Fantasy tools
Stack Finder: best line and PP1 stacks for Friday, Mar 20
Data updated:
Top 10 stacks
Sebastian Aho99%, Andrei Svechnikov97%, Seth Jarvis94%
Matchup factors →John Tavares96%, William Nylander99%, Matias Maccelli12%
Matchup factors →Taylor Hall29%, Logan Stankoven72%, Jackson Blake71%
Matchup factors →Nazem Kadri71%, Brock Nelson88%, Nicolas Roy14%
Matchup factors →Timo Meier80%, Nico Hischier96%, Dawson Mercer23%
Matchup factors →Nathan MacKinnon100%, Valeri Nichushkin56%, Martin Necas100%
Matchup factors →Nazem Kadri71%, Brock Nelson88%, Nathan MacKinnon100%, Martin Necas100%, Cale Makar100%
Matchup factors →Nick Schmaltz96%, Lawson Crouse34%, Clayton Keller100%
Matchup factors →Connor Brown5.8%, Jesper Bratt95%, Jack Hughes99%
Matchup factors →Carter Verhaeghe70%, Sam Bennett83%, Matthew Tkachuk99%
Matchup factors →
By game
CAR @ TOR
Sebastian Aho99%, Andrei Svechnikov97%, Seth Jarvis94%
11.2 min/game · 3.42 xGF/60 · P(intact) 95%
- TOR 5v5 xGA/60 +3%
- Together 3 of last 3
- DK correlation .27
John Tavares96%, William Nylander99%, Matias Maccelli12%
12.3 min/game · 3.38 xGF/60 · P(intact) 95%
- CAR 5v5 xGA/60 −6%
- Together 3 of last 3
- DK correlation .27
Taylor Hall29%, Logan Stankoven72%, Jackson Blake71%
9.7 min/game · 3.87 xGF/60 · P(intact) 95%
- TOR 5v5 xGA/60 +3%
- Together 3 of last 3
- DK correlation .29
Jordan Staal19%, Jordan Martinook4.3%, Nikolaj Ehlers91%
9.1 min/game · 2.83 xGF/60 · P(intact) 95%
- TOR 5v5 xGA/60 +3%
- Together 3 of last 3
- DK correlation .27
Mark Jankowski0.3%, William Carrier3.3%, Eric Robinson0.1%
6.1 min/game · 2.75 xGF/60 · P(intact) 82%
- TOR 5v5 xGA/60 +3%
- Together 2 of last 3
- DK correlation .28
Dakota Joshua4.0%, Bo Groulx, Nick Robertson33%
7.7 min/game · 1.96 xGF/60 · P(intact) 95%
- CAR 5v5 xGA/60 −6%
- Together 3 of last 3
- DK correlation .27
Max Domi4.9%, Matthew Knies95%, Easton Cowan55%
8.9 min/game · 1.37 xGF/60 · P(intact) 95%
- CAR 5v5 xGA/60 −6%
- Together 3 of last 3
- DK correlation .27
John Tavares96%, Oliver Ekman-Larsson39%, William Nylander99%, Matias Maccelli12%, Matthew Knies95%
1.9 min/game · 5.90 xGF/60 · P(intact) 82%
- CAR shorthanded time −4%
- CAR PK xGA/60 −10%
- Together 2 of last 3
- DK correlation .20
Taylor Hall29%, Nikolaj Ehlers91%, K'Andre Miller61%, Logan Stankoven72%, Jackson Blake71%
0.9 min/game · 8.83 xGF/60 · P(intact) 95%
- TOR shorthanded time −1%
- TOR PK xGA/60 +11%
- Together 3 of last 3
- DK correlation .20
Calle Jarnkrok0.0%, Steven Lorentz0.0%, Jacob Quillan0.0%
5.8 min/game · 1.77 xGF/60 · P(intact) 82%
- CAR 5v5 xGA/60 −6%
- Together 2 of last 3
- DK correlation .27
NJD @ WSH
Timo Meier80%, Nico Hischier96%, Dawson Mercer23%
12.1 min/game · 2.86 xGF/60 · P(intact) 95%
- WSH 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .26
Connor Brown5.8%, Jesper Bratt95%, Jack Hughes99%
9.7 min/game · 3.24 xGF/60 · P(intact) 95%
- WSH 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .28
Cody Glass4.0%, Arseny Gritsyuk21%, Lenni Hameenaho8.0%
9.9 min/game · 2.14 xGF/60 · P(intact) 95%
- WSH 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .28
Dougie Hamilton83%, Connor Brown5.8%, Jesper Bratt95%, Nico Hischier96%, Jack Hughes99%
2.1 min/game · 9.14 xGF/60 · P(intact) 95%
