Fantasy tools
Stack Finder: best line and PP1 stacks for Saturday, May 9
Data updated:
Top 10 stacks
Shayne Gostisbehere95%, Nikolaj Ehlers92%, Sebastian Aho99%, Andrei Svechnikov97%, Seth Jarvis94%
Matchup factors →Nazem Kadri71%, Gabriel Landeskog51%, Nathan MacKinnon100%, Martin Necas100%, Cale Makar100%
Matchup factors →Mats Zuccarello47%, Ryan Hartman36%, Kirill Kaprizov98%
Matchup factors →Taylor Hall25%, Logan Stankoven65%, Jackson Blake69%
Matchup factors →Artturi Lehkonen78%, Nathan MacKinnon100%, Martin Necas100%
Matchup factors →Brock Nelson88%, Gabriel Landeskog51%, Valeri Nichushkin56%
Matchup factors →Mats Zuccarello47%, Ryan Hartman36%, Kirill Kaprizov98%, Quinn Hughes100%, Matt Boldy100%
Matchup factors →Sebastian Aho99%, Andrei Svechnikov97%, Seth Jarvis94%
Matchup factors →Parker Kelly17%, Jack Drury4.8%, Logan O'Connor0.2%
Matchup factors →Marcus Johansson0.6%, Matt Boldy100%, Danila Yurov32%
Matchup factors →
By game
CAR @ PHI
Shayne Gostisbehere95%, Nikolaj Ehlers92%, Sebastian Aho99%, Andrei Svechnikov97%, Seth Jarvis94%
6.6 min/game · 7.64 xGF/60 · P(intact) 95%
- PHI shorthanded time +40%
- PHI PK xGA/60 +8%
- Together 3 of last 3
- DK correlation .20
Taylor Hall25%, Logan Stankoven65%, Jackson Blake69%
9.8 min/game · 3.88 xGF/60 · P(intact) 95%
- PHI 5v5 xGA/60 −10%
- Together 3 of last 3
- DK correlation .29
Sebastian Aho99%, Andrei Svechnikov97%, Seth Jarvis94%
6.6 min/game · 3.39 xGF/60 · P(intact) 95%
- PHI 5v5 xGA/60 −10%
- Together 3 of last 3
- DK correlation .27
Mark Jankowski0.2%, William Carrier3.3%, Eric Robinson0.1%
6.0 min/game · 2.81 xGF/60 · P(intact) 95%
- PHI 5v5 xGA/60 −10%
- Together 3 of last 3
- DK correlation .28
Jordan Staal19%, Jordan Martinook4.3%, Nikolaj Ehlers92%
5.0 min/game · 2.78 xGF/60 · P(intact) 95%
- PHI 5v5 xGA/60 −10%
- Together 3 of last 3
- DK correlation .27
Sean Couturier8.3%, Luke Glendening0.0%, Garnet Hathaway4.0%
6.4 min/game · 2.23 xGF/60 · P(intact) 82%
- CAR 5v5 xGA/60 −5%
- Together 2 of last 3
- DK correlation .28
Christian Dvorak15%, Travis Konecny91%, Trevor Zegras83%
7.9 min/game · 3.67 xGF/60 · P(intact) 23%
- CAR 5v5 xGA/60 −5%
- Together 2 of last 3
- DK correlation .27
Travis Konecny91%, Noah Cates15%, Trevor Zegras83%, Jamie Drysdale52%, Tyson Foerster38%
7.6 min/game · 5.70 xGF/60 · P(intact) 12%
- CAR shorthanded time +40%
- CAR PK xGA/60 −3%
- Together 1 of last 3
- DK correlation .20
Noah Cates15%, Tyson Foerster38%, Matvei Michkov83%
5.7 min/game · 3.14 xGF/60 · P(intact) 23%
- CAR 5v5 xGA/60 −5%
- Together 2 of last 3
- DK correlation .28
Alex Bump15%, Denver Barkey14%, Porter Martone87%
8.5 min/game · 2.57 xGF/60 · P(intact) 12%
- CAR 5v5 xGA/60 −5%
- Together 1 of last 3
COL @ MIN
Nazem Kadri71%, Gabriel Landeskog51%, Nathan MacKinnon100%, Martin Necas100%, Cale Makar100%
3.8 min/game · 9.51 xGF/60 · P(intact) 95%
- MIN shorthanded time +40%
- MIN PK xGA/60 +18%
- Together 3 of last 3
- DK correlation .20
Mats Zuccarello47%, Ryan Hartman36%, Kirill Kaprizov98%
11.0 min/game · 3.44 xGF/60 · P(intact) 95%
- COL 5v5 xGA/60 −7%
- Together 3 of last 3
- DK correlation .27
Artturi Lehkonen78%, Nathan MacKinnon100%, Martin Necas100%
8.2 min/game · 3.84 xGF/60 · P(intact) 95%
- MIN 5v5 xGA/60 −3%
- Together 3 of last 3
- DK correlation .28
Brock Nelson88%, Gabriel Landeskog51%, Valeri Nichushkin56%
9.0 min/game · 3.51 xGF/60 · P(intact) 82%
- MIN 5v5 xGA/60 −3%
- Together 2 of last 3
- DK correlation .27
Mats Zuccarello47%, Ryan Hartman36%, Kirill Kaprizov98%, Quinn Hughes100%, Matt Boldy100%
2.8 min/game · 10.01 xGF/60 · P(intact) 82%
- COL shorthanded time +40%
- COL PK xGA/60 +0%
- Together 2 of last 3
- DK correlation .19
Parker Kelly17%, Jack Drury4.8%, Logan O'Connor0.2%
7.9 min/game · 2.30 xGF/60 · P(intact) 95%
- MIN 5v5 xGA/60 −3%
- Together 3 of last 3
- DK correlation .29
Marcus Johansson0.6%, Matt Boldy100%, Danila Yurov32%
7.5 min/game · 2.53 xGF/60 · P(intact) 82%
- COL 5v5 xGA/60 −7%
- Together 2 of last 3
- DK correlation .27
Vladimir Tarasenko8.5%, Michael McCarron8.2%, Yakov Trenin19%
8.6 min/game · 1.76 xGF/60 · P(intact) 95%
- COL 5v5 xGA/60 −7%
- Together 3 of last 3
- DK correlation .27
Nazem Kadri71%, Nicolas Roy13%, Ross Colton7.3%
7.4 min/game · 2.20 xGF/60 · P(intact) 82%
- MIN 5v5 xGA/60 −3%
- Together 2 of last 3
Nick Foligno3.3%, Marcus Foligno4.1%, Nico Sturm0.1%
7.1 min/game · 2.05 xGF/60 · P(intact) 95%
- COL 5v5 xGA/60 −7%
- Together 3 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.