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NHL Stack Finder: Friday, Nov 6
Data updated:
Top 10 stacks
Joel Eriksson Ek81%, Kirill Kaprizov98%, Quinn Hughes100%, Matt Boldy100%, Maxim Shabanov27%
Matchup factors →Jack Quinn47%, Noah Ostlund26%, Zach Benson56%
Matchup factors →Tage Thompson100%, Josh Norris38%, Josh Doan61%
Matchup factors →A.J. Greer7.6%, Cutter Gauthier98%, Leo Carlsson94%
Matchup factors →Kent Johnson31%, Matthew Knies95%, Adam Fantilli91%
Matchup factors →Alexander Wennberg24%, Luca Cagnoni64%, Will Smith95%, Macklin Celebrini100%, Igor Chernyshov53%
Matchup factors →Ryan McLeod26%, Peyton Krebs19%, Konsta Helenius40%
Matchup factors →Alex Killorn5.1%, Mikael Granlund52%, Beckett Sennecke94%
Matchup factors →Charlie Coyle49%, Mathieu Olivier18%, Cole Sillinger11%
Matchup factors →Mason Marchment47%, Will Smith95%, Macklin Celebrini100%
Matchup factors →
By game
CBJ @ BUF
Jack Quinn47%, Noah Ostlund26%, Zach Benson56%
12.4 min/game · 3.49 xGF/60 · P(intact) 82%
- CBJ 5v5 xGA/60 −4%
- vs Cam Talbot .883 (57% to start)
- Talbot .812 on rebounds
- Together 2 of last 3
- DK correlation .28
Tage Thompson100%, Josh Norris38%, Josh Doan61%
11.8 min/game · 3.20 xGF/60 · P(intact) 82%
- CBJ 5v5 xGA/60 −4%
- vs Cam Talbot .883 (57% to start)
- Talbot .812 on rebounds
- Together 2 of last 3
- DK correlation .28
Kent Johnson31%, Matthew Knies95%, Adam Fantilli91%
11.4 min/game · 2.55 xGF/60 · P(intact) 95%
- BUF 5v5 xGA/60 −2%
- vs Ukko-Pekka Luukkonen .905 (67% to start)
- Luukkonen .867 on rebounds
- Together 2 of last 2
- DK correlation .28
Ryan McLeod26%, Peyton Krebs19%, Konsta Helenius40%
8.2 min/game · 3.76 xGF/60 · P(intact) 82%
- CBJ 5v5 xGA/60 −4%
- vs Cam Talbot .883 (57% to start)
- Talbot .812 on rebounds
- Together 2 of last 3
- DK correlation .28
Charlie Coyle49%, Mathieu Olivier18%, Cole Sillinger11%
10.2 min/game · 2.64 xGF/60 · P(intact) 95%
- BUF 5v5 xGA/60 −2%
- vs Ukko-Pekka Luukkonen .905 (67% to start)
- Luukkonen .867 on rebounds
- Together 2 of last 2
- DK correlation .28
Tage Thompson100%, Rasmus Dahlin100%, Jack Quinn47%, Josh Doan61%, Zach Benson56%
2.1 min/game · 8.35 xGF/60 · P(intact) 95%
- CBJ shorthanded time −7%
- CBJ PK xGA/60 +11%
- vs Cam Talbot .883 (57% to start)
- Talbot .812 on rebounds
- Together 3 of last 3
- DK correlation .20
Charlie Coyle49%, Zach Werenski100%, Kent Johnson31%, Matthew Knies95%, Adam Fantilli91%
1.9 min/game · 6.39 xGF/60 · P(intact) 95%
- BUF shorthanded time −15%
- BUF PK xGA/60 −5%
- vs Ukko-Pekka Luukkonen .905 (67% to start)
- Luukkonen .867 on rebounds
- Together 2 of last 2
- DK correlation .17
Danton Heinen0.0%, Conor Garland10%, Ryan Lomberg0.1%
4.7 min/game · 1.84 xGF/60 · P(intact) 95%
