Fantasy tools
Stack Finder: best line and PP1 stacks for Wednesday, Dec 2
Data updated:
Top 10 stacks
John Carlson98%, Nikita Kucherov100%, Jake Guentzel100%, Brayden Point95%, Brandon Hagel100%
Matchup factors →Mark Scheifele98%, Kyle Connor99%, Cole Perfetti61%
Matchup factors →Vincent Trocheck87%, Nick Schmaltz95%, Clayton Keller99%
Matchup factors →Sidney Crosby100%, Rickard Rakell82%, Nick Robertson32%
Matchup factors →Robert Thomas91%, Dylan Holloway93%, Jimmy Snuggerud79%
Matchup factors →Anders Lee28%, Dylan Guenther100%, Logan Cooley94%
Matchup factors →Jake Guentzel100%, Anthony Cirelli49%, Brandon Hagel100%
Matchup factors →Evgeni Malkin86%, Tommy Novak32%, Egor Chinakhov50%
Matchup factors →Nikita Kucherov100%, Brayden Point95%, Gage Goncalves10%
Matchup factors →Lawson Crouse34%, Barrett Hayton10%, Jack McBain17%
Matchup factors →
By game
TBL @ MTL
John Carlson98%, Nikita Kucherov100%, Jake Guentzel100%, Brayden Point95%, Brandon Hagel100%
5.5 min/game · 7.42 xGF/60 · P(intact) 95%
- MTL shorthanded time +3%
- MTL PK xGA/60 +10%
- vs Jakub Dobes .900 (82% to start)
- Dobes .868 on rebounds
- Together 3 of last 3
- DK correlation .28
Jake Guentzel100%, Anthony Cirelli49%, Brandon Hagel100%
8.9 min/game · 3.78 xGF/60 · P(intact) 95%
- MTL 5v5 xGA/60 +6%
- vs Jakub Dobes .900 (82% to start)
- Dobes .868 on rebounds
- Together 3 of last 3
- DK correlation .27
Nikita Kucherov100%, Brayden Point95%, Gage Goncalves10%
9.7 min/game · 3.65 xGF/60 · P(intact) 82%
- MTL 5v5 xGA/60 +6%
- vs Jakub Dobes .900 (82% to start)
- Dobes .868 on rebounds
- Together 2 of last 3
- DK correlation .29
Nick Suzuki100%, Cole Caufield100%, Lane Hutson100%, Juraj Slafkovský98%, Ivan Demidov92%
3.6 min/game · 8.68 xGF/60 · P(intact) 95%
- TBL shorthanded time +19%
- TBL PK xGA/60 −5%
- vs Andrei Vasilevskiy .912 (97% to start)
- Vasilevskiy .866 on rebounds
- Together 3 of last 3
- DK correlation .20
Zemgus Girgensons3.2%, Pontus Holmberg0.1%, Ilya Mikheyev7.5%
8.4 min/game · 2.71 xGF/60 · P(intact) 82%
- MTL 5v5 xGA/60 +6%
- vs Jakub Dobes .900 (82% to start)
- Dobes .868 on rebounds
- Together 2 of last 3
- DK correlation .29
Chris Kreider61%, Nick Suzuki100%, Cole Caufield100%
10.4 min/game · 2.44 xGF/60 · P(intact) 82%
- TBL 5v5 xGA/60 −7%
- vs Andrei Vasilevskiy .912 (97% to start)
- Vasilevskiy .866 on rebounds
- Together 2 of last 3
- DK correlation .28
Josh Anderson11%, Jake Evans4.9%, Zachary Bolduc21%
7.2 min/game · 3.06 xGF/60 · P(intact) 82%
- TBL 5v5 xGA/60 −7%
- vs Andrei Vasilevskiy .912 (97% to start)
- Vasilevskiy .866 on rebounds
- Together 2 of last 3
- DK correlation .28
Alex Newhook25%, Juraj Slafkovský98%, Ivan Demidov92%
8.9 min/game · 1.51 xGF/60 · P(intact) 82%
- TBL 5v5 xGA/60 −7%
- vs Andrei Vasilevskiy .912 (97% to start)
- Vasilevskiy .866 on rebounds
- Together 2 of last 3
- DK correlation .27
Phillip Danault4.9%, Alexandre Texier4.9%, Kirby Dach11%
7.8 min/game · 1.28 xGF/60 · P(intact) 82%
- TBL 5v5 xGA/60 −7%
- vs Andrei Vasilevskiy .912 (97% to start)
- Vasilevskiy .866 on rebounds
- Together 2 of last 3
PIT @ STL
Sidney Crosby100%, Rickard Rakell82%, Nick Robertson32%
10.5 min/game · 3.60 xGF/60 · P(intact) 95%
- STL 5v5 xGA/60 −2%
- vs Joel Hofer .911 (69% to start)
- Hofer .804 on rebounds
