Fantasy tools
Stack Finder: best line and PP1 stacks for Wednesday, Mar 25
Data updated:
Top 10 stacks
Vincent Trocheck87%, Mika Zibanejad98%, J.T. Miller91%, Adam Fox99%, Alexis Lafrenière85%
Matchup factors →Elias Lindholm43%, David Pastrnak100%, Pavel Zacha73%, Charlie McAvoy95%, Morgan Geekie95%
Matchup factors →Alex Tuch94%, Tage Thompson100%, Peyton Krebs17%
Matchup factors →David Pastrnak100%, Marat Khusnutdinov10%, Fraser Minten45%
Matchup factors →Mika Zibanejad98%, Alexis Lafrenière85%, Gabe Perreault59%
Matchup factors →Jason Zucker22%, Ryan McLeod24%, Jack Quinn51%
Matchup factors →Sam Carrick0.1%, Beck Malenstyn4.1%, Zach Benson55%
Matchup factors →J.T. Miller91%, Conor Sheary0.1%, Tye Kartye8.1%
Matchup factors →Viktor Arvidsson38%, Pavel Zacha73%, Casey Mittelstadt12%
Matchup factors →Josh Norris37%, Josh Doan59%, Noah Ostlund28%
Matchup factors →
By game
BOS @ BUF
Elias Lindholm43%, David Pastrnak100%, Pavel Zacha73%, Charlie McAvoy95%, Morgan Geekie95%
4.0 min/game · 9.12 xGF/60 · P(intact) 95%
- BUF shorthanded time +40%
- BUF PK xGA/60 +7%
- Together 3 of last 3
- DK correlation .19
Alex Tuch94%, Tage Thompson100%, Peyton Krebs17%
11.3 min/game · 2.78 xGF/60 · P(intact) 95%
- BOS 5v5 xGA/60 −1%
- Together 3 of last 3
- DK correlation .26
David Pastrnak100%, Marat Khusnutdinov10%, Fraser Minten45%
9.4 min/game · 3.16 xGF/60 · P(intact) 95%
- BUF 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .28
Jason Zucker22%, Ryan McLeod24%, Jack Quinn51%
8.7 min/game · 3.26 xGF/60 · P(intact) 95%
- BOS 5v5 xGA/60 −1%
- Together 3 of last 3
- DK correlation .28
Sam Carrick0.1%, Beck Malenstyn4.1%, Zach Benson55%
8.0 min/game · 3.48 xGF/60 · P(intact) 95%
- BOS 5v5 xGA/60 −1%
- Together 3 of last 3
- DK correlation .27
Viktor Arvidsson38%, Pavel Zacha73%, Casey Mittelstadt12%
10.2 min/game · 2.52 xGF/60 · P(intact) 95%
- BUF 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .26
Josh Norris37%, Josh Doan59%, Noah Ostlund28%
9.3 min/game · 2.70 xGF/60 · P(intact) 95%
- BOS 5v5 xGA/60 −1%
- Together 3 of last 3
- DK correlation .26
Elias Lindholm43%, Morgan Geekie95%, Lukas Reichel4.8%
8.5 min/game · 2.85 xGF/60 · P(intact) 95%
- BUF 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .27
Jason Zucker22%, Tage Thompson100%, Josh Norris37%, Rasmus Dahlin100%, Josh Doan59%
2.5 min/game · 8.34 xGF/60 · P(intact) 95%
- BOS shorthanded time +40%
- BOS PK xGA/60 +8%
- Together 3 of last 3
- DK correlation .20
Sean Kuraly0.2%, Tanner Jeannot7.3%, Mark Kastelic17%
7.0 min/game · 2.13 xGF/60 · P(intact) 95%
- BUF 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .29
NYR @ TOR
Vincent Trocheck87%, Mika Zibanejad98%, J.T. Miller91%, Adam Fox99%, Alexis Lafrenière85%
5.8 min/game · 6.97 xGF/60 · P(intact) 95%
- TOR shorthanded time +40%
- TOR PK xGA/60 +21%
- Together 3 of last 3
- DK correlation .20
Mika Zibanejad98%, Alexis Lafrenière85%, Gabe Perreault59%
11.3 min/game · 2.47 xGF/60 · P(intact) 95%
- TOR 5v5 xGA/60 +4%
- Together 3 of last 3
- DK correlation .28
J.T. Miller91%, Conor Sheary0.1%, Tye Kartye8.1%
8.4 min/game · 3.08 xGF/60 · P(intact) 95%
- TOR 5v5 xGA/60 +4%
- Together 3 of last 3
- DK correlation .28
Vincent Trocheck87%, Taylor Raddysh0.1%, Will Cuylle63%
9.3 min/game · 2.33 xGF/60 · P(intact) 95%
- TOR 5v5 xGA/60 +4%
- Together 3 of last 3
- DK correlation .28
John Tavares96%, William Nylander99%, Easton Cowan55%
8.9 min/game · 2.83 xGF/60 · P(intact) 82%
- NYR 5v5 xGA/60 +2%
- Together 2 of last 3
- DK correlation .28
Dakota Joshua4.0%, Bo Groulx, Matias Maccelli12%
6.6 min/game · 2.76 xGF/60 · P(intact) 95%
- NYR 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .28
John Tavares96%, Oliver Ekman-Larsson39%, William Nylander99%, Matthew Knies95%, Easton Cowan55%
2.7 min/game · 6.76 xGF/60 · P(intact) 82%
- NYR shorthanded time +40%
- NYR PK xGA/60 +14%
- Together 2 of last 3
- DK correlation .20
Max Domi4.9%, Nick Robertson33%, Matthew Knies95%
6.1 min/game · 2.22 xGF/60 · P(intact) 82%
- NYR 5v5 xGA/60 +2%
- Together 2 of last 3
- DK correlation .27
Jonny Brodzinski0.0%, Adam Edstrom4.1%, Jaroslav Chmelar0.1%
5.4 min/game · 2.80 xGF/60 · P(intact) 23%
- TOR 5v5 xGA/60 +4%
- Together 2 of last 3
- DK correlation .27
Calle Jarnkrok0.0%, Steven Lorentz0.0%, Jacob Quillan0.0%
6.3 min/game · 1.52 xGF/60 · P(intact) 23%
- NYR 5v5 xGA/60 +2%
- 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.