- BUF 5v5 xGA/60 −2%
- vs Ukko-Pekka Luukkonen .905 (67% to start)
- Luukkonen .867 on rebounds
- Together 2 of last 2
Sam Carrick0.1%, Beck Malenstyn4.1%, Jiri Kulich25%
5.8 min/game · 1.61 xGF/60 · P(intact) 82%
- CBJ 5v5 xGA/60 −4%
- vs Cam Talbot .883 (57% to start)
- Talbot .812 on rebounds
- Together 2 of last 3
- DK correlation .27
Sean Monahan19%, Valeri Nichushkin59%, Dmitri Voronkov19%
5.7 min/game · 1.26 xGF/60 · P(intact) 95%
- BUF 5v5 xGA/60 −2%
- vs Ukko-Pekka Luukkonen .905 (67% to start)
- Luukkonen .867 on rebounds
- Together 2 of last 2
- DK correlation .27
SJS @ MIN
Joel Eriksson Ek81%, Kirill Kaprizov98%, Quinn Hughes100%, Matt Boldy100%, Maxim Shabanov27%
4.6 min/game · 8.82 xGF/60 · P(intact) 82%
- SJS shorthanded time +12%
- SJS PK xGA/60 +2%
- vs Yaroslav Askarov .884 (71% to start)
- Askarov .831 on rebounds
- Together 2 of last 3
- DK correlation .18
Alexander Wennberg24%, Luca Cagnoni64%, Will Smith95%, Macklin Celebrini100%, Igor Chernyshov53%
3.0 min/game · 9.47 xGF/60 · P(intact) 95%
- MIN shorthanded time −1%
- MIN PK xGA/60 +2%
- vs Jesper Wallstedt .918 (69% to start)
- Wallstedt .839 on rebounds
- Together 2 of last 2
- DK correlation .28
Mason Marchment47%, Will Smith95%, Macklin Celebrini100%
12.1 min/game · 2.08 xGF/60 · P(intact) 95%
- MIN 5v5 xGA/60 −3%
- vs Jesper Wallstedt .918 (69% to start)
- Wallstedt .839 on rebounds
- Together 2 of last 2
- DK correlation .30
Nick Foligno3.3%, Marcus Foligno4.1%, Michael McCarron7.3%
8.6 min/game · 2.25 xGF/60 · P(intact) 82%
- SJS 5v5 xGA/60 +5%
- vs Yaroslav Askarov .884 (71% to start)
- Askarov .831 on rebounds
- Together 2 of last 3
- DK correlation .28
Blake Coleman38%, Joel Eriksson Ek81%, Matt Boldy100%
7.7 min/game · 2.48 xGF/60 · P(intact) 82%
- SJS 5v5 xGA/60 +5%
- vs Yaroslav Askarov .884 (71% to start)
- Askarov .831 on rebounds
- Together 2 of last 3
- DK correlation .26
Tyler Toffoli38%, Igor Chernyshov53%, Michael Misa59%
9.0 min/game · 2.11 xGF/60 · P(intact) 95%
- MIN 5v5 xGA/60 −3%
- vs Jesper Wallstedt .918 (69% to start)
- Wallstedt .839 on rebounds
- Together 2 of last 2
- DK correlation .28
Alexander Wennberg24%, Collin Graf28%, Ivar Stenberg82%
10.5 min/game · 1.52 xGF/60 · P(intact) 95%
- MIN 5v5 xGA/60 −3%
- vs Jesper Wallstedt .918 (69% to start)
- Wallstedt .839 on rebounds
- Together 2 of last 2
- DK correlation .28
Ryan Hartman39%, Yakov Trenin22%, Bobby Brink14%
8.0 min/game · 1.68 xGF/60 · P(intact) 82%
- SJS 5v5 xGA/60 +5%
- vs Yaroslav Askarov .884 (71% to start)
- Askarov .831 on rebounds
- Together 2 of last 3
- DK correlation .28
Kirill Kaprizov98%, Danila Yurov32%, Maxim Shabanov27%
7.0 min/game · 1.73 xGF/60 · P(intact) 82%
- SJS 5v5 xGA/60 +5%
