Edgehalla

Category Week Planner · 2026-27

Best faceoffs won schedules, fantasy week 13 (Dec 21-27)

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 3.87.

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 13
#TeamGPEffectivePer gameOpponents (factor)
1Columbus Blue Jackets43.870.97@ PHI (0.99)vs MTL (0.96)@ WSH (0.99)vs EDM (0.94)
2Vegas Golden Knights33.151.05vs CAR (1.05)@ ANA (1.06)@ SJS (1.05)
3New Jersey Devils32.991.00@ NYR (0.93)@ BUF (1.08)vs BOS (0.98)
4St. Louis Blues32.980.99@ FLA (1.04)@ TBL (1.01)vs NYR (0.93)
5Toronto Maple Leafs32.940.98vs WSH (0.99)@ DET (0.99)@ MTL (0.96)
6New York Islanders32.900.97vs BOS (0.98)@ BOS (0.98)vs OTT (0.94)
7Montreal Canadiens32.880.96@ CBJ (1.00)vs TOR (0.96)vs DAL (0.93)
8Boston Bruins32.860.95@ NYI (0.94)vs NYI (0.94)@ NJD (0.99)
9Pittsburgh Penguins22.131.06vs BUF (1.08)@ CAR (1.05)
10Tampa Bay Lightning22.071.03vs STL (1.02)@ FLA (1.04)
11Los Angeles Kings22.061.03@ MIN (1.06)vs UTA (1.00)
12Winnipeg Jets22.061.03@ NSH (1.00)@ MIN (1.06)
13Philadelphia Flyers22.061.03vs CBJ (1.00)@ CHI (1.07)
14Buffalo Sabres22.041.02@ PIT (1.05)vs NJD (0.99)
15Florida Panthers22.031.02vs STL (1.02)vs TBL (1.01)
16Vancouver Canucks22.021.01vs UTA (1.00)@ CGY (1.02)
17Carolina Hurricanes22.021.01@ VGK (0.97)vs PIT (1.05)
18New York Rangers22.011.00vs NJD (0.99)@ STL (1.02)
19Anaheim Ducks22.001.00vs VGK (0.97)vs COL (1.03)
20San Jose Sharks22.001.00@ SEA (1.03)vs VGK (0.97)
21Utah Mammoth21.991.00@ VAN (1.00)@ LAK (0.99)
22Dallas Stars21.970.99vs CGY (1.02)@ MTL (0.96)
23Detroit Red Wings21.960.98vs TOR (0.96)@ NSH (1.00)
24Minnesota Wild21.950.98vs LAK (0.99)vs WPG (0.96)
25Nashville Predators21.950.98vs WPG (0.96)vs DET (0.99)
26Washington Capitals21.950.98@ TOR (0.96)vs CBJ (1.00)
27Calgary Flames21.930.97@ DAL (0.93)vs VAN (1.00)
28Colorado Avalanche11.061.06@ ANA (1.06)
29Seattle Kraken11.041.04vs SJS (1.05)
30Edmonton Oilers10.990.99@ CBJ (1.00)
31Chicago Blackhawks10.980.98vs PHI (0.99)
32Ottawa Senators10.940.94@ NYI (0.94)

Projected faceoffs won leaders, week 13

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

Players by projected faceoffs won, week 13
#PlayerGPFOW
1Nico HischierNJD · C3136.6
2Sean MonahanCBJ · C4234.3
3John TavaresTOR · C3127.9
4Bo HorvatNYI · C3127.8
5Auston MatthewsTOR · C3127.3
6Robert ThomasSTL · C3227.0
7Elias LindholmBOS · C3126.6
8Charlie CoyleCBJ · C4226.1
9Adam FantilliCBJ · C4225.4
10Tomas HertlVGK · C3125.3
11Nick SuzukiMTL · C3125.2
12Sidney CrosbyPIT · C224.8
13Jack EichelVGK · C3124.4
14Jean-Gabriel PageauNYI · C3124.4
15William KarlssonVGK · C3124.0
16Jordan StaalCAR · C2121.8
17Phillip DanaultMTL · C3121.5
18Dylan LarkinDET · C220.7
19Nic DowdVGK · C3120.4
20Jake EvansMTL · C3120.3
21Vincent TrocheckUTA · C2119.5
22Joel Eriksson EkMIN · C219.2
23Pavel ZachaBOS · C3118.8
24Dylan StromeWSH · C2117.8
25Ryan O'ReillyNSH · C217.7
26Elias PetterssonVAN · C2117.6
27Mikael BacklundCGY · C217.5
28Brayden SchennNYI · C3117.3
29Aleksander BarkovFLA · C2117.0
30Sean CouturierPHI · C2216.4
31Nick PaulTOR · C3116.2
32Mason McTavishSTL · C3216.1
33Adam LowryWPG · C216.1
34Anthony CirelliTBL · C216.0
35Anton LundellFLA · C2116.0
36Sebastian AhoCAR · C2115.9
37J.T. MillerNYR · C2115.9
38Alexander WennbergSJS · C215.3
39Christian DvorakPHI · C2215.1
40Mark ScheifeleWPG · C215.0

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 13?

CBJ (4 games, 3.87 effective), VGK (3 games, 3.15 effective), NJD (3 games, 2.99 effective), STL (3 games, 2.98 effective), TOR (3 games, 2.94 effective).

Which teams have the worst faceoffs won schedule?

OTT (1 games), CHI (1 games), EDM (1 games), SEA (1 games), COL (1 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.