Remembering Dellow: A few graphs to convince you on Corsi

From Wikipedia Commons

Over the past year, I based a lot of research off of  former work by Tyler Dellow. It is a bit funny because I actually never read any of Dellow’s work until well after I started writing about underlying metrics in hockey. I knew of him, but mostly was brought up on Gabriel Desjardins, Eric Tulsky, Ben Wendorf (yes, Hockey-Graphs’ own Wendorff), and a few others. It is also a bit difficult now because Dellow’s website has gone dark with his hiring, which removed the work I quoted or built upon.

One Dellow article that will be severely missed is Two Graphs and 480 words will convince you on Corsi.

Dellow presented analytical data in simple and effective ways. It made understanding of complex concepts -such as regression in goal differentials- easy.

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Scoring talent influence on goal differentials and statistical double dipping

Screen shot 2014-09-09 at 1.20.24 AM

In August, I wrote an article on how you can translate Corsi differential values in terms of the average expected goal differential given for a players of similar average ice time.

In the article, I used an example of how this information could be used:

For example, Matt Halischuk and Eric Tangradi are two players who averaged 4th line minutes on the Winnipeg Jets. Tangradi finished the season with a 53.9% Corsi, while Halischuk was at 44.0%. Over the span of a season, forwards with those Corsi% would be expected to have on average of -1.04 and a -4.77 goal differential respectively. Therefore, on average, a 53.9% Corsi fourth line forward is worth 3.73 goals more than a 44.0% Corsi forward. Another option is comparing these players to the 46.8% Corsi% of an average fourth line player. The goal differentials can then be used to estimate win values using Pythagorean relationships.

There is a caveat with using raw Corsi% to estimate goal differentials; all effects -such as zone starts- still apply. The estimated goal differentials would be no more predictive than Corsi is; however, you can now easily and more accurately measure Corsi impact in terms of goals and wins.

Now, I used the example of Halischuk and Tangradi for a few reasons. The main one being that they are familiar to me as Winnipeg Jets players. They are two fourth line players that have experienced similar usage but have very polar opposite shot metrics. But, there is another reason… an interesting one.

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An early look into some of the new numbers available

From Wikipedia Commons: A graph showing the minimum value of Pearson’s correlation coefficient that is significantly different from zero at the 0.05 level, for a given sample size.

There are two new and very exciting frontiers being explored by the hockey analytics blogosphere. There is the manual tracking of zonal statistics, such as zone entries and exits. This area of research was first pioneered by Eric Tulsky and Corey Sznajder. Then there is the splicing of Corsi into microstates, such as looking at shot attempt differentials momentarily after face off wins or loses in particular zones. The early workers on these numbers were Tyler Delow and Muneeb Alam.
(side note: it should not be a surprise that one of each group was recently picked up by a NHL team this summer)

I recently was able to get data from the non-NHL hires named above (and will enjoy their contact while I can until they are picked up too). Sznajder provided me with zone entry and exit data for just over 60% of the NHL. If you would like to check out his project and contribute, check this link. Alam sent over shot attempt events 10 seconds after a defensive zone face off, which was further separated into wins and losses.

I originally received this data for study of the Jets and noticed what appeared to the eye to be a relationship, and wished to delve in further.

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Value of Corsi possession measured in goals

The average on-ice shooting and save percentages a player experiences tends to be influenced by their average time on ice per game. This relationship likely occurs due to a combination of factors: shooting talents of linemates and opponent, defensive talents of linemates and opponent, system and psychological effects, and an effect I like to call “streak effects”.
(See bottom for discussion on these effects)

Regardless of the reasons why, these effects indicate that not all Corsi percentages are created equal in impact. This has been discussed previously on Hockey-Graphs both here and here. So, can we measure this difference in impact? Continue reading

How well do Plus Possession Rookie D-Men do in their next few years?

There is nothing perhaps more encouraging to fans of struggling teams than to see a rookie D-Man come up and put up big numbers right out of the gate.  I speak of course, not just about goals and assists – in this case I refer to good possession #s (Corsi, Fenwick, and the relative versions thereabout).  Fans of the Oilers (Marincin), Leafs (Rielly), Isles (de Haan, Donovan), etc, all seem to have higher hopes than they might’ve otherwise due to how well their rookie D has performed.  After all, a top pair D Man (under control for cheap for years to come) can have such a great impact and they are extremely hard to find on the free market (or trade market).

But can these standout rookie D keep up their great performances?  After all, we always hear about the so-called “sophomore slump” and it’s not like players disappointing after great rookie years is that uncommon.  How certain can we be about the futures of rookie D-Men who come up and right away show strong possession #s?  Let’s see how similar rookie D the last few years did.

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Perspective On Possession

The more ubiquitous metrics like Corsi and Fenwick become, the stronger their skeptics will argue against them. Though modern analytics have now permeated big-time media and drawn the attention of renowned hockey personalities, they continue to be met with resistance among the more stubborn fans. Somewhere between the polarization of statistics acceptance and complete groupthink is a happy place where opinions may differ but people are knowledgable enough to understand what they’re disagreeing about. I maintain that much of the resistance against advanced statistics is born from a lack of understanding, or a lack of desire to understand. I’ll use Ottawa’s Erik Condra as an example. Condra has been a net relative plus for on-ice possession at even strength for each of his four NHL seasons, yet is seen as expendable by the majority of Senators fans. I’ve heard on multiple occasions that any metric which puts Condra ahead of say, Kyle Turris, must be wrong. What’s getting lost in the shuffle here is that Corsi is not the be-all-end-all stat its doubters perceive it to be. Condra’s CF% REL is telling us he sees a greater share of the 5v5 shot attempts directed at his opponent’s net relative to what occurs when he’s off the ice than Kyle Turris does. Nothing more. This is unequivocal as long as you put trust in the league’s trackers.

