Category Week Planner · 2026-27
Best faceoffs won schedules, fantasy week 10 (Nov 30-Dec 6)
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. WPG leads with 4.09.
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.
| # | Team | GP | Effective | Per game | Opponents (factor) |
|---|---|---|---|---|---|
| 1 | Winnipeg Jets | 4 | 4.09 | 1.02 | vs CHI (1.07)@ UTA (1.00)@ VGK (0.97)@ ANA (1.06) |
| 2 | Pittsburgh Penguins | 4 | 4.05 | 1.01 | vs CBJ (1.00)@ STL (1.02)vs NJD (0.99)vs SJS (1.05) |
| 3 | Nashville Predators | 4 | 4.03 | 1.01 | vs OTT (0.94)vs CHI (1.07)vs CBJ (1.00)@ STL (1.02) |
| 4 | Seattle Kraken | 4 | 4.00 | 1.00 | vs DAL (0.93)vs DAL (0.93)@ ANA (1.06)vs BUF (1.08) |
| 5 | Montreal Canadiens | 4 | 4.00 | 1.00 | @ UTA (1.00)vs TBL (1.01)vs FLA (1.04)vs OTT (0.94) |
| 6 | Buffalo Sabres | 4 | 3.98 | 1.00 | @ EDM (0.94)@ CGY (1.02)@ VAN (1.00)@ SEA (1.03) |
| 7 | Ottawa Senators | 4 | 3.96 | 0.99 | @ NSH (1.00)vs NJD (0.99)vs TBL (1.01)@ MTL (0.96) |
| 8 | Chicago Blackhawks | 4 | 3.92 | 0.98 | @ WPG (0.96)@ NSH (1.00)@ STL (1.02)vs EDM (0.94) |
| 9 | San Jose Sharks | 4 | 3.91 | 0.98 | @ NJD (0.99)@ NYR (0.93)@ NYI (0.94)@ PIT (1.05) |
| 10 | Utah Mammoth | 4 | 3.88 | 0.97 | vs MTL (0.96)vs WPG (0.96)@ VGK (0.97)@ LAK (0.99) |
| 11 | Edmonton Oilers | 3 | 3.13 | 1.04 | vs BUF (1.08)vs WSH (0.99)@ CHI (1.07) |
| 12 | New York Rangers | 3 | 3.12 | 1.04 | vs CAR (1.05)vs SJS (1.05)vs COL (1.03) |
| 13 | St. Louis Blues | 3 | 3.12 | 1.04 | vs PIT (1.05)vs CHI (1.07)vs NSH (1.00) |
| 14 | Columbus Blue Jackets | 3 | 3.10 | 1.03 | @ PIT (1.05)@ CAR (1.05)@ NSH (1.00) |
| 15 | Toronto Maple Leafs | 3 | 3.08 | 1.03 | vs PHI (0.99)vs FLA (1.04)@ CAR (1.05) |
| 16 | New York Islanders | 3 | 3.08 | 1.03 | vs FLA (1.04)@ PHI (0.99)vs SJS (1.05) |
| 17 | Dallas Stars | 3 | 3.06 | 1.02 | @ SEA (1.03)@ SEA (1.03)@ VAN (1.00) |
| 18 | Calgary Flames | 3 | 3.06 | 1.02 | @ DET (0.99)vs BUF (1.08)vs WSH (0.99) |
| 19 | Anaheim Ducks | 3 | 3.05 | 1.02 | vs MIN (1.06)vs SEA (1.03)vs WPG (0.96) |
| 20 | Minnesota Wild | 3 | 3.04 | 1.01 | @ ANA (1.06)@ LAK (0.99)vs PHI (0.99) |
| 21 | New Jersey Devils | 3 | 3.04 | 1.01 | vs SJS (1.05)@ OTT (0.94)@ PIT (1.05) |
| 22 | Los Angeles Kings | 3 | 3.03 | 1.01 | vs VGK (0.97)vs MIN (1.06)vs UTA (1.00) |
| 23 | Detroit Red Wings | 3 | 3.03 | 1.01 | vs CGY (1.02)vs COL (1.03)@ BOS (0.98) |
| 24 | Vancouver Canucks | 3 | 3.00 | 1.00 | @ WSH (0.99)vs BUF (1.08)vs DAL (0.93) |
| 25 | Vegas Golden Knights | 3 | 2.96 | 0.99 | @ LAK (0.99)vs UTA (1.00)vs WPG (0.96) |
| 26 | Philadelphia Flyers | 3 | 2.96 | 0.98 | @ TOR (0.96)vs NYI (0.94)@ MIN (1.06) |
| 27 | Washington Capitals | 3 | 2.95 | 0.98 | vs VAN (1.00)@ EDM (0.94)@ CGY (1.02) |
| 28 | Colorado Avalanche | 3 | 2.90 | 0.97 | @ BOS (0.98)@ DET (0.99)@ NYR (0.93) |
| 29 | Carolina Hurricanes | 3 | 2.88 | 0.96 | @ NYR (0.93)vs CBJ (1.00)vs TOR (0.96) |
| 30 | Florida Panthers | 3 | 2.85 | 0.95 | @ NYI (0.94)@ TOR (0.96)@ MTL (0.96) |
| 31 | Boston Bruins | 2 | 2.03 | 1.01 | vs COL (1.03)vs DET (0.99) |
| 32 | Tampa Bay Lightning | 2 | 1.90 | 0.95 | @ MTL (0.96)@ OTT (0.94) |
Projected faceoffs won leaders, week 10
Projected totals for the week. Off-night games in green.
