numbers. This season's games count for about 20% and the share grows every week.

Games, off-nights, back-to-backs, schedule edge and category strength per team, week 9
Team	GP	Off	B2B	Edge	SOG	HIT	BLK	FOW	SV
MIN	4	2		−7.8	4.02	4.06	3.94	4.15	4.02
ANA	4	1	1		4.04	3.90	3.96	4.07	4.04
CGY	4	1	1	−3.4	3.88	4.14	4.07	3.91	4.16
NJD	4	1	1	+0.3	4.11	3.84	4.08	3.97	3.92
NYI	4	1	1	−0.6	4.21	3.79	3.71	3.92	3.82
OTT	4	1	1	−12.1	3.84	4.22	4.55	4.12	4.05
SEA	4	1	1	+3.1	3.96	3.87	3.89	4.00	4.03
SJS	4	1	1	−3.8	3.92	3.83	4.13	4.12	4.30
UTA	4	1	1		4.02	3.96	3.74	3.95	3.98
WPG	4	1	1	−0.1	3.98	3.92	4.22	3.91	4.19
BOS	3	1			3.14	2.95	2.85	2.92	2.92
CBJ	3	1			2.98	3.00	3.31	2.97	3.28
CHI	3	1		+1.6	2.98	2.99	2.84	3.02	2.91
MTL	3	1		+11.1	2.89	2.94	3.20	2.99	3.28
VAN	3	1			3.13	3.01	2.86	2.96	2.86
EDM	3	1	1	−5.0	2.94	3.00	2.74	3.03	2.98
LAK	3	1	1	+0.2	3.06	2.83	3.09	3.07	3.00
TOR	3	1	1	−0.6	3.03	3.02	2.91	2.97	3.08
VGK	3	1	1	−9.8	3.00	2.88	2.97	2.94	3.00
COL	3		1	−0.8	3.07	2.88	2.90	3.06	2.98
DAL	3		1	+2.2	2.97	2.87	3.02	3.03	2.94
DET	3		1	+3.4	3.15	2.95	2.98	3.00	2.87
FLA	3		1	−0.5	2.94	3.09	3.02	2.94	3.01
NSH	3		1	−3.3	2.82	3.07	2.96	2.93	2.98
NYR	3		1	−1.0	3.09	3.16	2.85	3.16	2.84
PHI	3		1	+0.7	3.14	2.96	2.99	2.92	2.94
PIT	3		1	+0.8	3.07	3.01	2.97	3.05	2.97
STL	3		1	+0.7	3.03	3.01	2.88	2.85	2.97
TBL	3		1	−2.9	2.79	3.24	3.30	3.03	3.09
WSH	3		1	−4.1	3.09	2.90	2.99	3.05	2.80
BUF	2				1.93	2.10	1.95	1.98	1.99
CAR	2			+12.2	1.90	2.10	2.03	1.95	2.04

Matchup models plugged in

Shot Prop Lab: matchup-adjusted shots on goalSOGComing soon
Banger Environment Index: hits and blocksHIT, BLKComing soon
Draw Duel: projected faceoff winsFOWComing soon
PP Opportunity Forecast: power-play minutesPPPComing soon

Until a model is live, its categories use the team opponent factors above (or volume only where those did not validate).

Player rater for week 9

Projected totals for the week, ranked by z-score across your categories. Punt a category to ignore it; weight one up to chase it. Totals already include games played and each opponent's category environment.

