NHL Advanced Stats: Small Sample Application Example

Title: NHL Advanced Stats: Small Sample Application Example
Date: January 26, 2013
Original Source: Nucks Misconduct
Synopsis: My latest article at NM was a final application of my advanced stats primer, highlighting how the Canucks had made out through four games in terms of advanced stats.

So, as we get rolling into the NHL season here, I thought it would be a good opportunity to further cement our learning about Advanced NHL Stats from my articles below:


Applying to the Second Line Centre

In the first article, I laid out the advanced stats I will reference at times this year. In the second, I used those stats to evaluate second line centre candidates. It was in imperfect example, but showed how the stats can be deployed in terms of player evaluation.

One issue with these stats, though, is that it’s difficult to use them for narrative purposes – one game is too small a sample size to see any trend, but after too many games we tend to forget what we’ve seen earlier in favour of what we’ve seen recently.

Four games in, I thought it would be helpful to peruse the stats and see how they compare to what we may have thought while watching the poor start. As a reminder, the Canucks are 2-1-1 and have outscored opponents 13-12 thus far, and Daniel Sedin leads the blue and green with six points.

Note: The goal of this is not to make conclusions about individual players. The sample is just too small right now. The goal is to further prime you for advanced stats by using a recent and memorable example, even if it’s not a statistically significant one (although, in a 48-game season, there is really no sample too small).

Important Note #2: All the stats cited below are for 5-on-5 play only and are from Behind the Net.




Reminder: CORSI is basically a plus-minus rating based on shots instead of goals.
Through Four Games: It’s not really much of a surprise, but the Sedins have been very good at generating more chances than they allow when on the ice, and Zack Kassian has benefited from playing alongside them in this regard. On the point, Keith Ballard and Chris Tanev have been the most successful defensemen. Because the Canucks, as a team, have out chanced opponents by a large margin, only a few players have negative CORSI scores – Max Lapierre and Jannik Hansen are slight negatives up front, while Chris Higgins, Alex Burrows, Dan Hamhuis and Kevin Bieksa have struggled in this regard. We will see, though, that there are mitigating factors for players on both ends of the spectrum.

Reminder: This is the CORSI REL score of the players that a player played against. We can use this to judge which Canucks were playing against the toughest opposition (later in the year, we will use CORSI QOC alone, but it doesn’t tell us much yet).
Through Four Games: If you guessed that the Sedins have been playing against cupcake competition, you’d be wrong. We’ll see in a bit how they’re able to post such high CORSI scores, but it’s not because of the opponents they play against, who appear to be the second best opposing line, on average. It’s Burrows, Higgins and Hansen who have drawn the toughest assignments among forwards, which explains some of their poor CORSI rating. On defense, Hamhuis and Bieksa don’t get much of a reprieve here, but we do see that Ballard and Tanev have gotten a huge hand by way of easier competition. Jason Garrison and Alex Edler have faced the toughest opposition of any Canuck players this year.

Zone Start and Zone Finish
Reminder: These percentage stats show the frequency with which players started and ended their shifts in the opposing team’s zone.
Through Four Games: And if you thought the Sedins could post such strong CORSI scores because they’re only deployed in the offensive zone, you’ve been paying attention and win a cookie. The Sedins have started roughly 80% of their shifts in the offensive zone, which is an obscene number. Meanwhile, Lapierre and Andrew Ebbett have started roughly 60% of their shifts in the defensive zone. Because the Canucks have played well and dominated possession, most players have a Zone Start % above 50% right now, but this stat will tell us a lot about player roles later in the year. In terms of Zone Finish %, we see that Tanev and Ballard have been solid at moving the puck to the other end of the ice, as have Malhotra, Lapierre and Hansen. Only Burrows and Garrison have really struggled and allowed the play to flow the other way.

Reminder: PDO sums the shooting percentage of the team and the save percentage of the team when a player is on the ice. The stat is scaled to 1000, so someone with a PDO of 1050 has been fortunate, while someone with a PDO of 975 has been somewhat unfortunate.
Through Four Games: I don’t love PDO in general, but it’s actually pretty telling in small sample sizes, since players and goalies won’t perform at ridiculous extremes for long. For the Canucks, it’s been Hansen, Lapierre and Henrik who have been fairly lucky in this regard, with goalies posting incredible save percentages when they’re on the ice. Some of this is talent-related, but a lot of it, especially this early, is just luck. On the other end, Aaron Volpatti, Dale Weise and Malhotra have been extremely unlucky, with the team’s keepers posting save percentages below .870 when they’re on the ice (and of course, Weise and Malhotra haven’t been on for a goal scored, so the team’s shooting percentage when they’re on the ice is 0).

So there you go, that’s how the Canucks stack up through advanced stats so far at the individual level. At the team level, what we can see from these quick summaries is that the Canucks are doing a very good job keeping the play in the opposition’s end and generating more scoring chances, and the fact that they’ve allowed one more even strength goal than they’ve scored is something that is likely to correct itself. We can look at special teams in the future, but these are generally a lot more straight-forward and teams find their natural PP/PK talent level pretty quickly.

Yes, four games is a dangerously small sample size to make any conclusions from, but it also happens to be about eight percent of the 2013 NHL season. I just thought a third and final “application primer” was in order so that when I reference advanced stats in the future, I can simply just link back to these three pieces. Hopefully everyone has learned at least a bit more about advanced NHL stats, where to find them and how to apply them over the past couple weeks.

If you have questions, want further clarification or want to see any other statistical areas tackled in the future, comment/email/tweet me and I’d be happy to oblige. From here, I’ll likely transition into some more “standard” Canucks analysis like the rest of this awesome crew here at NM.

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