Fantasy tools
Stack Finder: best line and PP1 stacks for Sunday, Apr 19
Data updated:
Top 10 stacks
Mark Stone93%, Tomas Hertl85%, Jack Eichel99%, Mitch Marner99%, Pavel Dorofeyev93%
Matchup factors →Nikita Kucherov100%, Nick Paul7.3%, Brandon Hagel99%
Matchup factors →Anze Kopitar0.4%, Adrian Kempe97%, Artemi Panarin98%
Matchup factors →Trevor Moore8.9%, Quinton Byfield85%, Alex Laferriere67%
Matchup factors →Elias Lindholm43%, David Pastrnak100%, Morgan Geekie95%
Matchup factors →Nick Suzuki100%, Cole Caufield100%, Juraj Slafkovský98%
Matchup factors →Nick Schmaltz96%, Lawson Crouse34%, Clayton Keller100%
Matchup factors →Josh Norris37%, Josh Doan59%, Zach Benson55%
Matchup factors →Alexander Kerfoot3.2%, Michael Carcone6.6%, JJ Peterka77%
Matchup factors →Ivan Barbashev66%, Brett Howden15%, Pavel Dorofeyev93%
Matchup factors →
By game
BOS @ BUF
Elias Lindholm43%, David Pastrnak100%, Morgan Geekie95%
13.0 min/game · 2.84 xGF/60 · P(intact) 95%
- BUF 5v5 xGA/60 +6%
- Together 3 of last 3
- DK correlation .28
Josh Norris37%, Josh Doan59%, Zach Benson55%
9.7 min/game · 3.21 xGF/60 · P(intact) 95%
- BOS 5v5 xGA/60 +3%
- Together 3 of last 3
- DK correlation .27
Marat Khusnutdinov11%, Fraser Minten46%, James Hagens50%
11.9 min/game · 2.33 xGF/60 · P(intact) 82%
- BUF 5v5 xGA/60 +6%
- Together 2 of last 3
- DK correlation .27
Sean Kuraly0.2%, Tanner Jeannot6.5%, Mark Kastelic17%
10.1 min/game · 2.22 xGF/60 · P(intact) 82%
- BUF 5v5 xGA/60 +6%
- Together 2 of last 3
- DK correlation .29
Alex Tuch94%, Josh Norris37%, Jack Quinn51%, Josh Doan59%, Zach Benson55%
2.8 min/game · 9.46 xGF/60 · P(intact) 68%
- BOS shorthanded time +11%
- BOS PK xGA/60 +1%
- Together 1 of last 3
- DK correlation .28
Alex Tuch94%, Tage Thompson100%, Peyton Krebs17%
12.6 min/game · 2.85 xGF/60 · P(intact) 23%
- BOS 5v5 xGA/60 +3%
- Together 2 of last 3
- DK correlation .26
Jason Zucker22%, Ryan McLeod24%, Jack Quinn51%
9.9 min/game · 3.29 xGF/60 · P(intact) 23%
- BOS 5v5 xGA/60 +3%
- Together 2 of last 3
- DK correlation .28
Viktor Arvidsson34%, Pavel Zacha74%, Casey Mittelstadt12%
13.4 min/game · 2.56 xGF/60 · P(intact) 12%
- BUF 5v5 xGA/60 +6%
- Together 1 of last 3
- DK correlation .26
Jordan Greenway0.3%, Beck Malenstyn4.1%, Tyson Kozak3.2%
6.8 min/game · 1.50 xGF/60 · P(intact) 23%
- BOS 5v5 xGA/60 +3%
- Together 2 of last 3
- DK correlation .27
MTL @ TBL
Nikita Kucherov100%, Nick Paul7.3%, Brandon Hagel99%
12.9 min/game · 4.23 xGF/60 · P(intact) 68%
- MTL 5v5 xGA/60 +12%
- Together 1 of last 3
- DK correlation .29