- Together 3 of last 3
- DK correlation .27
Robert Thomas91%, Dylan Holloway93%, Jimmy Snuggerud79%
9.1 min/game · 3.68 xGF/60 · P(intact) 95%
- PIT 5v5 xGA/60 +4%
- vs Arturs Silovs .888 (76% to start)
- Silovs .767 on rebounds
- Together 2 of last 2
- DK correlation .28
Evgeni Malkin86%, Tommy Novak32%, Egor Chinakhov50%
10.3 min/game · 3.42 xGF/60 · P(intact) 95%
- STL 5v5 xGA/60 −2%
- vs Joel Hofer .911 (69% to start)
- Hofer .804 on rebounds
- Together 3 of last 3
- DK correlation .27
Sidney Crosby100%, Erik Karlsson95%, Rickard Rakell82%, Tommy Novak32%, Ben Kindel43%
2.4 min/game · 7.10 xGF/60 · P(intact) 82%
- STL shorthanded time −5%
- STL PK xGA/60 +2%
- vs Joel Hofer .911 (69% to start)
- Hofer .804 on rebounds
- Together 2 of last 3
- DK correlation .20
Jonatan Berggren4.0%, Jake Neighbours29%, Dalibor Dvorsky28%
6.0 min/game · 2.13 xGF/60 · P(intact) 95%
- PIT 5v5 xGA/60 +4%
- vs Arturs Silovs .888 (76% to start)
- Silovs .767 on rebounds
- Together 2 of last 2
- DK correlation .28
Cam Fowler14%, Robert Thomas91%, Dylan Holloway93%, Mason McTavish50%, Jimmy Snuggerud79%
3.4 min/game · 4.18 xGF/60 · P(intact) 95%
- PIT shorthanded time +0%
- PIT PK xGA/60 −7%
- vs Arturs Silovs .888 (76% to start)
- Silovs .767 on rebounds
- Together 2 of last 2
- DK correlation .18
Dillon Dube0%, Pius Suter4.2%, Zach Dean4.0%
10.4 min/game · 1.52 xGF/60 · P(intact) 75%
- PIT 5v5 xGA/60 +4%
- vs Arturs Silovs .888 (76% to start)
- Silovs .767 on rebounds
- Together 1 of last 2
Pavel Buchnevich33%, Connor McMichael33%, Mason McTavish50%
6.3 min/game · 1.81 xGF/60 · P(intact) 95%
- PIT 5v5 xGA/60 +4%
- vs Arturs Silovs .888 (76% to start)
- Silovs .767 on rebounds
- Together 2 of last 2
Hendrix Lapierre12%, Andrei Kuzmenko9.7%, Ben Kindel43%
7.4 min/game · 1.55 xGF/60 · P(intact) 12%
- STL 5v5 xGA/60 −2%
- vs Joel Hofer .911 (69% to start)
- Hofer .804 on rebounds
- Together 1 of last 3
Filip Hallander12%, Connor Dewar4.2%, Blake Lizotte0.2%
7.2 min/game · 1.64 xGF/60 · P(intact) 12%
- STL 5v5 xGA/60 −2%
- vs Joel Hofer .911 (69% to start)
- Hofer .804 on rebounds
- Together 1 of last 3
- DK correlation .31
WPG @ UTA
Mark Scheifele98%, Kyle Connor99%, Cole Perfetti61%
13.3 min/game · 3.04 xGF/60 · P(intact) 95%
- UTA 5v5 xGA/60 −3%
- vs Karel Vejmelka .897 (90% to start)
- Vejmelka .735 on rebounds
- Together 3 of last 3
- DK correlation .27
Vincent Trocheck87%, Nick Schmaltz95%, Clayton Keller99%
10.9 min/game · 3.40 xGF/60 · P(intact) 95%
- WPG 5v5 xGA/60 +3%
- vs Stuart Skinner .889 (59% to start)
- Skinner .804 on rebounds
- Together 3 of last 3
- DK correlation .29
Anders Lee28%, Dylan Guenther100%, Logan Cooley94%
9.6 min/game · 3.52 xGF/60 · P(intact) 95%
- WPG 5v5 xGA/60 +3%
- vs Stuart Skinner .889 (59% to start)
- Skinner .804 on rebounds
- Together 3 of last 3
- DK correlation .28
Lawson Crouse34%, Barrett Hayton10%, Jack McBain17%
8.7 min/game · 3.23 xGF/60 · P(intact) 95%
- WPG 5v5 xGA/60 +3%
- vs Stuart Skinner .889 (59% to start)
- Skinner .804 on rebounds
- Together 3 of last 3
- DK correlation .28
Mark Scheifele98%, Josh Morrissey99%, Kyle Connor99%, Gabriel Vilardi80%, Cole Perfetti61%
2.5 min/game · 8.91 xGF/60 · P(intact) 95%
- UTA shorthanded time −4%
- UTA PK xGA/60 +1%