Players ranked for week 9 by the selected categories
#	Player	GP	Score	G	A	+/-	PPP	SOG	HIT	BLK	W	GAA	SV%	SO
1	Nathan MacKinnonCOL · C	3	10.56	1.61	3.00	0.94	1.40	13.7	2.01	1.52
2	Macklin CelebriniSJS · C	4₁	10.08	1.95	3.37	-0.29	1.64	14.0	2.24	3.07
3	Ilya SorokinNYI · G	3.1 GS₁	9.01								1.48	2.63	.908	0.48
4	Kirill KaprizovMIN · LW	4₂	9.00	2.49	2.49	-0.31	1.57	14.1	2.58	1.54
5	Dylan GuentherUTA · RW	4₁	7.95	2.15	1.76	0.30	1.21	13.6	3.14	1.55
6	Cale MakarCOL · D	3	7.88	0.88	2.45	1.09	1.38	8.49	1.41	4.57
7	Jack HughesNJD · C	4₁	7.76	1.85	2.78	-0.28	1.31	15.8	0.42	1.94
8	Connor McDavidEDM · C	3₁	7.65	1.52	3.56	-0.12	1.86	9.89	1.85	1.05
9	Nikita KucherovTBL · RW	3	7.44	1.46	2.93	0.41	1.84	8.66	1.30	1.33
10	Quinn HughesMIN · D	4₂	7.37	0.78	3.76	-0.38	1.95	10.6	0.55	4.13
11	Karel VejmelkaUTA · G	3.0 GS₁	7.32								1.61	2.52	.910	0.19
12	Matthew BoldyMIN · RW	4₂	7.26	1.81	2.19	-0.28	1.38	13.3	3.02	2.88
13	Clayton KellerUTA · LW	4₁	6.49	1.27	3.07	0.34	1.35	10.6	0.43	1.55
14	Tim StutzleOTT · C	4₁	6.46	1.45	2.46	-0.04	1.21	9.44	6.41	2.47
15	Kyle ConnorWPG · LW	4₁	6.32	1.78	2.42	0.11	1.10	12.7	1.16	1.36
16	Cutter GauthierANA · LW	4₁	5.92	2.10	1.50	-0.26	0.93	15.3	3.41	1.43
17	Andrei VasilevskiyTBL · G	2.2 GS	5.84								1.22	2.45	.918	0.07
18	Joel HoferSTL · G	1.7 GS	5.82								0.87	2.46	.913	0.31
19	Bo HorvatNYI · C	4₁	5.77	1.76	1.61	-0.07	1.07	13.4	3.28	2.04
20	Martin NecasCOL · RW	3	5.74	1.32	2.27	0.83	1.11	8.57	2.81	0.92
21	Mikhail SergachevUTA · D	4₁	5.64	0.54	2.49	0.45	1.06	7.94	2.61	6.03
22	Mark ScheifeleWPG · C	4₁	5.61	1.63	2.62	0.11	1.11	8.59	1.99	2.55
23	Auston MatthewsTOR · C	3₁	5.56	1.67	1.56	0.20	0.83	12.0	2.09	3.59
24	Dylan CozensOTT · C	4₁	5.33	1.21	1.78	-0.03	0.93	9.90	10.8	1.59
25	Matthew SchaeferNYI · D	4₁	5.32	0.88	2.15	-0.08	1.13	12.3	1.80	4.67
26	Jake SandersonOTT · D	4₁	5.23	0.66	2.13	-0.05	1.02	8.75	1.92	8.36
27	Vincent TrocheckUTA · C	4₁	5.21	1.02	1.76	0.38	0.66	8.04	10.3	2.97
28	Leo CarlssonANA · C	4₁	4.98	1.93	2.17	-0.29	1.15	11.4	0.80	1.91
29	David PastrnakBOS · RW	3₁	4.76	1.30	2.37	0.11	0.98	11.5	2.77	0.99
30	Leon DraisaitlEDM · C	3₁	4.50	1.70	2.37	-0.12	1.42	8.81	1.52	0.81
31	Jet GreavesCBJ · G	2.4 GS₁	4.45								1.25	2.70	.914	0.09
32	Mathew BarzalNYI · RW	4₁	4.34	1.00	2.80	-0.07	1.08	9.99	1.66	2.41
33	Nick SchmaltzUTA · RW	4₁	4.33	1.56	1.85	0.34	0.90	9.70	1.01	2.19
34	Logan ThompsonWSH · G	2.1 GS	4.10								1.26	2.53	.907	0.14
35	Jack EichelVGK · C	3₁	4.09	1.32	2.42	-0.24	1.24	10.4	1.19	1.85
36	Josh MorrisseyWPG · D	4₁	4.06	0.58	2.22	0.13	0.93	8.29	1.98	6.00
37	Drake BathersonOTT · RW	4₁	3.99	1.30	1.98	-0.04	1.00	8.65	6.15	1.48
38	Zach WerenskiCBJ · D	3₁	3.97	0.70	2.16	0.09	0.91	10.3	0.95	4.97
39	Timo MeierNJD · LW	4₁	3.90	1.37	1.17	-0.26	0.49	13.7	6.64	2.84
40	Logan CooleyUTA · C	4₁	3.90	1.27	2.19	0.32	0.86	7.60	3.76	1.78
41	Brandon HagelTBL · LW	3	3.82	1.39	1.83	0.45	0.91	8.11	2.10	1.89
42	Jeremy SwaymanBOS · G	2.7 GS₁	3.79								1.34	2.72	.905	0.19
43	Jesper WallstedtMIN · G	1.8 GS₂	3.75								0.97	2.67	.910	0.21
44	Brady TkachukFLA · LW	3	3.70	1.06	1.17	0.13	0.81	10.8	8.90	1.07
45	MacKenzie WeegarUTA · D	4₁	3.63	0.24	1.17	0.45	0.30	7.06	8.69	7.81
46	Joey DaccordSEA · G	2.7 GS₁	3.56								1.29	2.62	.908	0.06
47	Evan BouchardEDM · D	3₁	3.47	0.85	2.30	-0.14	1.38	8.23	1.22	3.41
48	Jackson LaCombeANA · D	4₁	3.43	0.50	2.40	-0.40	0.93	7.99	3.56	6.63
49	Mackenzie BlackwoodCOL · G	2.0 GS	3.43								1.17	2.62	.907	0.15
50	Jake GuentzelTBL · LW	3	3.39	1.21	1.79	0.40	1.23	7.33	1.42	1.73

570 players match. Small green numbers are off-night games. Hover a value for its z-score; “–” means no projection for that category.

Which opponent effects are real?

