Carolina Hurricanes

What to expect in NHL’s second half

Now that we’re officially more than halfway into the NHL season, we’re starting to get a clearer picture of which teams are contenders and pretenders.

However, there is still a lot of hockey to play, and there will be a fair bit of movement up and down the standings. So what should we expect in the second half?

To answer this question, we gathered data (from and on midseason points, various possession metrics, goal differential and shooting and save percentages for every team after 41 games, starting with the 2007-08 season but excluding the lockout-shortened 2012-13 campaign.

The goal was to see which combination of these variables was best at predicting teams’ second-half point totals.

You’d certainly be excused if you thought that looking at a team’s points in the first half was the best way to predict its second-half points. After 41 games, the standings must reflect a team’s true ability, right?

As it turns out, second-half performance is best predicted by two of the most commonly used variables in the analytics repertoire: Score Adjusted Corsi and PDO.

For those not familiar with these concepts, Corsi is another name for shot attempts (shots on goal, missed shots and blocked shots). “Score Adjusted” Corsi (SAC) is a stat that, as the name suggests, adjusts Corsi to account for the power of score effects. “Score effects” describes the tendency for teams that are trailing in a game to amp up the offense and attempt significantly more shots. This matters because if you look at Corsi without any adjustments, teams that are playing from behind a lot look better than they really are and teams often defending a lead don’t look as strong as they truly are.

PDO is simpler – it is just the sum of a team’s shooting percentage and save percentage. So, for example, if the Carolina Hurricanes have a team shooting percentage of 6.1 and a save percentage of 90.8, their PDO is 96.9.

What was interesting, however, was that basing predictions on two variables did only marginally better than looking at just SAC. The fact SAC is one of the best predictors of future success is a result found by almost everyone doing hockey analytics. In general, however, predictions usually can be improved by using more information. In this particular case, what we find is that second-half points are very hard to predict, and that, to the extent we can predict them, SAC really is all you need.

Also interesting, to the extent a second variable can improve predictive power, is PDO is the best choice. Note that, when considering PDO in isolation, it has very little predictive value. This generally is thought to be because a team’s shooting and save percentage at the midseason point are very noisy measures of its true abilities in that regard. However, it turns out the information embedded in PDO, shrouded in noise as it might be, is different enough from the information embedded in SAC, that it is the best complement.

Regardless, the predictions generated by these variables still leave a lot of room for error. In recent seasons, teams have produced as many as 67 points in the second half (the 2009-10 Washington Capitals), or as few as 21 (the 2010-11 Colorado Avalanche), and the model can explain only about 20 percent of that variation. However, they’re still the best data (out of what we looked at) to base predictions. So what do they predict?

First, the playoff teams will be the ones that held such a spot at the halfway mark. No surprise there, but what is interesting is the significant shuffling of seeding. Nashville should cool off from its torrid start (60 points in the first half) but still uis predicted to clinch the best record in the league, just barely holding off Chicago. Buffalo, meanwhile, is predicted to get the best shot at Connor McDavid in the lottery as Edmonton is predicted to recover from its disastrous first half by posting a much improved second-half record (better than five other teams, including Calgary and Toronto). The Oilers, along with Carolina, are predicted to show the most improvement in the second half, bettering their first-half totals by 16 points each. Edmonton will, however, still end up with the second-worst record, and Carolina will be third worst. The biggest dropoffs are for Anaheim (predicted to amass 12 fewer second-half points than it did in the first half) and Montreal (11 fewer).

At the end of the day, however, a good takeaway from this is that second-half performance is surprisingly difficult to predict. You might think you have a good handle on your team, but chances are things won’t play out as they did in the first half. Which is just as well – isn’t that why we watch?

For more information on the methodology, go to