Archive for Analysis


A look at New York Rangers scoring chances

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As you know, we focus a lot around using both qualitative (eye test) and quantitative (#fancystats) analysis around here. Both have their pros and cons, and both should be used when evaluating talent and performance. These are not mutually exclusive measures.

One of the major drawbacks of #fancystats –specifically Corsi/Fenwick– is that it doesn’t measure the quality of the shot. While shot quality has been proven to be an unrepeatable skill, meaning we can’t quantify it at a statistical level, it doesn’t take away its importance. Luckily, the folks over at the indispensable war-on-ice have come to our aid with their scoring chance stat.

The Definition

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Categories : Analysis
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As we continue to digest last night’s win over the Kings (is winning getting boring, yet?), I thought we could go off-topic on this snowy Friday afternoon. For our regular readers, you know that I have been clamoring for a more comprehensive goaltending statistic than our standard rate stats; GAA and save percentage. As the world of Corsi and Fenwick and the like continue to evolve, goaltending statistics remain woefully underdeveloped. Enter former Rangers goaltender Steve Valiquette, who in addition to Sportsnet’s Chris Boyle (Shot Quality Project), have begun comprehensive research into how shot quality effects goaltender performance. (s/t to Kevin Power)

Every time a new goalie metric is conceptually introduced, I am forced to feel like an ungrateful cynic. Smart, hardworking, dedicated people are attempting to give me what I’ve asked for. Maybe it’s the delivery; “Steve Valiquette is going to change the way we think about goaltending!”, “This new statistic is going to revolutionize goaltending!”. No, it’s not. But it’s a great start.

MSG Networks

MSG Networks

I don’t want to get into the mechanics too much, but here is pretty much how Valiquette’s theory works:

  • The zone is divided in half, vertically from the center bar of the net to the top of the circles, and is bisected by a horizontal line going laterally across the top of the circles.
  • The centerline is Valiquette’s “Royal Road”. This line represents the lateral marker a puck travels across within that zone, which leads to higher percentage shots.
  • The types of shots are broken down into “Green” and “Red” shots.
  • Green shots include
    • Possession across “Royal Road”
    • Passes across “Royal Road”
    • Deflections
    • One-timers on same side of “Royal Road”
    • Screens
    • Broken plays
    • Green Rebounds
  • Red shots include
    • Shots from outside the designated area
    • Red rebounds
  • Green shots have accounted for roughly 76% of NHL goals this season, and are obviously converted at a much higher rate than Red shots (24%).

Ok, with that out of the way, here is my take. I think it’s fantastic the work Valiquette is putting in. I hope that is leads to a wealth of new information about how goaltending is evaluated. Once you get past all the terminology, his theory is pretty simple: shots that come what we consider “dangerous” areas of the ice are converted at a higher rate, more so if the goaltender is forced to move laterally. There is also a much higher chance of rebounds, deflections and scrambles resulting in goals than shots from the perimeter.

This all seems pretty obvious, no? I don’t mean to sound overly negative, but it seems to dress up a lot of concepts we simply take for granted, even if they aren’t currently quantified. Don’t get me wrong, I think this tracking concept has a ton of value as a foundation to a more comprehensive, value-based statistic (any statisticians out there, hit me up. I’ve got ideas).

I think that the greatest value this methodology has is situational analysis of current form. Craig Custance over at ESPN spoke to Valiquette about it (Insider post), and applied the method to Dallas goaltender Kari Lehtonen to evaluate whether his poor (relatively) rate stats this year are a true dip in form or whether he is getting hung out to dry. This is a really effective use of this statistic, but there is no basis for comparison that you can really hang your hat on to assess value.

Either way, this is a big step in the right direction for the continued evolution of advanced statistics. I believe a foundational concept now exists to build on, from a quantitative standpoint, and that is incredibly impressive in itself. But, as I said with GSAA, it’s a great start, and an evolution, but certainly not a revolution.

Categories : Analysis, Goaltending
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We’ve seen this narrative play out before haven’t we? Popular players in contract years can never seem to avoid fan scrutiny, beat writer adoration, or trade rumors. It’s Cally, Girardi, Henrik, etc. all over again, except this season it’s Marc Staal.

Over the past few weeks, the conversation around Staal has started to heat up. He’s been described as ‘untouchable’ by some and a ‘tire fire’ by others. Somewhere between extremes is where reality usually lies.

