(Image courtesy of Wikimedia)
Welcome to a brand-new, semi-regular segment where I -Garret Hohl- will touch on a few trending topics in hockey statistics in a less mathematical and more discussion format.
This week we will explore the debate on shot quality impacts on save percentage.
So let’s begin.
Back on the usual Monday/Tuesday schedule, Garret and Rhys try to talk about how injuries have affected the Canucks and Jets and their upcoming schedules and playoff chances, but get sidetracked and discuss why plus/minus is terrible instead, among other fun things.
There’s also some good old prospectin’ too, so join us on the other side of the jump!
Between their usual ramblings, Garret and Rhys somehow find time to discuss Monday’s NHL trade deadline.
Join us after the jump to listen!
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Embedding interactive graphs into blog posts, especially blogs with a narrow runner like ours, is frequently an awkward process. Just about the time things look good, you tinker with it and it looks bad. Nevertheless, I had a bunch of old data I put together, once upon a time, and I wanted to get it out there in a form that you could tinker with. Basically, in the past I have used the percentage of team shots in the games a player participated (%TSh; explanation here) as a way to capture a player’s contribution to the shot load; I also think it strongly implies a player’s involvement and contribution to team offense overall.
In the case of today’s graph, I took %TSh and looked at aging curves with a multitude of players from 1967-68 through 2012-13 (like I said, the data is a little old). I prepared this with a selected group of players available for the filter, the majority of whom are stronger, more familiar players of the years covered. I also included some players that struggled by the metric, for the sake of comparison. To filter, click on the “Name” bar, click on “Filter,” and let your imaginations run wild. Feel free to download if you wish.
Note: I believe I set the cut-off at 20 GP before I would record the point of data. It’s old. I’m old. We’re all getting older.
Photo by Eric Kilby, via Wikimedia Commons
Building on my post from last week on overall skater height going back to 1917-18, I wanted to dig a little further into the the complexity of the data to see if there were any interesting takeaways. This included breaking the data into forward and defense data, to see if there was every any substantial increase in defenseman size or any other allusions to an attitude change in terms of size trends and preferences. While there are some slight differences, most interesting to me was, for as many changes as the NHL has undergone, there seems to be a uniform attitude about size when looking at forwards and defensemen.
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“Jackets-Canucks Face-off” by Leech44 – Own work. Licensed under CC BY-SA 3.0 via Wikimedia Commons.
A debate was ongoing on twitter over faceoffs and their value. The debate lead to most of this data being scraped anyways, so I thought I would display everything for your viewing pleasure.
The truth is, faceoffs tend to be highly overrated. They matter, but they do not matter on average much more than any other of the many puck battles that occur throughout the game. When you really break it down, faceoffs are really just a set play puck battle after all. However, some have more values than others.
Let’s take a look.
This week, Garret and Rhys welcome internet friend and Flames fan Mike Fail (@MikeFAIL) to the podcast to discuss the Jets recent trade, the Canucks outlook at the deadline, Mike’s love of John Scott, and how great Jaromir Jagr has been through his career. We also react in real time to the David Clarkson news! Join us after the jump to listen!
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Hey everyone.
A bit of a change of pace for Hockey-Graphs, here is a bit of an informal blog post. There are two separate things I want to address.
First thing:
I made a mistake.
Long ago on an article about Corsi and context, I made an error with moving the data from one spreadsheet into another. Everything in the specific article was actually correct. However, I had linked to a summarized data table on Google docs though that had some erroneous data on it.
The Google doc had summarized the average goal differentials for sets of players given their position in the depth charts and their Corsi%. What had happened is I accidentally copied 2nd line and 3rd line forwards in both their appropriate place and where 2nd and 3rd pair defenders should go. The online document has since been corrected and can be viewed here.
I made another mistake though in building an article off of that Google document. This article used the previous data in creating a quick model to estimate the goal impact difference between two players with differing Corsi percentages. The image has since been corrected.
I want to always be clear of my methods and my intentions, so this is why I wanted to post this to you.
Second thing:
Far smaller detail, the Hockey-Graphs podcast will be postponed until probably Friday. Rhys and I were not able to find a time convenient to both of us in order to record a session until then.
Sorry.
Until next time and thank you for reading and supporting our work.
There has been a few tries in trying to find what drives special team success on the penalty kill and the power play. One thing though that hasn’t been talked about enough is the time factor and how it can impact a team.
Image by Erich Schutt, via Wikimedia Commons
As any person interested in hockey stats should do, I’ve been gradually building my own personal database of player information that I can use when Y3K robs my future post-human self of cloud data for 3 seconds. To that end, player size wasn’t a huge priority but I knew eventually I’d want to have it, if only to think about how normal-sized I’d be in the 1920s NHL. In the process of bringing in all that data, I decided to do a little demographic work on player height and weight. We all know the players are bigger now than they were before, but by how much? And is there greater variance in size now or in the past?
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Hockey Graphs 