- vs Yaroslav Askarov .884 (71% to start)
- Askarov .831 on rebounds
- Together 2 of last 3
- DK correlation .27
Barclay Goodrow0.1%, Kiefer Sherwood59%, Zack Ostapchuk3.2%
7.3 min/game · 1.81 xGF/60 · P(intact) 95%
- MIN 5v5 xGA/60 −3%
- vs Jesper Wallstedt .918 (69% to start)
- Wallstedt .839 on rebounds
- Together 2 of last 2
- DK correlation .27
ANA @ CGY
A.J. Greer7.6%, Cutter Gauthier98%, Leo Carlsson94%
9.7 min/game · 3.08 xGF/60 · P(intact) 82%
- CGY 5v5 xGA/60 +5%
- vs Dustin Wolf .895 (85% to start)
- Wolf .844 on rebounds
- Together 2 of last 3
- DK correlation .27
Alex Killorn5.1%, Mikael Granlund52%, Beckett Sennecke94%
8.7 min/game · 2.77 xGF/60 · P(intact) 95%
- CGY 5v5 xGA/60 +5%
- vs Dustin Wolf .895 (85% to start)
- Wolf .844 on rebounds
- Together 3 of last 3
- DK correlation .28
Mikael Granlund52%, Jackson LaCombe97%, Cutter Gauthier98%, Leo Carlsson94%, Beckett Sennecke94%
3.2 min/game · 5.93 xGF/60 · P(intact) 82%
- CGY shorthanded time +24%
- CGY PK xGA/60 −2%
- vs Dustin Wolf .895 (85% to start)
- Wolf .844 on rebounds
- Together 2 of last 3
- DK correlation .20
Martin Pospisil6.4%, Adam Klapka9.8%, Maxim Tsyplakov13%
7.2 min/game · 1.87 xGF/60 · P(intact) 95%
- ANA 5v5 xGA/60 −1%
- vs Ville Husso .884 (80% to start)
- Husso .889 on cycle shots
- Together 3 of last 3
- DK correlation .27
Morgan Frost23%, Joel Farabee11%, Matt Coronato40%, Zayne Parekh68%, Matvei Gridin40%
1.8 min/game · 6.68 xGF/60 · P(intact) 95%
- ANA shorthanded time −5%
- ANA PK xGA/60 −6%
- vs Ville Husso .884 (80% to start)
- Husso .889 on cycle shots
- Together 3 of last 3
- DK correlation .20
Judd Caulfield, Jeff Malott3.2%, Tim Washe4.8%
7.6 min/game · 1.42 xGF/60 · P(intact) 82%
- CGY 5v5 xGA/60 +5%
- vs Dustin Wolf .895 (85% to start)
- Wolf .844 on rebounds
- Together 2 of last 3
Morgan Frost23%, Matt Coronato40%, Matvei Gridin40%
10.5 min/game · 2.40 xGF/60 · P(intact) 23%
- ANA 5v5 xGA/60 −1%
- vs Ville Husso .884 (80% to start)
- Husso .889 on cycle shots
- Together 2 of last 3
- DK correlation .28
Mikael Backlund14%, Joel Farabee11%, Samuel Honzek14%
8.3 min/game · 2.66 xGF/60 · P(intact) 23%
- ANA 5v5 xGA/60 −1%
- vs Ville Husso .884 (80% to start)
- Husso .889 on cycle shots
- Together 2 of last 3
- DK correlation .27
Ryan Strome4.0%, Yegor Sharangovich7.8%, Connor Zary10%
10.9 min/game · 2.45 xGF/60 · P(intact) 12%
- ANA 5v5 xGA/60 −1%
- vs Ville Husso .884 (80% to start)
- Husso .889 on cycle shots
- Together 1 of last 3
- DK correlation .27
Frank Vatrano6.4%, Ryan Poehling6.7%, Nikita Nesterenko4.0%
7.8 min/game · 2.86 xGF/60 · P(intact) 12%
- CGY 5v5 xGA/60 +5%
- vs Dustin Wolf .895 (85% to start)
- Wolf .844 on rebounds
- Together 1 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.