- WSH shorthanded time −3%
- WSH PK xGA/60 +7%
- Together 3 of last 3
- DK correlation .20
Nick Bjugstad0.1%, Paul Cotter58%, Maxim Tsyplakov12%
7.0 min/game · 2.26 xGF/60 · P(intact) 95%
- WSH 5v5 xGA/60 +2%
- Together 3 of last 3
- DK correlation .27
Dylan Strome82%, Aliaksei Protas57%, Ryan Leonard66%
8.8 min/game · 3.12 xGF/60 · P(intact) 23%
- NJD 5v5 xGA/60 −4%
- Together 2 of last 3
- DK correlation .28
Tom Wilson94%, Pierre-Luc Dubois47%, Hendrix Lapierre14%
11.0 min/game · 2.02 xGF/60 · P(intact) 23%
- NJD 5v5 xGA/60 −4%
- Together 2 of last 3
- DK correlation .28
Alex Ovechkin95%, Tom Wilson94%, Dylan Strome82%, Jakob Chychrun99%, Pierre-Luc Dubois47%
3.5 min/game · 6.37 xGF/60 · P(intact) 23%
- NJD shorthanded time −2%
- NJD PK xGA/60 −5%
- Together 2 of last 3
- DK correlation .17
Brandon Duhaime0.2%, Connor McMichael34%, Ethen Frank3.2%
6.3 min/game · 2.49 xGF/60 · P(intact) 23%
- NJD 5v5 xGA/60 −4%
- Together 2 of last 3
- DK correlation .28
Alex Ovechkin95%, Anthony Beauvillier4.9%, Justin Sourdif16%
6.1 min/game · 2.47 xGF/60 · P(intact) 23%
- NJD 5v5 xGA/60 −4%
- Together 2 of last 3
- DK correlation .26
COL @ CHI
Nazem Kadri71%, Brock Nelson88%, Nicolas Roy14%
7.5 min/game · 4.54 xGF/60 · P(intact) 95%
- CHI 5v5 xGA/60 +13%
- Together 3 of last 3
- DK correlation .28
Nathan MacKinnon100%, Valeri Nichushkin56%, Martin Necas100%
10.4 min/game · 2.97 xGF/60 · P(intact) 95%
- CHI 5v5 xGA/60 +13%
- Together 3 of last 3
- DK correlation .28
Nazem Kadri71%, Brock Nelson88%, Nathan MacKinnon100%, Martin Necas100%, Cale Makar100%
3.9 min/game · 8.73 xGF/60 · P(intact) 95%
- CHI shorthanded time +1%
- CHI PK xGA/60 −1%
- Together 3 of last 3
- DK correlation .21
Parker Kelly19%, Jack Drury4.8%, Joel Kiviranta0.1%
8.8 min/game · 3.02 xGF/60 · P(intact) 95%
- CHI 5v5 xGA/60 +13%
- Together 3 of last 3
- DK correlation .28
Andre Burakovsky5.6%, Ryan Greene9.6%, Connor Bedard97%
10.4 min/game · 2.90 xGF/60 · P(intact) 95%
- COL 5v5 xGA/60 −8%
- Together 3 of last 3
- DK correlation .27
Teuvo Teravainen11%, Tyler Bertuzzi56%, Frank Nazar50%
10.6 min/game · 2.56 xGF/60 · P(intact) 95%
- COL 5v5 xGA/60 −8%
- Together 3 of last 3
- DK correlation .26
Teuvo Teravainen11%, Tyler Bertuzzi56%, Frank Nazar50%, Connor Bedard97%, Artyom Levshunov43%
2.8 min/game · 7.07 xGF/60 · P(intact) 95%
- COL shorthanded time −7%
- COL PK xGA/60 −8%
- Together 3 of last 3
- DK correlation .20
Ryan Donato9.0%, Andrew Mangiapane3.2%, Ilya Mikheyev7.5%
8.2 min/game · 2.05 xGF/60 · P(intact) 95%
- COL 5v5 xGA/60 −8%
- Together 3 of last 3
- DK correlation .29
Zakhar Bardakov0.0%, Ivan Ivan4.0%, Gavin Brindley14%
2.6 min/game · 1.82 xGF/60 · P(intact) 68%
- CHI 5v5 xGA/60 +13%
- Together 1 of last 3
- DK correlation .29
Sam Lafferty0.0%, Landon Slaggert0.0%, Nick Lardis22%
4.4 min/game · 1.35 xGF/60 · P(intact) 23%
- COL 5v5 xGA/60 −8%
- Together 2 of last 3
- DK correlation .31
FLA @ CGY
Carter Verhaeghe70%, Sam Bennett83%, Matthew Tkachuk99%
11.9 min/game · 2.98 xGF/60 · P(intact) 82%
- CGY 5v5 xGA/60 +2%
- Together 2 of last 3
- DK correlation .28
Mikael Backlund13%, Blake Coleman38%, Joel Farabee11%
11.1 min/game · 2.56 xGF/60 · P(intact) 95%
- FLA 5v5 xGA/60 −8%
- Together 3 of last 3
