Category Week Planner · 2026-27
Best saves schedules, fantasy week 14 (Dec 28-Jan 3)
Saves. The opponent factor is the opponent's own shots on goal per game. Teams are ranked by effective games: games this week, each weighted by how many shots the opponent takes. STL leads with 4.26.
Data updated:
Teams ranked for saves
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 | St. Louis Blues | 4 | 4.26 | 1.06 | @ COL (1.15)@ VGK (1.08)@ MIN (1.05)vs CGY (0.98) |
| 2 | Pittsburgh Penguins | 4 | 4.25 | 1.06 | vs CAR (1.09)@ OTT (1.03)vs MIN (1.05)vs EDM (1.08) |
| 3 | Washington Capitals | 4 | 4.10 | 1.03 | vs EDM (1.08)@ NYI (1.03)@ CBJ (0.97)vs OTT (1.03) |
| 4 | Detroit Red Wings | 4 | 4.09 | 1.02 | @ BUF (1.00)@ NYR (0.91)vs CAR (1.09)vs EDM (1.08) |
| 5 | Nashville Predators | 4 | 4.04 | 1.01 | @ FLA (0.97)@ TBL (1.00)vs FLA (0.97)@ CAR (1.09) |
| 6 | Carolina Hurricanes | 4 | 4.03 | 1.01 | vs TBL (1.00)@ PIT (1.07)@ DET (0.98)vs NSH (0.97) |
| 7 | Edmonton Oilers | 4 | 4.02 | 1.00 | @ WSH (0.98)@ BOS (0.98)@ DET (0.98)@ PIT (1.07) |
| 8 | Tampa Bay Lightning | 4 | 4.02 | 1.00 | @ CAR (1.09)vs NSH (0.97)vs MTL (0.95)vs BUF (1.00) |
| 9 | Vancouver Canucks | 4 | 4.00 | 1.00 | vs LAK (1.05)vs SEA (0.95)@ UTA (1.04)@ SEA (0.95) |
| 10 | Vegas Golden Knights | 4 | 3.99 | 1.00 | @ MIN (1.05)@ CHI (0.85)vs STL (0.94)vs COL (1.15) |
| 11 | Calgary Flames | 4 | 3.92 | 0.98 | vs SJS (0.98)vs WPG (1.00)@ WPG (1.00)@ STL (0.94) |
| 12 | Florida Panthers | 4 | 3.89 | 0.97 | vs NSH (0.97)vs BUF (1.00)@ NSH (0.97)vs MTL (0.95) |
| 13 | San Jose Sharks | 4 | 3.86 | 0.96 | @ WPG (1.00)@ CGY (0.98)vs PHI (0.89)vs TOR (0.98) |
| 14 | Columbus Blue Jackets | 4 | 3.83 | 0.96 | @ MTL (0.95)vs WSH (0.98)vs BOS (0.98)@ NYR (0.91) |
| 15 | Seattle Kraken | 4 | 3.78 | 0.95 | vs PHI (0.89)@ VAN (0.93)vs NYI (1.03)vs VAN (0.93) |
| 16 | Utah Mammoth | 3 | 3.23 | 1.08 | vs NJD (1.15)vs COL (1.15)vs VAN (0.93) |
| 17 | New Jersey Devils | 3 | 3.12 | 1.04 | @ UTA (1.04)@ ANA (1.02)@ LAK (1.05) |
| 18 | Minnesota Wild | 3 | 3.09 | 1.03 | vs VGK (1.08)@ PIT (1.07)vs STL (0.94) |
| 19 | Los Angeles Kings | 3 | 3.06 | 1.02 | @ VAN (0.93)vs TOR (0.98)vs NJD (1.15) |
| 20 | Toronto Maple Leafs | 3 | 3.06 | 1.02 | @ ANA (1.02)@ LAK (1.05)@ SJS (0.98) |
| 21 | Colorado Avalanche | 3 | 3.05 | 1.02 | vs STL (0.94)@ UTA (1.04)@ VGK (1.08) |
| 22 | Anaheim Ducks | 3 | 3.02 | 1.01 | vs TOR (0.98)vs NJD (1.15)vs PHI (0.89) |
| 23 | Chicago Blackhawks | 3 | 3.02 | 1.01 | vs VGK (1.08)vs BOS (0.98)@ DAL (0.96) |
| 24 | Ottawa Senators | 3 | 3.01 | 1.00 | vs DAL (0.96)vs PIT (1.07)@ WSH (0.98) |
| 25 | Buffalo Sabres | 3 | 2.96 | 0.99 | vs DET (0.98)@ FLA (0.97)@ TBL (1.00) |
| 26 | Philadelphia Flyers | 3 | 2.95 | 0.98 | @ SEA (0.95)@ SJS (0.98)@ ANA (1.02) |
| 27 | Winnipeg Jets | 3 | 2.94 | 0.98 | vs SJS (0.98)@ CGY (0.98)vs CGY (0.98) |
| 28 | Montreal Canadiens | 3 | 2.94 | 0.98 | vs CBJ (0.97)@ TBL (1.00)@ FLA (0.97) |
| 29 | New York Rangers | 3 | 2.92 | 0.97 | vs DET (0.98)@ DAL (0.96)vs CBJ (0.97) |
| 30 | Boston Bruins | 3 | 2.90 | 0.97 | vs EDM (1.08)@ CHI (0.85)@ CBJ (0.97) |
| 31 | Dallas Stars | 3 | 2.80 | 0.93 | @ OTT (1.03)vs NYR (0.91)vs CHI (0.85) |
| 32 | New York Islanders | 2 | 1.93 | 0.96 | vs WSH (0.98)@ SEA (0.95) |
Projected saves leaders, week 14
Projected totals for the week, weighted by expected starts. Off-night games in green.
