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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Save Percentage vs the Experts: Do shots against inflate a goaltender’s save percentage?

Curtesy of Wikipedia Commons

I’ve seen many statistical articles look at different ways to determine whether or not shot volume inflates a goaltender’s save percentage; however, I’ve never been satisfied with the methods used, regardless of the outcomes. So, I finally went and looked at the data myself.

It’s been seven months since I’ve written anything on save percentage. With all that wait, you’d think I’d give you a big, long, and in-depth article… but I won’t. 

I had one planned, but accidentally lost all my data. Of course, errors always come in clumps. Instead of recovering the lost data, I ended up permanently removing it. To make matters worse, extraskater.com going black made the information a hassle to manually extract again. I probably could write a code (or get someone else) to draw up the information again… but I still have one piece remaining from the original data: the graph.

What is this graph of? What does it mean? Continue reading

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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Twenty Players to Expect a Shooting Percentage Bounceback from in 2014-15

Photo from Michael Miller via Wikimedia Commons

Photo from Michael Miller via Wikimedia Commons

Goals are such a small sample stat that even over a full season you’ll see some raw figures that may not be overly indicative of ability. As a general rule, you can normally expect a player’s goal total to bounce back from a down season if the player is still producing shots on goal but suffered a significant drop in shooting percentage.

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Is it time to appoint a new jester?

Toronto -with its high profile in the media combined with some questionable management- has consistently been the brunt of jokes over blogs, message boards and twitter from other fanbases.

Recently the Toronto Maple Leafs has made a bunch of savvy, low-risk, high-potential steps both in management and player personnel to improve their team. While they are still a distance away from being a contending team, the steps taken are not those that the online hockey community has grown to love about Toronto.

With this knowledge and the offseason nearly in our rearview mirror, it is time for Hockey-Graphs to ask its analytically inclined following:

All teams in poll came from an unofficial nomination survey I conducted on twitter.

Can NHL Teams Win With Two Mega Cap-Hits?

The new contracts of Kane and Toews mean an imminent death of the Blackhawks, says everyone
Photo from Matt Boulton via Wikimedia Commons

When Patrick Kane and Patrick Kane signed matching eight year extensions with $10.5 million annual cap-hits, many wondered out loud if a team can be successful with two players occupying $21 million in cap space together.

So I decided to take a look at the relationship between a team’s success, measured by total regular season and playoff wins, and how much of their total cap outlay is from their top two cap charges.

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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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Defensemen still have no substantial and sustainable control over save percentage

For quite some time there has been a debate going on: those who think you should add a defenseman’s effect on save percentage into player evaluations and those who think that adding such information causes more harm than good to the analysis. Note that this does not mean defensemen do not affect save percentage. That is an entirely different stance.

When it comes to evaluating a player statistically, you want the number to account for two things: effect and control. If a statistic does not help quantify how a player improves their team’s chance at winning, it is useless in measuring effect. If a statistic has too much white noise or other contributing factors that it would take too large of a sample to become significant to the player’s contribution, it is useless in measuring a player’s control over the effect.

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