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Category: Analysis

Last year’s positive regression does not apply to this year’s Rangers

Did you know that Staal has brothers? (Photo by Jim McIsaac/Getty Images)

Photo by Jim McIsaac/Getty Images

In case you’ve missed it, and if you’ve been watching lately, you haven’t, the New York Rangers are playing some pretty inconsistent hockey. Some point to last year’s start as a sign of hope, but this is an entirely different situation from last year. The Rangers had solid possession numbers last year at this time, but were experiencing a combination of bad luck (bad SV% and SH%) and learning a new defensive zone coverage system.

This year, the Rangers are one of the worst possession teams in the league (20th – 49.5% FF close), but their PDO (combination of SH%+SV%, which usually regresses to 1000) is at 1000, meaning they likely won’t get any better there. Even when Ryan McDonagh comes back to the lineup, he’s replacing Matt Hunwick, who’s actually been one of the better defenders from a possession standpoint.

Long story short: That impressive streak we saw from January through the end of the year last season may not come this year. In fact, it likely won’t come.

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Six weeks of unsustainable SH% for four Rangers

NYPOST.COM

The Rangers were absolutely decimated by injuries in October and November, at one point skating without their top line center and four of their top-six defensemen. When you’re dressing options seven, eight, nine, and ten on the blue line, you need better contributions from those in the lineup. Luckily for the Rangers, four guys stepped up their games offensively to fill the void. Rick Nash (12-6-18), Derick Brassard (6-7-13), Martin St. Louis (6-6-12), and Kevin Klein (3-2-5) were major contributors in keeping the Rangers afloat.

Nash has been a force all season, looking like the Rick Nash the Rangers traded for in 2012. MSL has been contributing at his normal career pace despite playing a good number of the games at center this year. Brassard has shown great chemistry with them as well, looking like that $5 million center they re-signed in the offseason. As for Klein, he’s never cracked the five goal mark in a season, and he looks to be well on his way this year.

But these four players have one thing in common – unsustainable SH%, that is surely to regress as the season bears on.

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How hard is regression going to hit Cam Talbot?

Bruce Bennett/Getty Images

Bruce Bennett/Getty Images

There was a time last season that Henrik Lundqvist was playing so poorly, and Cam Talbot was playing so well, that a very small but very vocal segment of the fan base was calling for a change at the number one spot. Imagine that. Crazy, right? But, it happened. Small sample sizes can do wacky things to people’s perceptions. Talbot had a phenomenal 2013-2014 season, but has struggled so far (relatively speaking) in the new campaign.

Last year, Talbot ended the season with a 1.64 GAA and a .941 save percentage in 21 games played. If he had put up those numbers over a starter’s workload, he would have run away with the Vezina. We all knew (hopefully) that these flawed metrics, although nice to see from our backup, were not reflective of his true talent level. In fairness, they aren’t reflective of anyone’s true talent level.

In 4 games so far this season, Talbot’s GAA has ballooned to 3.48 and his save percentage has slid to .880. Neither of those numbers are particularly pretty. I’ve seen comments on the Twitters and other social media about how hard regression is hitting Talbot, which naturally begs the question: what is the mean he is regressing to?

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Measuring defense: Relative Corsi and Corsi Against

Dan Girardi: once again, Mr dependable. Photo: McIsaac/Getty

Photo: McIsaac/Getty

If you’ve been reading this blog for a while, you know that we like to combine what we see in the goal breakdowns with what we see in the stats. We are a bit limited by time constraints (thus no gifs or pictures of non-goal events), but we point out various areas where the Rangers have defensive breakdowns that lead to goals. While only pointing out goals is limited –If someone wants to pay me to do this full-time, I’ll gladly point out more plays. But I don’t have the time. So those of you complaining, quit it, it’s annoying and rather pointless– it is useful.

The defensive breakdowns are what we see with the eye test. And while the eye test should never be ignored, it does tend to focus on big screw-ups and lend itself to bias. It’s why defense should also be measured by two key metrics: One that we use regularly (relative Corsi) and one that we admittedly don’t use enough (Corsi Against).

Relative Corsi you are familiar with, as it measures possession on the ice relative to the rest of the team when off the ice. The concept here is that the higher the number, the more time you have the puck in the offensive zone, thus limiting opposition opportunities. Corsi Against measures shot suppression, which is a measure of relative Corsi, and shows us the shot attempts against while on the ice.

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The Rangers are bad at faceoffs, but how bad is the effect?

Dom Moore was brought back on a sensible deal - careful planning? (Photo: Dave Sandford/NHLI)

Photo: Dave Sandford/NHLI

I’m sure you’ve noticed this by now, but the New York Rangers stink on faceoffs. They are at 47.2% right now as a team. Of the players that have taken at least 50 faceoffs, only Derick Brassard (55.4%) and Dominic Moore (53.3%) are above 50%. Martin St. Louis (43.6%) and Kevin Hates (24.6%, ouch) bring up the rear for players that have taken 50 faceoffs. Derek Stepan isn’t a 50% faceoff guy, so his return won’t really help in that department.

But how much does this affect the on-ice product?

Statistical Sports Consulting printed a study on the effect of faceoffs on goals, and the results are pretty interesting. They first measured the faceoff differential to yield a goal differential, then measured the probability of winning a faceoff (Note: Not 50/50, there’s skill involved).

