Category Week Planner · 2026-27
Best faceoffs won schedules, fantasy week 26 (Mar 22-28)
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. SEA 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.
| # | Team | GP | Effective | Per game | Opponents (factor) |
|---|---|---|---|---|---|
| 1 | Seattle Kraken | 4 | 4.15 | 1.04 | vs VGK (0.97)vs ANA (1.06)@ MIN (1.06)@ CHI (1.07) |
| 2 | Anaheim Ducks | 4 | 4.12 | 1.03 | vs SJS (1.05)@ SJS (1.05)@ SEA (1.03)@ VAN (1.00) |
| 3 | Chicago Blackhawks | 4 | 4.09 | 1.02 | @ MIN (1.06)@ CBJ (1.00)vs NSH (1.00)vs SEA (1.03) |
| 4 | St. Louis Blues | 4 | 3.97 | 0.99 | @ WPG (0.96)vs NYI (0.94)@ CBJ (1.00)vs BUF (1.08) |
| 5 | Toronto Maple Leafs | 4 | 3.96 | 0.99 | @ MTL (0.96)@ BUF (1.08)vs OTT (0.94)vs WSH (0.99) |
| 6 | Washington Capitals | 4 | 3.93 | 0.98 | vs DET (0.99)@ DET (0.99)vs BOS (0.98)@ TOR (0.96) |
| 7 | Montreal Canadiens | 4 | 3.88 | 0.97 | vs TOR (0.96)vs OTT (0.94)vs NJD (0.99)vs DET (0.99) |
| 8 | Detroit Red Wings | 4 | 3.86 | 0.97 | @ WSH (0.99)vs WSH (0.99)@ NYI (0.94)@ MTL (0.96) |
| 9 | Columbus Blue Jackets | 3 | 3.14 | 1.05 | @ PIT (1.05)vs CHI (1.07)vs STL (1.02) |
| 10 | San Jose Sharks | 3 | 3.12 | 1.04 | @ ANA (1.06)vs ANA (1.06)@ UTA (1.00) |
| 11 | Dallas Stars | 3 | 3.10 | 1.03 | vs CAR (1.05)@ CAR (1.05)vs NSH (1.00) |
| 12 | Winnipeg Jets | 3 | 3.08 | 1.03 | vs STL (1.02)vs COL (1.03)@ COL (1.03) |
| 13 | Edmonton Oilers | 3 | 3.07 | 1.02 | @ CGY (1.02)@ MIN (1.06)vs LAK (0.99) |
| 14 | New York Rangers | 3 | 3.06 | 1.02 | @ NSH (1.00)@ FLA (1.04)@ TBL (1.01) |
| 15 | Vegas Golden Knights | 3 | 3.05 | 1.02 | @ SEA (1.03)@ VAN (1.00)@ CGY (1.02) |
| 16 | Philadelphia Flyers | 3 | 3.04 | 1.01 | @ NJD (0.99)@ TBL (1.01)@ FLA (1.04) |
| 17 | Minnesota Wild | 3 | 3.04 | 1.01 | vs CHI (1.07)vs EDM (0.94)vs SEA (1.03) |
| 18 | Pittsburgh Penguins | 3 | 3.03 | 1.01 | vs CBJ (1.00)vs NJD (0.99)vs CAR (1.05) |
| 19 | Vancouver Canucks | 3 | 3.02 | 1.01 | vs LAK (0.99)vs VGK (0.97)vs ANA (1.06) |
| 20 | Boston Bruins | 3 | 3.00 | 1.00 | vs BUF (1.08)vs OTT (0.94)@ WSH (0.99) |
| 21 | New Jersey Devils | 3 | 2.99 | 1.00 | vs PHI (0.99)@ PIT (1.05)@ MTL (0.96) |
| 22 | Tampa Bay Lightning | 3 | 2.96 | 0.99 | vs FLA (1.04)vs PHI (0.99)vs NYR (0.93) |
| 23 | Buffalo Sabres | 3 | 2.96 | 0.99 | @ BOS (0.98)vs TOR (0.96)@ STL (1.02) |
| 24 | Los Angeles Kings | 3 | 2.95 | 0.98 | @ VAN (1.00)@ CGY (1.02)@ EDM (0.94) |
| 25 | Nashville Predators | 3 | 2.93 | 0.98 | vs NYR (0.93)@ CHI (1.07)@ DAL (0.93) |
| 26 | Florida Panthers | 3 | 2.92 | 0.97 | @ TBL (1.01)vs NYR (0.93)vs PHI (0.99) |
| 27 | Colorado Avalanche | 3 | 2.92 | 0.97 | vs UTA (1.00)@ WPG (0.96)vs WPG (0.96) |
| 28 | Carolina Hurricanes | 3 | 2.92 | 0.97 | @ DAL (0.93)vs DAL (0.93)@ PIT (1.05) |
| 29 | Calgary Flames | 3 | 2.90 | 0.97 | vs EDM (0.94)vs LAK (0.99)vs VGK (0.97) |
| 30 | Ottawa Senators | 3 | 2.89 | 0.96 | @ MTL (0.96)@ BOS (0.98)@ TOR (0.96) |
| 31 | Utah Mammoth | 2 | 2.08 | 1.04 | @ COL (1.03)vs SJS (1.05) |
| 32 | New York Islanders | 2 | 2.02 | 1.01 | @ STL (1.02)vs DET (0.99) |
Projected faceoffs won leaders, week 26
Projected totals for the week. Off-night games in green.
