Fantasy tools
Stack Finder: best line and PP1 stacks for Monday, May 4
Data updated:
Top 10 stacks
Shayne Gostisbehere95%, Nikolaj Ehlers92%, Sebastian Aho99%, Andrei Svechnikov97%, Seth Jarvis94%
Matchup factors →Alex Killorn5.1%, Mikael Granlund52%, Beckett Sennecke95%
Matchup factors →Ivan Barbashev66%, Jack Eichel99%, Pavel Dorofeyev92%
Matchup factors →Taylor Hall25%, Logan Stankoven65%, Jackson Blake69%
Matchup factors →Alex Killorn5.1%, Jackson LaCombe97%, Mason McTavish51%, Cutter Gauthier98%, Beckett Sennecke95%
Matchup factors →Sebastian Aho99%, Andrei Svechnikov97%, Seth Jarvis94%
Matchup factors →Nic Dowd3.2%, Colton Sissons3.3%, Cole Smith0.1%
Matchup factors →Noah Cates15%, Trevor Zegras83%, Jamie Drysdale52%, Tyson Foerster38%, Porter Martone87%
Matchup factors →Jordan Staal19%, Jordan Martinook4.3%, Nikolaj Ehlers92%
Matchup factors →Christian Dvorak15%, Travis Konecny91%, Denver Barkey14%
Matchup factors →
By game
PHI @ CAR
Shayne Gostisbehere95%, Nikolaj Ehlers92%, Sebastian Aho99%, Andrei Svechnikov97%, Seth Jarvis94%
5.4 min/game · 7.74 xGF/60 · P(intact) 82%
- PHI shorthanded time +40%
- PHI PK xGA/60 +8%
- Together 2 of last 3
- DK correlation .20
Taylor Hall25%, Logan Stankoven65%, Jackson Blake69%
8.6 min/game · 3.84 xGF/60 · P(intact) 95%
- PHI 5v5 xGA/60 −10%
- Together 3 of last 3
- DK correlation .29
Sebastian Aho99%, Andrei Svechnikov97%, Seth Jarvis94%
8.5 min/game · 3.39 xGF/60 · P(intact) 95%
- PHI 5v5 xGA/60 −10%
- Together 3 of last 3
- DK correlation .27
Noah Cates15%, Trevor Zegras83%, Jamie Drysdale52%, Tyson Foerster38%, Porter Martone87%
2.8 min/game · 7.87 xGF/60 · P(intact) 95%
- CAR shorthanded time +40%
- CAR PK xGA/60 −3%
- Together 3 of last 3
- DK correlation .18
Jordan Staal19%, Jordan Martinook4.3%, Nikolaj Ehlers92%
7.1 min/game · 2.79 xGF/60 · P(intact) 82%
- PHI 5v5 xGA/60 −10%
- Together 2 of last 3
- DK correlation .27
Christian Dvorak15%, Travis Konecny91%, Denver Barkey14%
8.4 min/game · 1.95 xGF/60 · P(intact) 82%
- CAR 5v5 xGA/60 −5%
- Together 2 of last 3
- DK correlation .26
Sean Couturier8.3%, Luke Glendening0.0%, Garnet Hathaway4.0%
6.8 min/game · 2.28 xGF/60 · P(intact) 82%
- CAR 5v5 xGA/60 −5%
- Together 2 of last 3
- DK correlation .28
Mark Jankowski0.2%, William Carrier3.3%, Eric Robinson0.1%
5.4 min/game · 2.70 xGF/60 · P(intact) 82%
- PHI 5v5 xGA/60 −10%
- Together 2 of last 3
- DK correlation .28
Noah Cates15%, Alex Bump15%, Matvei Michkov83%
10.8 min/game · 2.81 xGF/60 · P(intact) 12%
- CAR 5v5 xGA/60 −5%
- Together 1 of last 3
- DK correlation .27
Owen Tippett77%, Trevor Zegras83%, Porter Martone87%
13.9 min/game · 1.83 xGF/60 · P(intact) 12%
- CAR 5v5 xGA/60 −5%
- Together 1 of last 3
- DK correlation .28
ANA @ VGK
Alex Killorn5.1%, Mikael Granlund52%, Beckett Sennecke95%
10.8 min/game · 3.07 xGF/60 · P(intact) 95%
- VGK 5v5 xGA/60 −10%
- Together 3 of last 3
- DK correlation .28
Ivan Barbashev66%, Jack Eichel99%, Pavel Dorofeyev92%
9.7 min/game · 2.81 xGF/60 · P(intact) 95%
- ANA 5v5 xGA/60 +10%
- Together 3 of last 3
- DK correlation .27
Alex Killorn5.1%, Jackson LaCombe97%, Mason McTavish51%, Cutter Gauthier98%, Beckett Sennecke95%
4.4 min/game · 7.70 xGF/60 · P(intact) 82%
- VGK shorthanded time +40%
- VGK PK xGA/60 −1%
- Together 2 of last 3
- DK correlation .20
Nic Dowd3.2%, Colton Sissons3.3%, Cole Smith0.1%
11.9 min/game · 1.75 xGF/60 · P(intact) 95%
- ANA 5v5 xGA/60 +10%
- Together 3 of last 3
- DK correlation .28
Reilly Smith, Mark Stone93%, Mitch Marner99%
11.4 min/game · 2.76 xGF/60 · P(intact) 23%
- ANA 5v5 xGA/60 +10%
- Together 2 of last 3
- DK correlation .27
Mark Stone93%, Tomas Hertl84%, Shea Theodore92%, Jack Eichel99%, Pavel Dorofeyev92%
4.6 min/game · 10.60 xGF/60 · P(intact) 12%
- ANA shorthanded time +40%
- ANA PK xGA/60 +15%
- Together 1 of last 3
- DK correlation .17
Troy Terry46%, Cutter Gauthier98%, Leo Carlsson95%
9.2 min/game · 2.59 xGF/60 · P(intact) 23%
- VGK 5v5 xGA/60 −10%
- Together 2 of last 3
- DK correlation .28
Chris Kreider63%, Ryan Poehling6.7%, Mason McTavish51%
7.6 min/game · 2.80 xGF/60 · P(intact) 23%
- VGK 5v5 xGA/60 −10%
- Together 2 of last 3
- DK correlation .28
Tomas Hertl84%, Keegan Kolesar5.1%, Brett Howden15%
2.7 min/game · 2.61 xGF/60 · P(intact) 23%
- ANA 5v5 xGA/60 +10%
- Together 2 of last 3
- DK correlation .27
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.