Category Week Planner · 2026-27
Best hits schedules, fantasy week 9 (Nov 23-29)
Hits. The opponent factor is hits its opponents record per game (the home scorer’s counting bias is now negligible). Teams are ranked by effective games: games this week, each weighted by how many hits the opponent allows. OTT leads with 4.22.
Data updated:
Teams ranked for hits
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 | Ottawa Senators | 4 | 4.22 | 1.05 | vs CGY (0.99)@ FLA (1.05)@ TBL (1.04)@ CAR (1.14) |
| 2 | Calgary Flames | 4 | 4.14 | 1.04 | @ OTT (1.06)@ PIT (1.05)@ NJD (0.98)@ NYR (1.06) |
| 3 | Minnesota Wild | 4 | 4.06 | 1.02 | @ SJS (0.97)vs UTA (0.97)vs COL (1.00)@ CHI (1.13) |
| 4 | Utah Mammoth | 4 | 3.96 | 0.99 | @ WPG (0.97)@ MIN (0.92)@ NSH (1.00)@ DAL (1.07) |
| 5 | Winnipeg Jets | 4 | 3.92 | 0.98 | vs UTA (0.97)@ BOS (1.02)@ NYI (0.95)@ NJD (0.98) |
| 6 | Anaheim Ducks | 4 | 3.90 | 0.97 | @ SEA (1.01)vs SJS (0.97)vs LAK (0.96)@ LAK (0.96) |
| 7 | Seattle Kraken | 4 | 3.87 | 0.97 | vs ANA (0.92)vs CHI (1.13)@ EDM (0.91)vs EDM (0.91) |
| 8 | New Jersey Devils | 4 | 3.84 | 0.96 | vs CBJ (0.94)@ CBJ (0.94)vs CGY (0.99)vs WPG (0.97) |
| 9 | San Jose Sharks | 4 | 3.83 | 0.96 | vs MIN (0.92)@ ANA (0.92)vs VGK (0.99)@ COL (1.00) |
| 10 | New York Islanders | 4 | 3.79 | 0.95 | vs TOR (0.97)vs STL (0.90)vs WPG (0.97)@ PHI (0.95) |
| 11 | Tampa Bay Lightning | 3 | 3.24 | 1.08 | vs CAR (1.14)vs OTT (1.06)@ FLA (1.05) |
| 12 | New York Rangers | 3 | 3.16 | 1.05 | @ BUF (1.04)@ CHI (1.13)vs CGY (0.99) |
| 13 | Florida Panthers | 3 | 3.09 | 1.03 | vs OTT (1.06)@ WSH (1.00)vs TBL (1.04) |
| 14 | Nashville Predators | 3 | 3.07 | 1.02 | @ DAL (1.07)vs UTA (0.97)@ DET (1.03) |
| 15 | Toronto Maple Leafs | 3 | 3.02 | 1.01 | @ NYI (0.95)@ BOS (1.02)@ PIT (1.05) |
| 16 | Vancouver Canucks | 3 | 3.01 | 1.00 | @ DET (1.03)@ PHI (0.95)@ BOS (1.02) |
| 17 | Pittsburgh Penguins | 3 | 3.01 | 1.00 | vs CGY (0.99)@ BUF (1.04)vs TOR (0.97) |
| 18 | St. Louis Blues | 3 | 3.01 | 1.00 | @ NYI (0.95)vs DAL (1.07)vs WSH (1.00) |
| 19 | Edmonton Oilers | 3 | 3.00 | 1.00 | vs VGK (0.99)vs SEA (1.01)@ SEA (1.01) |
| 20 | Columbus Blue Jackets | 3 | 3.00 | 1.00 | @ NJD (0.98)vs NJD (0.98)vs DET (1.03) |
| 21 | Chicago Blackhawks | 3 | 2.99 | 1.00 | @ SEA (1.01)vs NYR (1.06)vs MIN (0.92) |
| 22 | Philadelphia Flyers | 3 | 2.96 | 0.98 | @ WSH (1.00)vs VAN (1.01)vs NYI (0.95) |
| 23 | Boston Bruins | 3 | 2.95 | 0.98 | vs WPG (0.97)vs TOR (0.97)vs VAN (1.01) |
| 24 | Detroit Red Wings | 3 | 2.95 | 0.98 | vs VAN (1.01)@ CBJ (0.94)vs NSH (1.00) |
| 25 | Montreal Canadiens | 3 | 2.94 | 0.98 | vs LAK (0.96)@ COL (1.00)@ VGK (0.99) |
| 26 | Washington Capitals | 3 | 2.90 | 0.97 | vs PHI (0.95)vs FLA (1.05)@ STL (0.90) |
| 27 | Colorado Avalanche | 3 | 2.88 | 0.96 | vs MTL (0.99)@ MIN (0.92)vs SJS (0.97) |
| 28 | Vegas Golden Knights | 3 | 2.88 | 0.96 | @ EDM (0.91)@ SJS (0.97)vs MTL (0.99) |
| 29 | Dallas Stars | 3 | 2.87 | 0.96 | vs NSH (1.00)@ STL (0.90)vs UTA (0.97) |
| 30 | Los Angeles Kings | 3 | 2.83 | 0.94 | @ MTL (0.99)@ ANA (0.92)vs ANA (0.92) |
| 31 | Buffalo Sabres | 2 | 2.10 | 1.05 | vs NYR (1.06)vs PIT (1.05) |
| 32 | Carolina Hurricanes | 2 | 2.10 | 1.05 | @ TBL (1.04)vs OTT (1.06) |
Projected hits leaders, week 9
Projected totals for the week. Off-night games in green.
