Fantasy tools
Stack Finder: best line and PP1 stacks for Friday, Mar 13
Data updated:
Top 10 stacks
Zach Hyman96%, Ryan Nugent-Hopkins75%, Leon Draisaitl100%, Connor McDavid100%, Evan Bouchard100%
Matchup factors →Brayden Schenn35%, Bo Horvat91%, Mathew Barzal82%, Calum Ritchie31%, Matthew Schaefer100%
Matchup factors →Anze Kopitar0.4%, Adrian Kempe97%, Artemi Panarin97%
Matchup factors →Robert Thomas89%, Dylan Holloway92%, Jimmy Snuggerud78%
Matchup factors →Trevor Moore8.8%, Quinton Byfield84%, Alex Laferriere67%
Matchup factors →Bo Horvat91%, Mathew Barzal82%, Emil Heineman31%
Matchup factors →Zach Hyman96%, Ryan Nugent-Hopkins75%, Connor McDavid100%
Matchup factors →Jordan Kyrou74%, Jake Neighbours29%, Dalibor Dvorsky29%
Matchup factors →Anders Lee29%, Jean-Gabriel Pageau11%, Simon Holmstrom16%
Matchup factors →Leon Draisaitl100%, Jack Roslovic35%, Vasily Podkolzin86%
Matchup factors →
By game
LAK @ NYI
Brayden Schenn35%, Bo Horvat91%, Mathew Barzal82%, Calum Ritchie31%, Matthew Schaefer100%
5.5 min/game · 9.23 xGF/60 · P(intact) 82%
- LAK shorthanded time +40%
- LAK PK xGA/60 −4%
- Together 2 of last 3
- DK correlation .19
Anze Kopitar0.4%, Adrian Kempe97%, Artemi Panarin97%
12.9 min/game · 3.13 xGF/60 · P(intact) 95%
- NYI 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .28
Trevor Moore8.8%, Quinton Byfield84%, Alex Laferriere67%
13.1 min/game · 2.95 xGF/60 · P(intact) 95%
- NYI 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .28
Bo Horvat91%, Mathew Barzal82%, Emil Heineman31%
12.7 min/game · 3.72 xGF/60 · P(intact) 82%
- LAK 5v5 xGA/60 −13%
- Together 2 of last 3
- DK correlation .27
Anders Lee29%, Jean-Gabriel Pageau11%, Simon Holmstrom16%
9.1 min/game · 2.70 xGF/60 · P(intact) 95%
- LAK 5v5 xGA/60 −13%
- Together 3 of last 3
- DK correlation .30
Scott Laughton4.8%, Alex Turcotte8.8%, Jared Wright0.0%
8.1 min/game · 2.10 xGF/60 · P(intact) 95%
- NYI 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .27
Jeff Malott3.2%, Samuel Helenius3.2%, Taylor Ward0.0%
5.3 min/game · 2.55 xGF/60 · P(intact) 95%
- NYI 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .28
Brayden Schenn35%, Ondrej Palat0.1%, Calum Ritchie31%
8.3 min/game · 1.77 xGF/60 · P(intact) 82%
- LAK 5v5 xGA/60 −13%
- Together 2 of last 3
- DK correlation .28
Anze Kopitar0.4%, Adrian Kempe97%, Artemi Panarin97%, Alex Laferriere67%, Brandt Clarke95%
3.5 min/game · 8.49 xGF/60 · P(intact) 23%
- NYI shorthanded time +40%
- NYI PK xGA/60 +23%
- Together 2 of last 3
- DK correlation .19
Casey Cizikas3.3%, Anthony Duclair3.2%, Marc Gatcomb7.3%
6.6 min/game · 1.91 xGF/60 · P(intact) 12%
- LAK 5v5 xGA/60 −13%
- Together 1 of last 3
- DK correlation .29
EDM @ STL
Zach Hyman96%, Ryan Nugent-Hopkins75%, Leon Draisaitl100%, Connor McDavid100%, Evan Bouchard100%
6.0 min/game · 10.21 xGF/60 · P(intact) 95%
- STL shorthanded time +40%
- STL PK xGA/60 +16%
- Together 3 of last 3
- DK correlation .20
Robert Thomas89%, Dylan Holloway92%, Jimmy Snuggerud78%
10.9 min/game · 3.74 xGF/60 · P(intact) 95%
- EDM 5v5 xGA/60 −1%
- Together 3 of last 3
- DK correlation .28
Zach Hyman96%, Ryan Nugent-Hopkins75%, Connor McDavid100%
8.9 min/game · 3.86 xGF/60 · P(intact) 95%
- STL 5v5 xGA/60 −8%
- Together 3 of last 3
- DK correlation .28
Jordan Kyrou74%, Jake Neighbours29%, Dalibor Dvorsky29%
9.2 min/game · 2.65 xGF/60 · P(intact) 95%
- EDM 5v5 xGA/60 −1%
- Together 3 of last 3
- DK correlation .28
Leon Draisaitl100%, Jack Roslovic35%, Vasily Podkolzin86%
7.6 min/game · 2.95 xGF/60 · P(intact) 95%
- STL 5v5 xGA/60 −8%
- Together 3 of last 3
- DK correlation .27
Pavel Buchnevich33%, Pius Suter4.1%, Otto Stenberg14%
10.9 min/game · 1.72 xGF/60 · P(intact) 68%
- EDM 5v5 xGA/60 −1%
- Together 1 of last 3
- DK correlation .27
Adam Henrique0.4%, Trent Frederic4.9%, Josh Samanski3.2%
7.5 min/game · 2.41 xGF/60 · P(intact) 68%
- STL 5v5 xGA/60 −8%
- Together 1 of last 3
- DK correlation .27
Nathan Walker0.1%, Alexey Toropchenko0.1%, Jack Finley0.0%
8.4 min/game · 1.73 xGF/60 · P(intact) 68%
- EDM 5v5 xGA/60 −1%
- Together 1 of last 3
- DK correlation .27
Jason Dickinson3.2%, Kasperi Kapanen18%, Matt Savoie36%
6.0 min/game · 1.80 xGF/60 · P(intact) 95%
- STL 5v5 xGA/60 −8%
- Together 3 of last 3
- DK correlation .28
Jonathan Drouin0.4%, Robert Thomas89%, Philip Broberg63%, Dylan Holloway92%, Jimmy Snuggerud78%
3.7 min/game · 7.82 xGF/60 · P(intact) 23%
- EDM shorthanded time +40%
- EDM PK xGA/60 +17%
- Together 2 of last 3
- DK correlation .18
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.