Split-half r compares each team's allowed rate in odd and even games of last season (32 teams); season reliability is the Spearman-Brown step-up. Year-over-year r compares team factors between the two previous seasons. The gain is out of sample: in 6 runs (2024-25 and 2025-26, each split on December 1, January 1 and February 1) we fit on games before the split, predict every team's weekly totals after it, and report how much of the error left by games times the team's own rate the opponent factor removes, pooled over 2,764 team-weeks with a 90% bootstrap interval. A category uses opponent factors only if the pooled gain is at least 1% and the interval stays above zero (tested 2026-10-04). Single runs vary a lot (the range column), and even where factors help the gain is small: games played matter far more.

Team-level reliability and out-of-sample forecast gain of each category's opponent factor
Category	Per game	Split-half r	Season reliability	Year-over-year r	Pooled gain (90% interval)	Range over runs	Used
Goals	3.1	0.36	0.53	0.29	+0.3% (−0.2% to +0.7%)	−0.1% to +0.9%	No, volume only
Assists	5.3	0.45	0.62	0.32	+0.4% (−0.1% to +0.9%)	−0.1% to +1.4%	No, volume only
Points	8.4	0.43	0.60	0.31	+0.3% (−0.1% to +0.8%)	−0.1% to +1.2%	No, volume only
Shots on goal	27.0	0.84	0.92	0.55	+2.0% (+1.3% to +2.7%)	+0.9% to +3.9%	Yes (small effect)
Hits	24.2	0.81	0.90	0.66	+1.9% (+1.2% to +2.6%)	+0.6% to +3.1%	Yes (small effect)
Blocks	14.6	0.85	0.92	0.73	+1.5% (+0.8% to +2.2%)	−0.1% to +2.9%	Yes (small effect)
Penalty minutes	10.5	0.56	0.72	0.54	+0.9% (+0.6% to +1.2%)	+0.5% to +1.2%	No, volume only
Faceoffs won	29.4	0.79	0.88	0.71	+2.4% (+1.5% to +3.2%)	+1.2% to +3.7%	Yes (small effect)
Saves	27.0	0.86	0.93	0.62	+2.1% (+1.4% to +2.9%)	+0.5% to +4.5%	Yes (small effect)
Goals against	3.1	0.48	0.65	0.40	+0.4% (+0.0% to +0.8%)	+0.0% to +0.9%	No, volume only
Goals-against average	3.1	0.48	0.65	0.40	–	–	Not tested, volume only

How it works

Volume first. We count each team's games in the Monday-to-Sunday fantasy week and flag off-nights (fewer than 10 games) and back-to-backs. See games per week for the whole season.
Opponents second, only where it helps. For each category we measure what every team allows per game (shots on goal against, hits and blocks its opponents record, faceoffs its opponents win, goals against). A factor of 1.10 means 10% more than the league average. We then tested out of sample whether these factors improve weekly forecasts over games times each team's own rate. They did, by a small margin, for shots on goal, hits, blocks, faceoffs won and goalie saves (where the factor is the opponent's own shots), so only those use them. Goals, assists, points, penalty minutes and goals against are volume only. The table below has the numbers.
Each factor is shrunk toward average by how reliable that category is at the team level. We split last season's games into odd and even halves: when the halves agree (high split-half r), team differences are real and the factor is kept; when they do not, it is pulled toward 1. Early in the season this year's small sample is blended with last season, regressed by how well team factors carried over from one season to the next.
Player rates come from rest-of-season projections: LineupExperts for goals, assists, points, PIM, shots, hits, blocks and goalie wins, saves and goals against, with our derived projection model filling power-play points, shorthanded points, plus/minus and faceoffs. Anything still missing (and shutouts) comes from our box scores, this season plus last season at half weight.
Goalies: expected starts come from our goalie start model where it covers the week, otherwise from each goalie's share of projected starts, normalised so a team's goalies add up to one start per game. Wins get the Schedule Edge win-probability change for each game.
The rater turns weekly totals into z-scores within the top 300 skaters and 64 goalies, then sums them with your weights. Punted categories count for nothing. SV% and GAA are scored by impact, so one start cannot top them.

Frequently asked questions

Which NHL teams play four games in fantasy week 9?

MIN, ANA, CGY, NJD, NYI, OTT, SEA, SJS, UTA, WPG play four or more games from Nov 23-29.

What are the off-nights in week 9?

Monday, Nov 23 (7 games), Tuesday, Nov 24 (1 games), Sunday, Nov 29 (2 games).

Which teams have the best shots, hits and blocks schedule this week?

Counting games and opponents together: shots on goal NYI, NJD, ANA; hits OTT, CGY, MIN; blocks OTT, WPG, SJS.

What does punting a category mean?

Punting means giving up on a category on purpose (often plus/minus, PIM or faceoffs) to win more of the rest. Tick P on a category in the rater and it stops counting toward a player’s score, so players who help elsewhere rise.

Can it use my league’s categories?

Yes. Connect an ESPN or Fantrax league and press “Use my ESPN / Fantrax league”: the rater loads your categories (or points per stat in a points league), marks your own players and can hide players rostered in your league. Yahoo sync is unavailable right now.

Games per night