Nick Suzuki100%, Cole Caufield100%, Juraj Slafkovský98%
11.5 min/game · 3.16 xGF/60 · P(intact) 95%
- TBL 5v5 xGA/60 −1%
- Together 3 of last 3
- DK correlation .28
Nick Suzuki100%, Cole Caufield100%, Lane Hutson99%, Juraj Slafkovský98%, Ivan Demidov91%
2.8 min/game · 9.04 xGF/60 · P(intact) 95%
- TBL shorthanded time +1%
- TBL PK xGA/60 −4%
- Together 3 of last 3
- DK correlation .20
Alexandre Texier4.9%, Alex Newhook25%, Ivan Demidov91%
8.5 min/game · 2.44 xGF/60 · P(intact) 82%
- TBL 5v5 xGA/60 −1%
- Together 2 of last 3
- DK correlation .27
Kirby Dach11%, Zachary Bolduc23%, Oliver Kapanen28%
7.6 min/game · 2.34 xGF/60 · P(intact) 82%
- TBL 5v5 xGA/60 −1%
- Together 2 of last 3
- DK correlation .27
Nikita Kucherov100%, Oliver Bjorkstrand17%, Nick Paul7.3%, Darren Raddysh98%, Brandon Hagel99%
2.0 min/game · 6.01 xGF/60 · P(intact) 68%
- MTL shorthanded time +7%
- MTL PK xGA/60 +11%
- Together 1 of last 3
- DK correlation .17
Yanni Gourde4.9%, Jakob Pelletier7.2%, Gage Goncalves10%
3.0 min/game · 2.61 xGF/60 · P(intact) 68%
- MTL 5v5 xGA/60 +12%
- Together 1 of last 3
- DK correlation .28
Corey Perry6.6%, Jake Guentzel100%, Brayden Point95%
10.6 min/game · 3.24 xGF/60 · P(intact) 12%
- MTL 5v5 xGA/60 +12%
- Together 1 of last 3
- DK correlation .27
Phillip Danault4.9%, Josh Anderson11%, Jake Evans4.9%
10.1 min/game · 2.08 xGF/60 · P(intact) 12%
- TBL 5v5 xGA/60 −1%
- Together 1 of last 3
- DK correlation .28
Scott Sabourin0.0%, Oliver Bjorkstrand17%, Conor Geekie20%
7.8 min/game · 2.31 xGF/60 · P(intact) 12%
- MTL 5v5 xGA/60 +12%
- Together 1 of last 3
LAK @ COL
Anze Kopitar0.4%, Adrian Kempe97%, Artemi Panarin98%
13.1 min/game · 3.26 xGF/60 · P(intact) 95%
- COL 5v5 xGA/60 −3%
- Together 3 of last 3
- DK correlation .28
Trevor Moore8.9%, Quinton Byfield85%, Alex Laferriere67%
13.3 min/game · 3.17 xGF/60 · P(intact) 95%
- COL 5v5 xGA/60 −3%
- Together 3 of last 3
- DK correlation .28
Joel Armia3.3%, Scott Laughton4.8%, Jared Wright0.0%
9.9 min/game · 2.66 xGF/60 · P(intact) 95%
- COL 5v5 xGA/60 −3%
- Together 3 of last 3
- DK correlation .28
Jack Drury4.8%, Logan O'Connor0.2%, Joel Kiviranta0.1%
7.4 min/game · 2.91 xGF/60 · P(intact) 68%
- LAK 5v5 xGA/60 −9%
- Together 1 of last 3
- DK correlation .27
Alex Barré-Boulet0.0%, Zakhar Bardakov0.0%, Jason Polin0.0%
8.6 min/game · 2.38 xGF/60 · P(intact) 68%
- LAK 5v5 xGA/60 −9%
- Together 1 of last 3
Anze Kopitar0.4%, Adrian Kempe97%, Artemi Panarin98%, Alex Laferriere67%, Brandt Clarke95%
1.8 min/game · 7.28 xGF/60 · P(intact) 95%
- COL shorthanded time −4%