- vs Karel Vejmelka .897 (90% to start)
- Vejmelka .735 on rebounds
- Together 3 of last 3
- DK correlation .19
Kevin Stenlund0.1%, Michael Carcone6.6%, Daniil But20%
7.9 min/game · 2.67 xGF/60 · P(intact) 95%
- WPG 5v5 xGA/60 +3%
- vs Stuart Skinner .889 (59% to start)
- Skinner .804 on rebounds
- Together 3 of last 3
- DK correlation .28
Nick Schmaltz95%, Clayton Keller99%, Mikhail Sergachev98%, Dylan Guenther100%, Logan Cooley94%
1.9 min/game · 9.64 xGF/60 · P(intact) 95%
- WPG shorthanded time −10%
- WPG PK xGA/60 −1%
- vs Stuart Skinner .889 (59% to start)
- Skinner .804 on rebounds
- Together 3 of last 3
- DK correlation .21
Gabriel Vilardi80%, Isak Rosen14%, Viggo Björck43%
8.2 min/game · 1.94 xGF/60 · P(intact) 95%
- UTA 5v5 xGA/60 −3%
- vs Karel Vejmelka .897 (90% to start)
- Vejmelka .735 on rebounds
- Together 3 of last 3
Adam Lowry7.5%, Morgan Barron4.8%, Brad Lambert15%
8.6 min/game · 1.17 xGF/60 · P(intact) 95%
- UTA 5v5 xGA/60 −3%
- vs Karel Vejmelka .897 (90% to start)
- Vejmelka .735 on rebounds
- Together 3 of last 3
- DK correlation .28
Nino Niederreiter4.0%, Vladislav Namestnikov0.1%, Alex Iafallo4.2%
7.1 min/game · 1.04 xGF/60 · P(intact) 23%
- UTA 5v5 xGA/60 −3%
- vs Karel Vejmelka .897 (90% to start)
- Vejmelka .735 on rebounds
- Together 2 of last 3
- DK correlation .28
How it works
- Lines and PP1 come from each team's last three games of shift data: the forward trio with the most 5-on-5 time together, and the power-play five with the most time together.
- Unit strength is expected goals for per 60 minutes, recent games plus the season, shrunk toward league average so a hot two-game sample does not dominate.
- Matchup: the opponent's 5-on-5 expected goals against per 60 (for lines) or its penalty-kill rate and shorthanded minutes (for PP1), relative to the league.
- Goalie: the likely opposing starter from our goalie start model, weighted by probability, with his save % shrunk toward league average; shot-class save % when available.
- Stack score = rate × minutes × P(unit intact) × matchup × goalie, in expected goals for tonight, so lines and power plays rank on one scale.
Methodology: How we detect lines · Expected goals · Goalie shot classes · Goalie start model
Frequently asked questions
What is a stack in fantasy hockey?
A stack is a group of teammates who play together, usually a forward line or the first power-play unit, so one goal pays several of your players at once. It is the main strategy in daily fantasy hockey.
How are stacks ranked?
Each unit gets an expected-goals number for tonight: how many chances it creates per 60 minutes (recent games plus season, shrunk toward league average), how many minutes it plays, how likely it is to stay together, how leaky the opponent is at 5-on-5 or shorthanded, and how good the likely opposing goalie is.
Where do the lines come from?
From the NHL shift charts of each team's last three games: the three forwards who share the most 5-on-5 ice time form a line, and the five skaters with the most power-play time together form PP1.
Why does it say "shot-based"?
While a season is being reprocessed with our expected-goals model, unit rates fall back to shots on goal times a league-average 0.075 xG per shot. The ranking logic is the same.