Before we evaluate whether or not to resign, trade, or let Marc walk, we have to define what his role will be moving forward. From there we can analyze if there are adequate replacements inside or outside the organization.

Staal’s role

Last spring Marc described his role within AV’s team concept to Steve Serby of the NYPost.

Defensive defenseman. I take care of my own end … try to be great positionally and have a good stick, and make sure I’m getting out of my end quickly, not spending a lot of time there … get transition, give it to the forwards, and let them do their thing.”

Roles like these are always tough to quantify, especially for players like Staal who are typically deployed in their own end zone, against top scoring lines, and don’t contribute much offensively.

Fortunately, has begun tracking shots in the slot/hextally figures and scoring chances – long overdue in my opinion – which gives us a decent view of Staal’s effectiveness.

Some stats

So far this season, Staal’s even-strength scoring chances against (per 60 minutes of playing time) is 25.60, which is right in line with his career average. However, his scoring chances for (per 60 minutes of playing time) is 24.80, which is well below his career average (27.9).

Obviously there are many factors at play here, but the macro takeaway is that he’s still solid in his own zone defensively, but perhaps not at getting the puck up ice. Whether or not this is a blip on the radar or a trend remains to be seen. However, it seems his play is heading in the right direction after a tough stretch between mid-November and mid-December.

Screen Shot 2015-01-03 at 6.14.46 PM

On Ice Scoring Chance Differential

Staal’s value

With limited offensive potential, Staal’s value is ultimately going to be determined by whether or not GMs view him as a first or second pairing defensemen. If they believe him to be a first pairing defensemen, he could probably get $5.5-$5.9M per year for 5-6 years, which is about what most defensemen in his role and age range have been garnering (e.g., Seabrook, Girardi, Carle, etc.).

If they view him as a second pairing defensemen, he’s probably looking at $4.5M-$4.9M and a similar term. Again, this is looking at recent contracts for defensemen in similar roles and age range (e.g., Stralman, Tyutin, Goligoski, etc.).

Staal’s Replacement

I always get ragged on for suggesting that Marc could join his brothers in Carolina. I just have a hard time seeing him sign anywhere else if he doesn’t re-up with NY. The Canes defense is aging and mediocre. They don’t have any d-men (other than Falk) locked up long-term. More importantly, Eric and Jordan have NTCs.

Trading Staal pre-free agency would make sense, but I’m sure every GM is aware of the possibility he could head south this summer. Knowing this, the most we could probably get in return is a pick or a prospect, neither of which help us win a Cup this year.

As far as internal options go, McIlrath (currently in Hartford) is probably a bottom pairing defensemen if he even makes it to Broadway. John Moore still has a ways to go if we’re going to bump him up from the third pairing. Connor Allen (also in Hartford) is probably more of a backup for Moore than a replacement for Staal. Brady Skjei (NYR 1st rounder in 2012) is the likely replacement, but he’s still a year or two away.

The Final Word

Ultimately, if you don’t want to resign Staal for the terms described above, you’re probably looking at a stopgap solution via free agency or an offseason trade until Brady Skjei can take the reigns.

Categories : Analysis, Players
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One of the major drawbacks of using raw puck possession (Corsi/Fenwick) numbers is that it doesn’t account for game and score situations. More accurate measures have been made available by using these numbers at even strength –where most of the game is played– and in score close situations. Score close –where the game is within one goal in the first two periods or tied in the third– is what has been the driving force for the predictive measure of the stats.

Score close situations wasn’t perfect because it limits the number of events recorded, but since it was the most accurate metric that was readily available, we leveraged it*. The most accurate has always been score adjusted puck possession, but it was only available via manual calculations. Luckily, the indispensable war-on-ice added this calculation to their page, and now we can use this to more accurately assess player performance.

*-It’s worth noting that we are limited by not only what is available, but what is available for us to analyze/evaluate. Several intelligent people have manually tracked/adjusted some of these stats, but it’s not something that has been available for all due to the manual nature of that tracking.  There will be many more advancements as more data –specifically player movement tracking– is available.

