Edgehalla

Category Week Planner · 2026-27

Best faceoffs won schedules, fantasy week 23 (Mar 1-7)

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. CBJ leads with 4.15.

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 23
#TeamGPEffectivePer gameOpponents (factor)
1Columbus Blue Jackets44.151.04vs CAR (1.05)@ SJS (1.05)@ LAK (0.99)@ ANA (1.06)
2Toronto Maple Leafs44.121.03vs DET (0.99)vs SEA (1.03)@ CAR (1.05)@ FLA (1.04)
3Los Angeles Kings44.091.02vs ANA (1.06)vs TBL (1.01)vs CBJ (1.00)vs COL (1.03)
4Ottawa Senators44.081.02@ NJD (0.99)vs BOS (0.98)vs SEA (1.03)vs BUF (1.08)
5Pittsburgh Penguins44.041.01@ BUF (1.08)@ FLA (1.04)@ BOS (0.98)@ NYI (0.94)
6Washington Capitals44.001.00@ NYI (0.94)vs CAR (1.05)@ WPG (0.96)@ MIN (1.06)
7San Jose Sharks43.991.00vs MTL (0.96)vs CBJ (1.00)vs COL (1.03)vs TBL (1.01)
8Buffalo Sabres43.940.99vs PIT (1.05)vs STL (1.02)vs NYR (0.93)@ OTT (0.94)
9Carolina Hurricanes43.940.99@ CBJ (1.00)@ WSH (0.99)vs TOR (0.96)vs UTA (1.00)
10Chicago Blackhawks43.890.97vs EDM (0.94)@ VAN (1.00)@ CGY (1.02)@ EDM (0.94)
11Vegas Golden Knights43.840.96@ PHI (0.99)@ NYI (0.94)@ NJD (0.99)@ NYR (0.93)
12Seattle Kraken43.810.95@ WPG (0.96)@ TOR (0.96)@ MTL (0.96)@ OTT (0.94)
13Edmonton Oilers33.151.05@ CHI (1.07)@ CGY (1.02)vs CHI (1.07)
14Tampa Bay Lightning33.101.03@ LAK (0.99)@ ANA (1.06)@ SJS (1.05)
15Nashville Predators33.101.03vs COL (1.03)vs UTA (1.00)vs MIN (1.06)
16Utah Mammoth33.091.03@ FLA (1.04)@ NSH (1.00)@ CAR (1.05)
17Montreal Canadiens33.081.03@ SJS (1.05)vs SEA (1.03)vs VAN (1.00)
18Colorado Avalanche33.041.01@ NSH (1.00)@ SJS (1.05)@ LAK (0.99)
19Dallas Stars33.041.01vs MIN (1.06)@ PHI (0.99)@ DET (0.99)
20Winnipeg Jets33.031.01vs SEA (1.03)vs WSH (0.99)vs CGY (1.02)
21New York Rangers33.021.01vs BOS (0.98)@ BUF (1.08)vs VGK (0.97)
22Florida Panthers33.011.00vs UTA (1.00)vs PIT (1.05)vs TOR (0.96)
23New York Islanders33.001.00vs WSH (0.99)vs VGK (0.97)vs PIT (1.05)
24Anaheim Ducks33.001.00@ LAK (0.99)vs TBL (1.01)vs CBJ (1.00)
25Calgary Flames32.960.99vs EDM (0.94)vs CHI (1.07)@ WPG (0.96)
26Boston Bruins32.920.97@ NYR (0.93)@ OTT (0.94)vs PIT (1.05)
27Minnesota Wild32.920.97@ DAL (0.93)@ NSH (1.00)vs WSH (0.99)
28Philadelphia Flyers32.920.97vs VGK (0.97)vs DAL (0.93)vs STL (1.02)
29New Jersey Devils32.900.97vs OTT (0.94)@ DET (0.99)vs VGK (0.97)
30Detroit Red Wings32.880.96@ TOR (0.96)vs NJD (0.99)vs DAL (0.93)
31St. Louis Blues22.061.03@ BUF (1.08)@ PHI (0.99)
32Vancouver Canucks22.021.01vs CHI (1.07)@ MTL (0.96)

Projected faceoffs won leaders, week 23

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

Players by projected faceoffs won, week 23
#PlayerGPFOW
1Sidney CrosbyPIT · C4447.2
2Jordan StaalCAR · C4442.5
3John TavaresTOR · C4439.1
4Auston MatthewsTOR · C4438.3
5Sean MonahanCBJ · C4436.8
6Dylan StromeWSH · C4436.6
7Chandler StephensonSEA · C4436.1
8Nico HischierNJD · C3335.5
9Sebastian AhoCAR · C4431.2
10Tomas HertlVGK · C4430.8
11Alexander WennbergSJS · C4430.7
12Dylan LarkinDET · C3330.4
13Vincent TrocheckUTA · C3330.2
14Jack EichelVGK · C4429.7
15Macklin CelebriniSJS · C4429.6
16Leon DraisaitlEDM · C3329.5
17William KarlssonVGK · C4429.2
18Bo HorvatNYI · C3328.8
19Ryan McLeodBUF · C4428.8
20Joel Eriksson EkMIN · C3328.7
21Ryan O'ReillyNSH · C3328.1
22Charlie CoyleCBJ · C4428.0
23Joshua NorrisBUF · C4427.7
24Adam FantilliCBJ · C4427.2
25Elias LindholmBOS · C3327.2
26Pierre-Luc DuboisWSH · C4427.1
27Mikael BacklundCGY · C3326.8
28Nick SuzukiMTL · C3326.8
29Nathan MacKinnonCOL · C3326.4
30Brock NelsonCOL · C3325.6
31Quinton ByfieldLAK · C4425.6
32Claude GirouxOTT · RW4425.4
33Jean-Gabriel PageauNYI · C3325.2
34Aleksander BarkovFLA · C3325.2
35Nic DowdVGK · C4424.9
36Matthew BeniersSEA · C4424.5
37Anthony CirelliTBL · C3324.0
38J.T. MillerNYR · C3323.9
39Anton LundellFLA · C3323.7
40Adam LowryWPG · C3323.6

Matchup models plugged in

  • Draw Duel: projected faceoff winsFOWComing soon

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 23?

CBJ (4 games, 4.15 effective), TOR (4 games, 4.12 effective), LAK (4 games, 4.09 effective), OTT (4 games, 4.08 effective), PIT (4 games, 4.04 effective).

Which teams have the worst faceoffs won schedule?

VAN (2 games), STL (2 games), DET (3 games), NJD (3 games), PHI (3 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.