There is an axiomatic truth regarding on-ice possession that is seldom spoken albeit intuitive enough not to have to be. Not all possession shares equal worth. The differences that exist between shot rates and shooting percentages while on the ice add or subtract importance to the minutes you play and in turn, the share of shot attempts you generate. At equal CF%, a first-line player’s minutes will hold more value than a fourth-liner’s due to the simple fact more goals are scored in those minutes. It is thus an oversimplification to compare Turris and Condra’s CF% ratings without proper context. A different way to look at possession is to examine the expected goal differential based on shooting percentages we can reasonable expect from the quality of the players on the ice. In other words, how rewarding are a player’s minutes at a set possession share?

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What’s the deal with Andrew MacDonald: Why do the statistics suggest he’s terrible?

Did you really think I was going to miss the opportunity to post the AMac with chains gif again? You thought wrong.

Islander Defenseman Andrew MacDonald is one of the hot names being bounced around during the trade deadline.  On one hand, this makes sense: He’s making basically nothing on his current contract, he’s one of the time on ice leaders in the NHL this year and has handled top level competition for a few years now.

On the other hand, his conventional fancystats show a well…..massive decline:

AMacThreeYear

Yikes.  That 2013-2014 number is downright terrible, dropping MacDonald into the bottom tier of defensemen.  And no zone starts and certainly not competition (see this article for an analysis of AMac vs various levels of competition) does not account for this.  If you believed the fancystats, AMac isn’t just not a top tier DMan, but not even a 2nd or 3rd pairing guy who could help any team at all.  Yet teams seem to believe he’s worth a high pick?  So what’s going on?  Is the conventional thought completely wrong here?

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More on “Corsi & Context”, with some added predictive modelling

Corsi

INTRODUCTION

I have always been of the opinion that Corsi is part of the larger puzzle in trying to gain greater understanding of the game and how a player can affect their team’s chance to win.  Like all statistics though, it needs appropriate sample size and context, and will never tell you everything. Teammates, opponents, luck, system, strategy and what moments a coach deploys a player will always effect results… although, there can also be times where context is overly stressed. While Corsi does tend to need less context than many other hockey statistics, there are some things that need to be kept in mind in how two players with the same Corsi% are not always created equally.

Tyler Dellow wrote a piece on context that is definitely worth a read. In the article Dellow used two tables showing how Corsi changes dependent on ice time for the 2011-12 season.

We will revisit this article using a larger sample and look at both forwards and defensemen.
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The Day David Staples Killed Corsi Because…Taylor Hall

File:Taylor Hall.JPG

Photo by Alexiaxx, via Wikimedia Commons

I’ve been following the story of Taylor Hall as the season progresses, particularly through Tyler Dellow’s attempts to un-vex the vexing year Hall is having (Parts IIIIIIIV). In Tyler’s second part, he notes three differences between this year and last year: fewer zone entries with a carry, poorer retrieval of dump-ins, and a lower shots-per-carry total. The latter, Tyler notes, is likely symptomatic of a larger emphasis on dumping-in, wherein a player carries to just inside the blue line before dumping. He quotes Dallas Eakins as suggesting that Hall, in-particular, seems to take this dumping-in approach to heart. I’d add that there’s a possibility that this is abbreviating potential offensive zone possession time, as overall Hall and the other Edmonton Oilers have dropped from nearly 50 seconds per shift to 47 seconds. Further to that point, Tyler noticed in the fourth part that the Oilers have seemed to adopt a tip-in dump-in, wherein the player in the neutral zone either redirects or chips, while standing in place, the puck into the offensive zone. Just based on the video evidence Tyler provided, this looks like an extraordinarily passive approach to the dump, equivalent to dumping and getting off the ice. In that latter scenario, you are unequivocally giving up possession. In the tip-in approach, you take your active close player and leave them in-place, in favor of a later-to-the-game forechecker. It would seem to me that you’d benefit from an active dump-and-chase forechecker.

There are a couple of others irons you can put in the fire, including variance of CF% (a 5% swing is not unheard-of, particularly moving from a 48 to a 56-game sample), potential fatigue from increased playing time (he’s taken on some penalty kill minutes and more even-strength minutes this year), and the swapping out of Ales Hemsky as a linemate (for Sam Gagner). The tougher competition, for me, is essentially washed out by a bump up in offensive zone starts. I don’t see evidence of recording bias, either. I suspect a couple potential, additional things: 1) the drop-off is right there with the Ovechkin-Dale Hunter drop-off, so there might be some player vs. system aggravation, and 2) some fatigue issues related to the early-season knee injury. Injuries aren’t just about pain, they can also compromise strength and endurance. A guy like him, who has had injury issues in the past, does not want the “soft” label (you’ve seen what that’s done to Hemsky’s time in Edmonton), and might not want to admit it to the media or himself.

Up to this point, you’ve seen Dellow’s and my own introspection into what appears to be a poor possession season from Taylor Hall. Enter David Staples.

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Input versus Output: An Ongoing Battle that No One Knows About

XKCD comics is written by Randall Munroe, a physicist who probably doesn’t know what  hockey underlying numbers (ie: #fancystats or advance statistics) even are, let alone supports them… yet – for the most part – he gets it.

Mainstream sports commentary is full of poor analysis when it comes to using numbers appropriately. Most of this comes from a lack of understanding between the difference between inputs versus outputs and how much a player can control certain factors. (It should be noted that this is a broad generalization; not everyone falls into this category).

Benjamin Wendorf displayed a bit of these factoids in his recent article Why The Hockey News’ Ken Campbell is Wrong About Alex Ovechkin, but Campbell still didn’t get it.

What happened:

For those that do not know, here is a quick summary of Campbell’s article:
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