| # | Player | GP | HIT |
|---|---|---|---|
| 1 | Jacob MelansonSEA · RW | 41 | 19.1 |
| 2 | Kiefer SherwoodSJS · RW | 41 | 17.9 |
| 3 | Yakov TreninMIN · LW | 42 | 16.6 |
| 4 | Adam KlapkaCGY · RW | 41 | 15.7 |
| 5 | Martin PospisilCGY · LW | 41 | 15.1 |
| 6 | Jack McBainUTA · C | 41 | 14.5 |
| 7 | Marcus FolignoMIN · LW | 42 | 11.8 |
| 8 | Liam O'BrienUTA · LW | 41 | 11.8 |
| 9 | Will CuylleNYR · LW | 3 | 11.7 |
| 10 | Marc GatcombVGK · RW | 31 | 11.4 |
| 11 | Emil HeinemanNYI · LW | 41 | 11.0 |
| 12 | Dylan CozensOTT · C | 41 | 10.8 |
| 13 | Garnet HathawayFLA · RW | 3 | 10.6 |
| 14 | Vincent TrocheckUTA · C | 41 | 10.3 |
| 15 | Cole KoepkeWPG · LW | 41 | 10.1 |
| 16 | Dakota JoshuaTOR · LW | 31 | 10.1 |
| 17 | Jeff MalottANA · LW | 41 | 10.1 |
| 18 | Lawson CrouseUTA · RW | 41 | 10.1 |
| 19 | Mathieu OlivierCBJ · RW | 31 | 10.1 |
| 20 | Colton DachEDM · LW | 31 | 10.0 |
| 21 | A.J. GreerANA · LW | 41 | 10.0 |
| 22 | Michael McCarronMIN · C | 42 | 9.9 |
| 23 | Matthew RempeNYR · RW | 3 | 9.8 |
| 24 | Jeremy LauzonVGK · D | 31 | 9.7 |
| 25 | Tye KartyeNYR · LW | 3 | 9.6 |
| 26 | Jack St. IvanyWPG · D | 41 | 9.4 |
| 27 | Keegan KolesarDET · RW | 3 | 9.4 |
| 28 | Ty DellandreaSJS · C | 41 | 9.3 |
| 29 | Tanner JeannotBOS · LW | 31 | 9.1 |
| 30 | Radko GudasFLA · D | 3 | 9.1 |
| 31 | Jeffrey VielTBL · LW | 3 | 9.0 |
| 32 | Connor CliftonBOS · D | 31 | 9.0 |
| 33 | Ross JohnstonSTL · LW | 3 | 8.9 |
| 34 | Brady TkachukFLA · LW | 3 | 8.9 |
| 35 | MacKenzie WeegarUTA · D | 41 | 8.7 |
| 36 | Luke SchennVAN · D | 31 | 8.7 |
| 37 | Brayden SchennNYI · C | 41 | 8.6 |
| 38 | Vasily PodkolzinEDM · LW | 31 | 8.6 |
| 39 | Lian BichselDAL · D | 3 | 8.4 |
| 40 | Carl GrundstromPHI · LW | 3 | 8.4 |
Matchup models plugged in
- Banger Environment Index: hits and blocksHIT, BLKComing 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 hits, last season's split-half r was 0.81 and the year-over-year r 0.66; factors are shrunk accordingly.
Frequently asked questions
Which teams have the best hits schedule in week 9?
OTT (4 games, 4.22 effective), CGY (4 games, 4.14 effective), MIN (4 games, 4.06 effective), UTA (4 games, 3.96 effective), WPG (4 games, 3.92 effective).
Which teams have the worst hits schedule?
CAR (2 games), BUF (2 games), LAK (3 games), DAL (3 games), VGK (3 games).
How reliable is the opponent effect for hits?
Last season the opponent measure (how many hits the opponent allows) had a split-half correlation of 0.81 across the 32 teams and a year-over-year correlation of 0.66. 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.