Edgehalla

Category Week Planner · 2026-27

Best faceoffs won schedules, fantasy week 1 (Sep 28-Oct 4)

Faceoffs won. The opponent factor is faceoffs its opponents win per game. Teams are ranked by effective games: games this week, each weighted by how many faceoffs won the opponent allows. NYR leads with 3.98.

Data updated:

Teams ranked for faceoffs won

Early season: teams have played about 2 games, so the opponent factors are mostly 2025-26 numbers. This season's games count for about 20% and the share grows every week.

Teams by effective faceoffs won games, week 1
#TeamGPEffectivePer gameOpponents (factor)
1New York Rangers43.981.00@ BOS (0.98)vs TBL (1.01)@ DET (0.99)vs UTA (1.00)
2Vancouver Canucks43.860.96@ EDM (0.94)vs EDM (0.94)vs CGY (1.02)vs VGK (0.97)
3Florida Panthers33.151.05@ CAR (1.05)@ SJS (1.05)@ ANA (1.06)
4Vegas Golden Knights33.131.04vs CHI (1.07)vs ANA (1.06)@ VAN (1.00)
5Philadelphia Flyers33.091.03vs PIT (1.05)@ NJD (0.99)vs CAR (1.05)
6Calgary Flames33.061.02vs SEA (1.03)@ VAN (1.00)@ SEA (1.03)
7Chicago Blackhawks33.051.02@ VGK (0.97)@ UTA (1.00)@ BUF (1.08)
8Edmonton Oilers33.031.01vs VAN (1.00)@ VAN (1.00)vs SEA (1.03)
9Carolina Hurricanes33.021.01vs FLA (1.04)vs WSH (0.99)@ PHI (0.99)
10Utah Mammoth32.991.00vs CHI (1.07)@ CBJ (1.00)@ NYR (0.93)
11Seattle Kraken32.970.99@ CGY (1.02)@ EDM (0.94)vs CGY (1.02)
12Boston Bruins32.950.98vs NYR (0.93)@ WPG (0.96)@ MIN (1.06)
13Toronto Maple Leafs32.830.94vs MTL (0.96)vs NYI (0.94)vs OTT (0.94)
14Columbus Blue Jackets22.081.04vs BUF (1.08)vs UTA (1.00)
15Los Angeles Kings22.081.04@ COL (1.03)@ SJS (1.05)
16Buffalo Sabres22.061.03@ CBJ (1.00)vs CHI (1.07)
17Washington Capitals22.061.03@ CAR (1.05)@ TBL (1.01)
18San Jose Sharks22.041.02vs FLA (1.04)vs LAK (0.99)
19Dallas Stars22.021.01vs STL (1.02)@ NSH (1.00)
20Colorado Avalanche22.021.01vs LAK (0.99)vs STL (1.02)
21Anaheim Ducks22.011.00@ VGK (0.97)vs FLA (1.04)
22Montreal Canadiens22.011.00@ TOR (0.96)@ PIT (1.05)
23Nashville Predators21.991.00vs MIN (1.06)vs DAL (0.93)
24Minnesota Wild21.980.99@ NSH (1.00)vs BOS (0.98)
25Winnipeg Jets21.970.99vs BOS (0.98)@ DET (0.99)
26St. Louis Blues21.960.98@ DAL (0.93)@ COL (1.03)
27New York Islanders21.950.97@ TOR (0.96)vs NJD (0.99)
28Pittsburgh Penguins21.940.97@ PHI (0.99)vs MTL (0.96)
29New Jersey Devils21.920.96vs PHI (0.99)@ NYI (0.94)
30Tampa Bay Lightning21.920.96@ NYR (0.93)vs WSH (0.99)
31Detroit Red Wings21.890.94vs NYR (0.93)vs WPG (0.96)
32Ottawa Senators10.960.96@ TOR (0.96)

Projected faceoffs won leaders, week 1

Projected totals for the week. Off-night games in green.