- DK correlation .27
Morgan Frost21%, Matt Coronato38%, Matvei Gridin37%
9.8 min/game · 2.04 xGF/60 · P(intact) 95%
- FLA 5v5 xGA/60 −8%
- Together 3 of last 3
- DK correlation .28
Blake Coleman38%, Morgan Frost21%, Matt Coronato38%, Zayne Parekh66%, Matvei Gridin37%
2.5 min/game · 7.01 xGF/60 · P(intact) 95%
- FLA shorthanded time +16%
- FLA PK xGA/60 −12%
- Together 3 of last 3
- DK correlation .19
Ryan Strome4.0%, Victor Olofsson27%, Yegor Sharangovich6.8%
10.9 min/game · 2.15 xGF/60 · P(intact) 23%
- FLA 5v5 xGA/60 −8%
- Together 2 of last 3
- DK correlation .28
Jesper Boqvist0.1%, Anton Lundell44%, Mackie Samoskevich
12.2 min/game · 2.84 xGF/60 · P(intact) 12%
- CGY 5v5 xGA/60 +2%
- Together 1 of last 3
- DK correlation .28
Tomas Nosek, A.J. Greer7.5%, Cole Reinhardt0.1%
8.1 min/game · 2.17 xGF/60 · P(intact) 23%
- CGY 5v5 xGA/60 +2%
- Together 2 of last 3
- DK correlation .28
Carter Verhaeghe70%, Aaron Ekblad46%, Matthew Tkachuk99%, Anton Lundell44%, Mackie Samoskevich
4.1 min/game · 7.23 xGF/60 · P(intact) 12%
- CGY shorthanded time +3%
- CGY PK xGA/60 +4%
- Together 1 of last 3
- DK correlation .19
Vinnie Hinostroza0.0%, Luke Kunin0.0%, Nolan Foote0.0%
7.8 min/game · 2.99 xGF/60 · P(intact) 12%
- CGY 5v5 xGA/60 +2%
- Together 1 of last 3
- DK correlation .27
Ryan Lomberg0.1%, Martin Pospisil7.2%, Connor Zary10%
7.2 min/game · 1.58 xGF/60 · P(intact) 12%
- FLA 5v5 xGA/60 −8%
- Together 1 of last 3
- DK correlation .28
ANA @ UTA
Nick Schmaltz96%, Lawson Crouse34%, Clayton Keller100%
9.5 min/game · 3.11 xGF/60 · P(intact) 95%
- ANA 5v5 xGA/60 +9%
- Together 3 of last 3
- DK correlation .29
Chris Kreider63%, Troy Terry46%, Leo Carlsson95%
9.9 min/game · 3.43 xGF/60 · P(intact) 82%
- UTA 5v5 xGA/60 −5%
- Together 2 of last 3
- DK correlation .28
Nick Schmaltz96%, Clayton Keller100%, Mikhail Sergachev99%, Barrett Hayton10%, Dylan Guenther100%
2.7 min/game · 9.41 xGF/60 · P(intact) 95%
- ANA shorthanded time −1%
- ANA PK xGA/60 +6%
- Together 3 of last 3
- DK correlation .20
Michael Carcone6.6%, Barrett Hayton10%, Jack McBain17%
8.1 min/game · 2.95 xGF/60 · P(intact) 95%
- ANA 5v5 xGA/60 +9%
- Together 3 of last 3
- DK correlation .28
JJ Peterka77%, Dylan Guenther100%, Logan Cooley94%
9.0 min/game · 2.64 xGF/60 · P(intact) 95%
- ANA 5v5 xGA/60 +9%
- Together 3 of last 3
- DK correlation .28
Alex Killorn5.1%, Mikael Granlund54%, Beckett Sennecke95%
10.4 min/game · 3.03 xGF/60 · P(intact) 82%
- UTA 5v5 xGA/60 −5%
- Together 2 of last 3
- DK correlation .28
Jeffrey Viel3.2%, Ryan Poehling6.7%, Cutter Gauthier98%
9.7 min/game · 3.14 xGF/60 · P(intact) 82%
- UTA 5v5 xGA/60 −5%
- Together 2 of last 3
- DK correlation .27
Alex Killorn5.1%, Jacob Trouba73%, Jackson LaCombe98%, Cutter Gauthier98%, Beckett Sennecke95%
3.0 min/game · 6.56 xGF/60 · P(intact) 68%
- UTA shorthanded time −1%
- UTA PK xGA/60 +7%
- Together 1 of last 3
- DK correlation .14
Alexander Kerfoot3.2%, Kevin Stenlund0.1%, Kailer Yamamoto0.2%
7.1 min/game · 2.12 xGF/60 · P(intact) 82%
- ANA 5v5 xGA/60 +9%
- Together 2 of last 3
- DK correlation .27
Frank Vatrano5.6%, Jansen Harkins0.0%, Tim Washe4.9%
4.0 min/game · 1.58 xGF/60 · P(intact) 95%
- UTA 5v5 xGA/60 −5%
- Together 3 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.