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Building a better goaltending statistic

Wikipedia.org

Wikipedia.org

Over the past few years, the debate has grown more intense about the validity and reliance on #fancystats. The concept of quantifying the game has been a theme we have run with around here, albeit with the conceit that there is no perfect, all-knowing stat that can be universally relied upon to demonstrate a player’s ability level.

Statistics trying to quantify human athletic performance are inherently limited. There are very human characteristics in play; such as intelligence, judgment, emotion, situational awareness, etc. It makes it difficult to measure performance as if they were vital signs. I think that to fully expect that level of quantification or to vilify the statistic for being unable to is missing the point.

Much like politics, I think the emergence of these statistics and the resistance to adoption has pushed the two positions out to the extremes. The old school hockey community has written them off or marginalized their effectiveness, citing “games are played on the ice, not on a spreadsheet”, or taking pot shots at the Maple Leafs for hiring Kyle Dubas for their Assistant GM position, and various stats writers to make up a new analytics department. Read more »

New Rangers lines expecting different deployment strategies

Rick Nash is Canadian for frustrating.

Really need a big year from you buddy.

If there was one thing proven about Alain Vigneault’s coaching style over the years, it’s that he tactically deploys his lines (after a whistle) depending on the zone start and the opposing line sent out. Last year we saw the Brian Boyle-Dominic Moore-Derek Dorsett line get the majority of their starts in the defensive zone, while the Mats Zuccarello-Derick Brassard-Benoit Pouliot line received primarily offensive zone starts. It is a common practice among coaches.

This year the lines have been shaken up with the turnover, and the matchups will likely change, both in zone starts and in quality of competition faced. Using the lines from practice the past few days, we can make some guesses.

Chris Kreider-Martin St. Louis-Rick Nash

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A glance at the score sheet from last night’s game

miller

Miller

Obviously a lot of these stats can be taken with a grain (or mountain) of salt and I unfortunately do not have access to the “fancier” stats but I wanted to point out a few things that stood out from last night’s score sheet:

  • The Rangers Fenwick For (FF) was 33 and their Corsi For (CF) was 38. The Devils FF was 45 and their CF was 58.
  • The Rangers penalty kill allowed 1 goal that came from a 5-on-3 and killed off six 5v4 power play chances for the Devils allowing 6 shots.
  • Ryan Malone led the Rangers with 4 shots in 12:33 TOI. Three Rangers had 3 shots. (St. Louis, Bourque and Kristo).
  • Kevin Hayes was 6/11 in faceoffs, J.T. Miller was 8/18, Chris Mueller was 8/15 and Matthew Lombardi was 6/14.
  • The Rangers as a team won 57% of their PP draws and 57% of their short handed draws. At even strength they won 44% and their total for the game was 48%.

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Taking a look at John Moore’s 2013/14 season

john moore

Incase you missed it the Rangers and defenseman John Moore finally reached an agreement on a one-year $850,500 contract on Wednesday. Moore, who is slotted in to the Rangers bottom defensive pairing once again, will be entering his fourth NHL season in 2014/15. At 23 years old (24 in November), the 2009 first round draft pick is running out of time to make me, amongst others, find confidence in his ability to blossom into the top four defenseman we were told he could be.

After noticing a lot of debate amongst Rangers fans I decided to take a closer look at Moore’s 5v5 metrics (his PP TOI was too small a sample for me to consider of any value).

The first thing that pops out when looking at Moore’s 5v5 numbers is his zone start percentage (ZS%) of 64%. Amongst 2013/14 Metropolitan Division defensemen this was the highest ZS% for anyone. Moore should have been able to thrive being placed in the offensive zone as often as he was, however his result was less than impressive. Moore held a corsi for percentage (CF%) of 51.7% with his relative CF% (relCF%) at -1.26%, an awful number when taking into account how he was handed the offensive zone on a silver platter. This is not out of the ordinary for Moore, who has failed to record a positive relCF% since breaking into the league with regular playing time in 2011/12 .

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Tracking the Rangers zone entries & exits for the upcoming season

Mats Zuccarello

Over the course of the summer there has been one major topic that continues to pop up in the hockey analytics world: tracking zone entries and exits. The concept was originally brought to the forefront of the analytics community by blogger turned NHL consultant, Eric Tulsky. From there the idea of tracking these events took off, most noticeably with Corey Sznajder (creator of the Hurricanes’ blog “Shutdown Line”) who undertook a huge workload this summer by tracking every entry and exit from every NHL game of the 2013-14 season.

That’s just a little bit of the background, but why is tracking entries and exits so important?

The days of chip and chase hockey are slowly dying. Just like everything else in this world, hockey is evolving. Many teams have hired people to head up analytics departments this summer, including the Rangers (Jim Sullivan). Puck possession is as valuable as anything else in the game. The belief is that carrying the puck into the offensive zone is a more efficient play than dumping and chasing, and ultimately results in more shot attempts. At the same time, carrying the puck out of your own end results in more success through the neutral zone than stretch passes through multiple opponents. By tracking these events game by game we will be able to get a sense of which players can sustain high possession numbers and contribute to a teams shot total, which in turn will contribute to goals scored.

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