Blue Jackets Coach Todd Richards’ Firing, & Why the History Doesn’t Agree With Mike Harrington

Photo by user

Photo by user “Arnold C,” via Wikimedia Commons; altered by author

The Columbus Blue Jackets made a bold move today, firing their coach of 3 1/2 seasons Todd Richards in favor of noted firebrand and Brandon Dubinsky fan John Tortorella. The move, riding the coattails of a 0-7 start for the Jackets, was done unusually early in the season, so unusually I decided to spill a little ink on it.

Around the same time I was rounding up the data, the esteemed (Buffalo Baseball Hall of Fame!) Sabres writer and analytics pot-shotter Mike Harrington decided now was the time to defend a decision that made little sense, about a team he doesn’t write about. It started with a reasonable tweet from Friend of the Blog Micah Blake McCurdy:

At which point Harrington followed:

Alright, Mike, let’s take a look at the “numbers that count,” according to you. There’s a fun history here.

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NHL Analytic Teams’ State of the Union

Pure-mathematics-formulæ-blackboard

Fandom means a lot of different things to different people. But one thing unites us all: we hope our favorite team will win, and spend a great deal of time thinking how they can.

For those of us who dig a little deeper on the “how” side and use analytics, we hope that our work will eventually make its way to a front office. In some ways, it already has: numerous “hockey bloggers” hirings have been made recently.

But how many and for which teams?

With some research, I’ve culled a working document on all analytics hires for NHL teams and how they may be using analytics. The following descriptions comes from a variety of sources including Craig Custance’s Great Analytics Rankings [Paywall], fellow bloggers from across the internet, media reports, word of mouth and anonymous insiders.

It should be noted that just because a team has made an “analytics hiring”, it doesn’t necessarily mean that they value their input or use the analysis provided properly. In fact, hires can be made simply for PR reasons, and some teams may even give analytics tasks as secondary duties to staff members who do not posses any formal background in the subject. Teams may also have hired private firms providing proprietary data, which in reality may not provide any tangible, verifiable value than what is free and readily available online.

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2014-15 Preview: The Central Divison

Image from Matt Boulton via Wikimedia Commons

If you’re a fan of a Central Division team that doesn’t employ Ondrej Pavelec, you’re probably feeling optimistic as we approach the upcoming season. And you should: this is clearly the best division in the NHL, and all six of its non-Manitoban clubs have legitimate playoff hopes.

Of course, not all six will reach that milestone; at least one will join Winnipeg on the outside looking in. At this time, however, few can agree on how the standings will shake out. The Stars have been projected anywhere from second to fifth; the Avalanche have been slotted everywhere but last. Some are high on the Blues, others are sick of them constantly disappointing.

This uncertainty should make for an exciting year in “Conference III.” Below is a team-by-team breakdown of the league’s toughest division:

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Gordie Howe vs. Bobby Orr vs. Wayne Gretzky vs. Sidney Crosby: Not Your Typical WOWY

Photo by "Djcz", via Wikimedia Commons

Photo by “Djcz”, via Wikimedia Commons

With or Without You analysis, often referred to as WOWY, frequently involves either comparing the performance of a team or particular players when a single player is and isn’t playing. While the approach is a risky one (sample size is a pretty big issue), it can actually be quite telling when you collect enough data.

The value of modern WOWY is that you can definitely get data from precisely the seconds a player played apart from the seconds they weren’t on the ice. Historical WOWY, on the other hand, cannot do much better than taking data from games a player played versus games they didn’t. To this end, then, I wanted to see if historical WOWY can tell us much of anything, and the best way to do that is to focus on players that are undisputed in their value. In this case, I went for WOWYs of the big guns, four of the best players across the eras of NHL history: Gordie Howe, Bobby Orr, Wayne Gretzky, Sidney Crosby.
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Friday Quick Graphs: Toronto Maple Leafs, Chicago Blackhawks, Edmonton Oilers, and Boston Bruins Shot Distributions, 5 Years

What you see above are the even-strength shots-for locations for the near-indisputable top team of the last five seasons (Chicago Blackhawks) versus the near-indisputable worst team of the last five seasons. This is a sort of visual anti-shot quality argument, a demonstration of why, across these five seasons, the indisputable #1 team would shoot 9.9% while the indisputable #30 team would shoot 9.6%. Notice the horseshoe design, about where defensemen normally sit, then jump up into the play. Notice the dense cluster around the high slot. All teams make these plays, try to make them, the difference being some are better at possessing and moving the puck to make the shot. What’s the primary difference above? The amount of shots.

None of the above charting is possible without Greg Sinclair’s awesome site, Super Shot Search. Bookmark it, use it, love it.

Oh, hey, what if I was to look at the teams with the best and worst save percentage these last five years? Would they look different in even-strength shots-against? Well, let’s see, Toronto and Boston:

There is a difference here, I think. I mean, the initial difference are the numbers, Boston’s SV% (92.1%) versus Toronto’s (89.5%). Another difference is it seems the two charts maintain roughly the same shot distributions, but flip ends of the rink. Not much to dwell on there. One thing I will say, that could relate to the SV% discrepancy, is that it doesn’t appear that Toronto records many, if any, shots from right along the boards. Now, I don’t know if this is a recorder’s error or not; it seems to me it’s pretty hard to get a shot from right tight along the boards. Maybe one recorder does it based on where the body of the skater was located, I don’t know. Or…Toronto does allow shooters to come in a little tighter, and Boston owns the center ice a bit better. Could that explain a near-3% discrepancy? I don’t think so; we know Toronto’s had worse goaltending. But it might’ve “helped.”