At this time of year team records can be deceiving.

Every year some teams get off to fast starts but then collapse, and others start slow but work their way into a playoff position by season’s end.

Last year saw the Phoenix (now Arizona) Coyotes come storming out of the gate to get 34 points in their first 25 games -- tied for the 7th-best start in the league -- only to end up out of the playoffs with 89 points.

In the other direction, Columbus and Philadelphia started last year with 21 and 24 points, respectively, and worked their way up to 93 and 94 points, and resulting playoff berths.

So what happened?

One of the biggest contributions of the current analytics movement has been to emphasize that teams can go on hot runs over relatively large stretches while being largely outplayed, but that such success is generally not sustainable for a full season. Conversely, teams can have prolonged stretches where the points don’t come, even though they’ve generally been the better team. But chances are that before long, the points will start to come for those clubs.

This isn’t to say that a team’s record after 25 games doesn’t matter; far from it. But models that include both points and possession measures do significantly better in explaining who made the playoffs and who didn’t. Specifically, a model with both points and Score Adjusted Corsi fits the data much better than models with just one variable, and better than any of the models with two variables that were looked at.

For those not familiar with Score Adjusted Corsi, it is a possession metric that reflects the fact that teams generally do better in terms of possession when they’re trailing (or worse when they’re ahead) – a phenomenon called “score effects” (see IJay’s Nov. 14 column for more on that point). Thus, some teams’ possession metrics look better than they really are simply because they play from behind a lot, while others look worse because they have the lead a lot.

From the model using both points and Score Adjusted Corsi, we found that on average, each additional point a team has at the 25-game mark increased their chance of making the playoffs by about 7.2 percentage points; each percentage point increase in their Score Adjusted Corsi increased their chance of making the playoffs by 8.1 percentage points.

So what does that mean for this year’s teams? We can generate probabilities for each team of making the post-season this year based on data from past seasons. Keep in mind that, as mentioned above, the model only accounts for 42 per cent of the information that one would ideally like to have, and so should be taken with a grain of salt. For example, it doesn’t account for teams that have above-average shooting or goaltending, nor does it take into consideration the information contained in the games that teams have played past the 25-game mark. Unfortunately, it does not even account for the recent change in playoff format, with divisions and wildcards. However, it does give insight as to who are likely candidates for collapses and who might yet climb up the standings.

According to the model, the team most likely to collapse is Calgary, with its chance of making the playoffs being only 34.6 per cent, even though it had 32 points after 25 games, good for sixth-best in the West and third in its division.

In the East, Tampa Bay, Montreal and Detroit were sitting in the divisionally guaranteed playoff positions after 25 games, while Boston, Toronto and Florida were all tied with 29 points. Only two of those teams could make the playoffs as a wildcard, however, as either the Rangers or the Capitals would get the nod by virtue of being third in the Metropolitan. According to the model, Toronto would be the odds-on favourite to be on the outside looking in (again), while Washington would be favoured to get that third divisional spot.

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With just over a third of the season left to play, things can (and almost certainly will) change between now and the end of the season - but already there are some indications that all is not as the standings suggest. We’ll check back at the midseason point to see how things are progressing.

Data was taken from www.puckon.net . For more on the predictive properties of the various statistics, as well as the method used to generate these probabilities, go to http://www.depthockeyanalytics.com .

The Department of Hockey Analytics employs advanced statistical methods and innovative approaches to better understand the game of hockey. Its three founders are Ian Cooper, a lawyer, former player agent and Wharton Business School graduate; Dr. Phil Curry, a professor of economics at the University of Waterloo; and IJay Palansky, a litigator at the law firm of Armstrong Teasdale, former high-stakes professional poker player, and Harvard Law School graduate. Visit us on line at www.depthockeyanalytics.com