| # | Player | GP | FOW |
|---|---|---|---|
| 1 | Sidney CrosbyPIT · C | 43 | 47.4 |
| 2 | Chandler StephensonSEA · C | 43 | 37.8 |
| 3 | Vincent TrocheckUTA · C | 42 | 37.8 |
| 4 | Nico HischierNJD · C | 31 | 37.2 |
| 5 | Ryan O'ReillyNSH · C | 42 | 36.5 |
| 6 | Nick SuzukiMTL · C | 43 | 34.9 |
| 7 | Dylan LarkinDET · C | 31 | 31.9 |
| 8 | Adam LowryWPG · C | 43 | 31.9 |
| 9 | Jordan StaalCAR · C | 31 | 31.1 |
| 10 | Alexander WennbergSJS · C | 43 | 30.0 |
| 11 | Joel Eriksson EkMIN · C | 31 | 29.9 |
| 12 | Phillip DanaultMTL · C | 43 | 29.8 |
| 13 | Mark ScheifeleWPG · C | 43 | 29.7 |
| 14 | Bo HorvatNYI · C | 32 | 29.5 |
| 15 | Leon DraisaitlEDM · C | 32 | 29.4 |
| 16 | John TavaresTOR · C | 31 | 29.2 |
| 17 | Ryan McLeodBUF · C | 43 | 29.1 |
| 18 | Macklin CelebriniSJS · C | 43 | 29.0 |
| 19 | Auston MatthewsTOR · C | 31 | 28.6 |
| 20 | Kevin StenlundUTA · C | 42 | 28.3 |
| 21 | Robert ThomasSTL · C | 33 | 28.3 |
| 22 | Jake EvansMTL · C | 43 | 28.2 |
| 23 | Joshua NorrisBUF · C | 43 | 27.9 |
| 24 | Mikael BacklundCGY · C | 31 | 27.7 |
| 25 | Sean MonahanCBJ · C | 31 | 27.5 |
| 26 | Dylan StromeWSH · C | 31 | 27.0 |
| 27 | Elias PetterssonVAN · C | 32 | 26.1 |
| 28 | Jean-Gabriel PageauNYI · C | 32 | 25.8 |
| 29 | Matthew BeniersSEA · C | 43 | 25.7 |
| 30 | Nathan MacKinnonCOL · C | 31 | 25.2 |
| 31 | Jack DruryNSH · C | 42 | 24.8 |
| 32 | J.T. MillerNYR · C | 31 | 24.7 |
| 33 | Claude GirouxOTT · RW | 42 | 24.7 |
| 34 | Brock NelsonCOL · C | 31 | 24.5 |
| 35 | Barrett HaytonUTA · C | 42 | 24.3 |
| 36 | Aleksander BarkovFLA · C | 31 | 23.9 |
| 37 | Tomas HertlVGK · C | 31 | 23.7 |
| 38 | Sean CouturierPHI · C | 31 | 23.5 |
| 39 | Jack EichelVGK · C | 31 | 22.9 |
| 40 | Sebastian AhoCAR · C | 31 | 22.8 |
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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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 10?
WPG (4 games, 4.09 effective), PIT (4 games, 4.05 effective), NSH (4 games, 4.03 effective), SEA (4 games, 4.00 effective), MTL (4 games, 4.00 effective).
Which teams have the worst faceoffs won schedule?
TBL (2 games), BOS (2 games), FLA (3 games), CAR (3 games), COL (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.