Players by projected faceoffs won, week 1
#PlayerGPFOW
1Elias PetterssonVAN · C4333.9
2J.T. MillerNYR · C4433.0
3Jordan StaalCAR · C3232.5
4Mika ZibanejadNYR · C4429.1
5Leon DraisaitlEDM · C3228.4
6Mikael BacklundCGY · C3227.9
7Elias LindholmBOS · C3227.5
8Chandler StephensonSEA · C3227.4
9Vincent TrocheckUTA · C3227.2
10Aatu RatyVAN · C4327.0
11John TavaresTOR · C3226.9
12Auston MatthewsTOR · C3226.3
13Jack EichelVGK · C3325.7
14William KarlssonVGK · C3325.5
15Sean CouturierPHI · C3224.6
16Sebastian AhoCAR · C3223.8
17Aleksander BarkovFLA · C3323.6
18Nico HischierNJD · C2123.6
19Tomas HertlVGK · C3323.2
20Sidney CrosbyPIT · C2122.7
21Christian DvorakPHI · C3222.6
22Anton LundellFLA · C3322.5
23Morgan FrostCGY · C3222.3
24Kevin StenlundUTA · C3221.5
25Jason DickinsonEDM · C3221.2
26Dylan LarkinDET · C2219.9
27Joel Eriksson EkMIN · C2119.5
28Pavel ZachaBOS · C3219.4
29Matthew BeniersSEA · C3219.3
30Nic DowdVGK · C3319.0
31Filip ChytilVAN · C4318.9
32Marco RossiVAN · C4318.8
33Dylan StromeWSH · C2118.8
34Bo HorvatNYI · C2118.7
35Sean MonahanCBJ · C2118.5
36Ryan O'ReillyNSH · C2118.1
37Sam BennettFLA · C3317.9
38Barrett HaytonUTA · C3217.9
39Robert ThomasSTL · C2117.8
40Noah CatesPHI · C3217.7

Matchup models plugged in

  • Draw Duel: projected faceoff winsFOWLive

Until a model is live, its categories use the team opponent factors above (or volume only where those did not validate).

How it works

  1. Volume first. We count each team's games in the Monday-to-Sunday fantasy week and flag off-nights (fewer than 10 games) and back-to-backs. See games per week for the whole season.
  2. Opponents second, only where it helps. For each category we measure what every team allows per game (shots on goal against, hits and blocks its opponents record, faceoffs its opponents win, goals against). A factor of 1.10 means 10% more than the league average. We then tested out of sample whether these factors improve weekly forecasts over games times each team's own rate. They did, by a small margin, for shots on goal, hits, blocks, faceoffs won and goalie saves (where the factor is the opponent's own shots), so only those use them. Goals, assists, points, penalty minutes and goals against are volume only. The table below has the numbers.
  3. Each factor is shrunk toward average by how reliable that category is at the team level. We split last season's games into odd and even halves: when the halves agree (high split-half r), team differences are real and the factor is kept; when they do not, it is pulled toward 1. Early in the season this year's small sample is blended with last season, regressed by how well team factors carried over from one season to the next.
  4. Player rates come from rest-of-season projections: LineupExperts for goals, assists, points, PIM, shots, hits, blocks and goalie wins, saves and goals against, with our derived projection model filling power-play points, shorthanded points, plus/minus and faceoffs. Anything still missing (and shutouts) comes from our box scores, this season plus last season at half weight.
  5. Goalies: expected starts come from our goalie start model where it covers the week, otherwise from each goalie's share of projected starts, normalised so a team's goalies add up to one start per game. Wins get the Schedule Edge win-probability change for each game.
  6. The rater turns weekly totals into z-scores within the top 300 skaters and 64 goalies, then sums them with your weights. Punted categories count for nothing. SV% and GAA are scored by impact, so one start cannot top them.
For faceoffs won, last season's split-half r was 0.79 and the year-over-year r 0.71; factors are shrunk accordingly.

Frequently asked questions

Which teams have the best faceoffs won schedule in week 1?

NYR (4 games, 3.98 effective), VAN (4 games, 3.86 effective), FLA (3 games, 3.15 effective), VGK (3 games, 3.13 effective), PHI (3 games, 3.09 effective).

Which teams have the worst faceoffs won schedule?

OTT (1 games), DET (2 games), TBL (2 games), NJD (2 games), PIT (2 games).

How reliable is the opponent effect for faceoffs won?

Last season the opponent measure (how many faceoffs won the opponent allows) had a split-half correlation of 0.79 across the 32 teams and a year-over-year correlation of 0.71. We shrink each team's factor by that reliability. Out of sample it trims weekly forecast error by only a couple of percent, so games played matter far more.

Should I stream for one category?

In head-to-head category leagues, streaming for a close category late in the week often wins it. Volume matters most: an extra game usually beats a better opponent. Use the full week planner to see every category at once and punt the ones you cannot win.