| # | Player | GS | SV |
|---|---|---|---|
| 1 | Arturs SilovsPIT · G | 3.43 | 92.6 |
| 2 | Logan ThompsonWSH · G | 3.03 | 81.5 |
| 3 | Andrei VasilevskiyTBL · G | 3.12 | 81.5 |
| 4 | John GibsonDET · G | 2.92 | 77.7 |
| 5 | Karel VejmelkaUTA · G | 2.71 | 75.1 |
| 6 | Dustin WolfCGY · G | 2.92 | 74.8 |
| 7 | Brandon BussiCAR · G | 3.02 | 73.9 |
| 8 | Jet GreavesCBJ · G | 2.82 | 71.8 |
| 9 | Lukas DostalANA · G | 2.52 | 69.0 |
| 10 | Jacob MarkstromFLA · G | 2.63 | 62.5 |
| 11 | Joel HoferSTL · G | 2.22 | 62.1 |
| 12 | Jeremy SwaymanBOS · G | 2.32 | 60.7 |
| 13 | Kevin LankinenVAN · G | 2.33 | 59.1 |
| 14 | Juuse SarosNSH · G | 2.22 | 58.8 |
| 15 | Darcy KuemperLAK · G | 2.22 | 58.4 |
| 16 | Igor ShesterkinNYR · G | 2.32 | 58.0 |
| 17 | Carter HartVGK · G | 2.22 | 56.6 |
| 18 | Jakub DobesMTL · G | 2.21 | 55.4 |
| 19 | Joey DaccordSEA · G | 2.22 | 53.4 |
| 20 | Jake OettingerDAL · G | 2.31 | 53.3 |
| 21 | Spencer KnightCHI · G | 2.01 | 52.6 |
| 22 | Mackenzie BlackwoodCOL · G | 2.01 | 52.5 |
| 23 | Ukko-Pekka LuukkonenBUF · G | 2.12 | 51.4 |
| 24 | Linus UllmarkOTT · G | 1.91 | 49.8 |
| 25 | Ilya SorokinNYI · G | 1.91 | 49.5 |
| 26 | Philipp GrubauerSEA · G | 1.82 | 47.6 |
| 27 | Jordan BinningtonSTL · G | 1.82 | 47.5 |
| 28 | Yaroslav AskarovSJS · G | 1.82 | 47.0 |
| 29 | Sergei BobrovskyTOR · G | 1.71 | 44.7 |
| 30 | Stuart SkinnerWPG · G | 1.72 | 42.1 |
| 31 | Jake AllenNJD · G | 1.61 | 42.0 |
| 32 | Joseph WollPHI · G | 1.52 | 40.6 |
| 33 | Tristan JarryEDM · G | 1.53 | 38.3 |
| 34 | Daniel VladarPHI · G | 1.52 | 37.6 |
| 35 | Jesper WallstedtMIN · G | 1.32 | 36.3 |
| 36 | Akira SchmidFLA · G | 1.43 | 36.0 |
| 37 | Frederik AndersenEDM · G | 1.33 | 34.2 |
| 38 | Devon LeviEDM · G | 1.13 | 29.9 |
| 39 | Alex NedeljkovicSJS · G | 1.12 | 28.8 |
| 40 | Filip GustavssonMIN · G | 1.02 | 28.3 |
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 saves, last season's split-half r was 0.86 and the year-over-year r 0.62; factors are shrunk accordingly.
Frequently asked questions
Which teams have the best saves schedule in week 14?
STL (4 games, 4.26 effective), PIT (4 games, 4.25 effective), WSH (4 games, 4.10 effective), DET (4 games, 4.09 effective), NSH (4 games, 4.04 effective).
Which teams have the worst saves schedule?
NYI (2 games), DAL (3 games), BOS (3 games), NYR (3 games), MTL (3 games).
How reliable is the opponent effect for saves?
Last season the opponent measure (how many shots the opponent takes) had a split-half correlation of 0.86 across the 32 teams and a year-over-year correlation of 0.62. 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.