| # | Player | GP | FOW |
|---|---|---|---|
| 1 | Dylan LarkinDET · C | 42 | 40.8 |
| 2 | Chandler StephensonSEA · C | 41 | 39.3 |
| 3 | John TavaresTOR · C | 42 | 37.6 |
| 4 | Auston MatthewsTOR · C | 42 | 36.8 |
| 5 | Nico HischierNJD · C | 3 | 36.7 |
| 6 | Robert ThomasSTL · C | 41 | 35.9 |
| 7 | Dylan StromeWSH · C | 43 | 35.9 |
| 8 | Sidney CrosbyPIT · C | 31 | 35.4 |
| 9 | Nick SuzukiMTL · C | 43 | 33.9 |
| 10 | Jordan StaalCAR · C | 3 | 31.4 |
| 11 | Joel Eriksson EkMIN · C | 3 | 29.8 |
| 12 | Phillip DanaultMTL · C | 43 | 29.0 |
| 13 | Leon DraisaitlEDM · C | 3 | 28.8 |
| 14 | Elias LindholmBOS · C | 31 | 28.0 |
| 15 | Sean MonahanCBJ · C | 31 | 27.9 |
| 16 | Jake EvansMTL · C | 43 | 27.4 |
| 17 | Matthew BeniersSEA · C | 41 | 26.7 |
| 18 | Pierre-Luc DuboisWSH · C | 43 | 26.6 |
| 19 | Ryan O'ReillyNSH · C | 31 | 26.5 |
| 20 | Elias PetterssonVAN · C | 3 | 26.3 |
| 21 | Mikael BacklundCGY · C | 3 | 26.2 |
| 22 | Nathan MacKinnonCOL · C | 3 | 25.4 |
| 23 | Ryan PoehlingANA · C | 42 | 25.1 |
| 24 | Andrew CoppDET · C | 42 | 24.8 |
| 25 | Brock NelsonCOL · C | 3 | 24.6 |
| 26 | Aleksander BarkovFLA · C | 3 | 24.5 |
| 27 | Tomas HertlVGK · C | 3 | 24.4 |
| 28 | J.T. CompherDET · C | 42 | 24.3 |
| 29 | Mikael GranlundANA · C | 42 | 24.3 |
| 30 | Sean CouturierPHI · C | 3 | 24.2 |
| 31 | J.T. MillerNYR · C | 3 | 24.2 |
| 32 | Adam LowryWPG · C | 3 | 24.0 |
| 33 | Alexander WennbergSJS · C | 33 | 24.0 |
| 34 | Jack EichelVGK · C | 3 | 23.6 |
| 35 | William KarlssonVGK · C | 3 | 23.2 |
| 36 | Macklin CelebriniSJS · C | 33 | 23.1 |
| 37 | Leo CarlssonANA · C | 42 | 23.1 |
| 38 | Sebastian AhoCAR · C | 3 | 23.0 |
| 39 | Anton LundellFLA · C | 3 | 23.0 |
| 40 | Anthony CirelliTBL · C | 3 | 22.9 |
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 26?
SEA (4 games, 4.15 effective), ANA (4 games, 4.12 effective), CHI (4 games, 4.09 effective), STL (4 games, 3.97 effective), TOR (4 games, 3.96 effective).
Which teams have the worst faceoffs won schedule?
NYI (2 games), UTA (2 games), OTT (3 games), CGY (3 games), CAR (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.