I'm usually of the opinion that you can't be totally doom and gloom about a team until around Christmas time. At that point, a team's usually played enough of the season that you can identify trends and habits -- and things don't usually change too much, even with major shakeups like coaching dismissals.
However, the one month mark is still significant in that you have a fairly reasonable sample size to see how this team comes together. Habits are starting to form and patterns are starting to become evident, which means that some people get that pit of dread in their stomach when they watch their team take the ice. How did last year's one-month records compare to the final season outcome? Well, thanks to the magic of Excel, I've compiled those numbers and taken a look.
The table below's win/loss/SOL record is all based on the month of October. The Month % column looks at percentage of possible points in a month, so basically the total points divided by two points per game. The Season column is the final points standings from the 2009-10 season, and the Season % column is the percentage of possible total points (points/164, or two potential points over 82 games). Diff is the important column, as it compares the point percentage from the first month to the season. I've bolded teams that have had a difference of more than 10%.
Hit the jump and see if you can play Puck-stradomos based on one month of the NHL season.
| Team | Win | Loss | SOL | GP | Points | Month % | Season | Season % | Diff |
| Anaheim Ducks | 4 | 6 | 2 | 12 | 10 | 0.416666667 | 89 | 0.542683 | 0.126016 |
| Atlanta Thrashers | 5 | 4 | 1 | 10 | 11 | 0.55 | 83 | 0.506098 | -0.0439 |
| Boston Bruins | 6 | 5 | 1 | 12 | 13 | 0.541666667 | 91 | 0.554878 | 0.013211 |
| Buffalo Sabres | 8 | 2 | 1 | 11 | 17 | 0.772727273 | 100 | 0.609756 | -0.16297 |
| Calgary Flames | 7 | 4 | 1 | 12 | 15 | 0.625 | 90 | 0.54878 | -0.07622 |
| Carolina Hurricanes | 2 | 7 | 3 | 12 | 7 | 0.291666667 | 80 | 0.487805 | 0.196138 |
| Chicago Blackhawks | 8 | 4 | 1 | 13 | 17 | 0.653846154 | 112 | 0.682927 | 0.029081 |
| Colorado Avalanche | 10 | 2 | 2 | 14 | 22 | 0.785714286 | 95 | 0.579268 | -0.20645 |
| Columbus Blue Jackets | 6 | 5 | 1 | 12 | 13 | 0.541666667 | 79 | 0.481707 | -0.05996 |
| Dallas Stars | 6 | 3 | 5 | 14 | 17 | 0.607142857 | 88 | 0.536585 | -0.07056 |
| Detroit Red Wings | 5 | 4 | 3 | 12 | 13 | 0.541666667 | 102 | 0.621951 | 0.080285 |
| Edmonton Oilers | 7 | 6 | 1 | 14 | 15 | 0.535714286 | 62 | 0.378049 | -0.15767 |
| Florida Panthers | 4 | 7 | 1 | 12 | 9 | 0.375 | 77 | 0.469512 | 0.094512 |
| Los Angeles Kings | 8 | 4 | 2 | 14 | 18 | 0.642857143 | 101 | 0.615854 | -0.027 |
| Minnesota Wild | 5 | 9 | 0 | 14 | 10 | 0.357142857 | 84 | 0.512195 | 0.155052 |
| Montreal Canadiens | 7 | 7 | 0 | 14 | 14 | 0.5 | 88 | 0.536585 | 0.036585 |
| Nashville Predators | 6 | 6 | 1 | 13 | 13 | 0.5 | 100 | 0.609756 | 0.109756 |
| New Jersey Devils | 8 | 4 | 0 | 12 | 16 | 0.666666667 | 103 | 0.628049 | -0.03862 |
| New York Islanders | 4 | 4 | 5 | 13 | 13 | 0.5 | 79 | 0.481707 | -0.01829 |
| New York Rangers | 8 | 5 | 1 | 14 | 17 | 0.607142857 | 87 | 0.530488 | -0.07666 |
| Ottawa Senators | 4 | 6 | 1 | 11 | 9 | 0.409090909 | 94 | 0.573171 | 0.16408 |
| Philadelphia Flyers | 6 | 4 | 1 | 11 | 13 | 0.590909091 | 88 | 0.536585 | -0.05432 |
| Phoenix Coyotes | 9 | 4 | 0 | 13 | 18 | 0.692307692 | 107 | 0.652439 | -0.03987 |
| Pittsburgh Penguins | 11 | 3 | 0 | 14 | 22 | 0.785714286 | 101 | 0.615854 | -0.16986 |
| San Jose Sharks | 9 | 4 | 1 | 14 | 19 | 0.678571429 | 113 | 0.689024 | 0.010453 |
| St. Louis Blues | 5 | 6 | 1 | 12 | 11 | 0.458333333 | 90 | 0.54878 | 0.090447 |
| Tampa Bay Lightning | 4 | 3 | 4 | 11 | 12 | 0.545454545 | 80 | 0.487805 | -0.05765 |
| Toronto Maple Leafs | 1 | 7 | 4 | 12 | 6 | 0.25 | 74 | 0.45122 | 0.20122 |
| Vancouver Canucks | 7 | 7 | 0 | 14 | 14 | 0.5 | 103 | 0.628049 | 0.128049 |
| Washington Capitals | 8 | 2 | 3 | 13 | 19 | 0.730769231 | 121 | 0.737805 | 0.007036 |
What does this tell us? About a third of NHL teams (nine total) had their performance change by 10% or more, and most of those teams were on the positive side. The ones that dipped had killer starts that they simply couldn't keep up. Here's the more interesting number: if you take in all of the changes, the average is 0.09, or 9%. That means that on average, there's a significant swing one way or the other -- however, you can see that for the teams that had bad starts, it usually means that it wasn't enough to overcome the hole they dug themselves in the beginning.
The trick, then, is if you have a bad first month, you essentially need to claw your way back to .500 by around mid-December to still have a fighting chance. If your points percentage is just awful for the first month, being mediocre with spurts of good won't get you anywhere.


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