The preference for score adjusted puck possession is still the same as with regular (non-score adjusted) possession: You want to be on the positive side of 50% for CF%/FF% and on the positive side of 0%. I sorted by difference between score adjusted and raw (right column) to show how the numbers were effected. First the forwards:

Skater ES rel CF% ES rel SA CF% Difference
Fast -6.1 -5.2 0.9
Glass -6.3 -5.7 0.6
Moore 0.1 0.5 0.4
Kreider 1.3 1.6 0.3
Hagelin 1.8 1.8 0
Stepan -5.7 -5.7 0
St. Louis -2.9 -3 -0.1
Miller 5.1 4.9 -0.2
Brassard 0.2 0 -0.2
Zuccarello 4.2 4 -0.2
Stempniak 4.7 4.4 -0.3
Duclair 0.3 0 -0.3
Hayes 2.1 1.8 -0.3
Nash -0.1 -0.6 -0.5

The most interesting aspect here is that Jesper Fast has the biggest improvement when taking score into effect. His numbers are weighted down by 25% offensive zone starts though. Tanner Glass is at least a pleasant surprise at the second largest differential, but he’s seeing 45% OZ starts, so it’s less impressive than Fast and Dominic Moore’s improvements.

The person on this list I’m worried about the most is Derek Stepan, who is having a Tyler Bozakian season, and that’s not a good thing. He’s putting up numbers, but he’s been off this year.

Now the defense:

Skater ES rel CF% ES rel SA CF% Difference
Kostka 4.8 5.5 0.7
McDonagh -4.3 -3.8 0.5
Klein 4.1 4.3 0.2
Staal -2.2 -2.1 0.1
Girardi -7 -7 0
Moore 2 1.6 -0.4
Hunwick 3.8 3.2 -0.6
Boyle 2.7 2.1 -0.6

Mike Kostka got a bad rap with those awful turnovers during his short stay. The eye test catches big things like that, but not the small, subtle things done regularly. I’m not saying Kostka should be in the lineup, I’m just saying he wasn’t the huge tire fire everyone made him out to be. Unsurprisingly, Ryan McDonagh takes the biggest leap forward among the regulars (these tables contain players with > 100 minutes played).

My biggest concern, aside from Girardi, is Dan Boyle. Boyle has been underwhelming this season, and his recent demotion to bottom pairing has been evidence of that. There have been rumblings that he’s playing hurt, and I hope that’s the case since his play has been poor this year. The Rangers have been able to overcome Boyle’s ineffectiveness because of the impressive play of Kevin Klein.

Going forward, we are going to use score adjusted numbers to analyze players and teams, since they are a more accurate representation of a player’s effectiveness. However like all possession numbers, deployment –zone starts and quality of competition/teammates– needs to be factored into the equation as well.

Categories : Analysis
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In case you missed it yesterday, I was asked why the Rangers kept Dan Girardi over Anton Stralman. After going into qualitative (eye test) and quantitative (#fancystats) analysis, comparing Girardi to Stralman, it was clear to see that Stralman was always the better choice. However, one thing that was not as clear was whether the Rangers actually chose Girardi over Stralman. It’s something I noted in the last line of that post:

It’s worth noting that I think the Rangers chose Boyle over Stralman, and wanted to keep Girardi regardless.

It makes sense that the Rangers didn’t want to deal their captain (Ryan Callahan) and another leader in Girardi that would be given the ‘A’ following the eventual buyout of Brad Richards. So, keeping Girardi was always, in my eyes, in their plan. However, keeping Girardi did not address the obvious need for a defenseman that could run the powerplay. With Dan Boyle on his way out in San Jose, the fit was obvious.

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I received one question this week in the mailbag, and it was a doozy, so this entire post is dedicated to it. As always, email me if you have any questions, and I’ll be sure to address each one.

BrooklynVic asks: Why did the Rangers keep Dan Girardi over Anton Stralman? How could our valuation of Stralman been so undervalued considering how he is playing now? 

BV, this is a loaded question, to which we can break it down into several parts. Addressing the first part is the easy part: Keeping Girardi over Stralman had a lot to do with perceived value versus market value, and the writing was on the wall when Stralman rejected the Rangers offer of three years, $9 million. The Rangers valued him at  $4 million, which is what Larry Brooks reported. That was $500,000 less than what he got from Tampa Bay over five years. The Rangers didn’t want to go that long or that high.

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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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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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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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Categories : Analysis, Goaltending
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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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