- COL PK xGA/60 −6%
- Together 3 of last 3
- DK correlation .19
Mathieu Joseph3.2%, Samuel Helenius3.2%, Taylor Ward0.0%
5.0 min/game · 1.53 xGF/60 · P(intact) 95%
- COL 5v5 xGA/60 −3%
- Together 3 of last 3
- DK correlation .29
Gabriel Landeskog51%, Nathan MacKinnon100%, Martin Necas100%
13.9 min/game · 3.83 xGF/60 · P(intact) 12%
- LAK 5v5 xGA/60 −9%
- Together 1 of last 3
- DK correlation .28
Brock Nelson88%, Valeri Nichushkin56%, Ross Colton7.3%
10.7 min/game · 2.79 xGF/60 · P(intact) 12%
- LAK 5v5 xGA/60 −9%
- Together 1 of last 3
- DK correlation .26
Gabriel Landeskog51%, Nathan MacKinnon100%, Devon Toews66%, Nicolas Roy14%, Martin Necas100%
3.4 min/game · 7.26 xGF/60 · P(intact) 12%
- LAK shorthanded time −7%
- LAK PK xGA/60 −10%
- Together 1 of last 3
- DK correlation .18
UTA @ VGK
Mark Stone93%, Tomas Hertl85%, Jack Eichel99%, Mitch Marner99%, Pavel Dorofeyev93%
4.2 min/game · 10.98 xGF/60 · P(intact) 95%
- UTA shorthanded time +2%
- UTA PK xGA/60 +9%
- Together 3 of last 3
- DK correlation .28
Nick Schmaltz96%, Lawson Crouse34%, Clayton Keller100%
11.4 min/game · 3.01 xGF/60 · P(intact) 95%
- VGK 5v5 xGA/60 −6%
- Together 3 of last 3
- DK correlation .29
Alexander Kerfoot3.2%, Michael Carcone6.6%, JJ Peterka77%
10.2 min/game · 2.80 xGF/60 · P(intact) 95%
- VGK 5v5 xGA/60 −6%
- Together 3 of last 3
- DK correlation .28
Ivan Barbashev66%, Brett Howden15%, Pavel Dorofeyev93%
8.4 min/game · 3.59 xGF/60 · P(intact) 82%
- UTA 5v5 xGA/60 −1%
- Together 2 of last 3
- DK correlation .28
Mark Stone93%, Jack Eichel99%, Mitch Marner99%
7.1 min/game · 3.00 xGF/60 · P(intact) 82%
- UTA 5v5 xGA/60 −1%
- Together 2 of last 3
- DK correlation .28
Liam O'Brien0.1%, Kevin Stenlund0.1%, Brandon Tanev0.1%
8.0 min/game · 2.31 xGF/60 · P(intact) 95%
- VGK 5v5 xGA/60 −6%
- Together 3 of last 3
- DK correlation .28
Reilly Smith, Tomas Hertl85%, Colton Sissons3.3%
5.2 min/game · 3.07 xGF/60 · P(intact) 82%
- UTA 5v5 xGA/60 −1%
- Together 2 of last 3
- DK correlation .27
Nic Dowd3.2%, Keegan Kolesar5.1%, Cole Smith0.1%
5.8 min/game · 2.31 xGF/60 · P(intact) 95%
- UTA 5v5 xGA/60 −1%
- Together 3 of last 3
- DK correlation .28
Nick Schmaltz96%, Clayton Keller100%, Mikhail Sergachev98%, Dylan Guenther100%, Logan Cooley94%
4.3 min/game · 9.06 xGF/60 · P(intact) 23%
- VGK shorthanded time −7%
- VGK PK xGA/60 −6%
- Together 2 of last 3
- DK correlation .21
Kailer Yamamoto0.2%, Dylan Guenther100%, Logan Cooley94%
7.9 min/game · 3.48 xGF/60 · P(intact) 23%
- VGK 5v5 xGA/60 −6%
- 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.