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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A Tank Battle in Pictures: Toronto Maple Leafs, Edmonton Oilers, Arizona Coyotes, & Buffalo Sabres in 2014-15

Having just added the 2014-15 season to our historical comparison charts, now was a good time to revisit (as I promised in my posts here and here on Pittsburgh’s 1983-84 tank battle) this season’s battle between Arizona, Toronto, Edmonton, and Buffalo. To do this, I tracked the progression of each teams shots-for percentage across two periods (or 2pS%), a possession proxy I developed for historical data that can help us compare teams back to 1952. As you can see above, the perception of the tank battle among these four teams wasn’t quite accurate to their results; Edmonton and Buffalo did not seem to have a marked drop-off in the final quarter-season.

Arizona and Toronto, on the other hand, did noticeably drop, and in Arizona’s case to a level below the hapless Sabres. Ultimately, the fight was more to maintain their improved odds, because Buffalo managed to hold at rock bottom. As I asked when I wrote about the topic with Pittsburgh in mind, it still gives rise to an interesting question: is it more wrong to tank than to maintain a low level all year? In some cases, a team that’s already laid low doesn’t need to tank deliberately…but on the flip side, I suppose that team also assumes risk in losing support and fans by not appearing competitive all season.

How did the Coyotes and Maple Leafs compare to what I’ve christened the “gold standard” for tanks, the 1983-84 Pittsburgh Penguins’ tank for Mario Lemieux? Well, the nice thing is that the plus- and minus-one standard deviations in 2pS% were virtually identical in 1983-84 and 2014-15, so I didn’t have to tinker with them:

While Pittsburgh had probably the starkest, earliest drop-off, both Arizona and Toronto were able to reach the same kinds of lows by the end of the season. To their credit, while the Coyotes and Leafs were, at best, in the lower half of the league in possession, they certainly did their best in the race to the bottom. You could question the wisdom of this kind of thing, since Pittsburgh was guaranteed the top pick if they reached the cellar, while this year’s tanks were struggling for a higher probability.

The Greatest Tank Battle: Penguins vs. Devils, 1983-84

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Mario Lemieux with Laval of the QMJHL in 1984; photo by http://www.lhjmq.qc.ca/ via Wikimedia Commons

What do you do when a 6’4″ QMJHL forward who scored 184 points in 66 games in his last underage season scores at a 282-point pace in his draft year? You tank — you tank as hard as you can. In the latter half of the 1983-84 season, the Pittsburgh Penguins and New Jersey Devils were in an unspoken, pitched battle for the bottom of the league and everybody knew it. While the Penguins would ultimately win out, sputtering to a 16-58-6 record (“good” for 38 points in the standings) to New Jersey’s 17-56-7 (41 points), the two teams were coming from distinctly different franchise backgrounds.

Using information from our new interactive charts, we can see what set these teams apart, and led them to take different paths in what turned out to be a pretty wild race to the cellar of the NHL.

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The Art of Tanking: The Pittsburgh Penguins in 1983-84

While tanking is a hot topic in this year’s NHL, the act of tanking is as old as the idea of granting the worst teams a shot at the #1 pick in the draft. Case in-point: the 1983-84 Pittsburgh Penguins, routinely considered the most overt of tankers in NHL history. The graph above is just one example of their tank, and man is that bad. The yellow and grey lines indicate one standard deviation above and below league-average historical possession (using 2-Period Shot Percentage, or 2pS%, explained here). The blue line is a 20-game moving average (the orange is cumulative), and you’re seeing that right; a team close to the middle of the pack dropped nearly two standard deviations, or from near the top to near the bottom of the league. That graph, and all the ones below, are just some examples of the kind of tinkering you can do with our new interactive graphs, which I highly recommend you check out.

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2014-15 NHL Season Preview: The Pacific Division

Photo by "Kaz Andrew", via Wikimedia Commons

Photo by Kaz Andrew, via Wikimedia Commons

Whenever I put together something as broad as a division preview, especially since the divisions have expanded, I usually try to slap something together that helps me get a quick impression of the teams as compared to one another. This time around, I put a little work into generating a 5v5 simulation of this coming season, specifically among the projected top 6 forwards, top 4 defensemen, and goaltenders. As 5v5 play comprises a little over 80% of all NHL gameplay, and these players tend to more consistently drive results (as players of around 3/5 to 2/3 of gameplay), focusing on their 5v5 performances from last year bring us to use a bit more stable indicators of future team performance. The quick-and-dirty approach here benefits from the fact that most of the Pacific lineups are quite similar from last year, and the top 6 and top 4 players tend to be deployed in the same roles from year to year. So, I took the average 5v5 Corsi-For% of the entire of the top 6 and top 4 for each team, the average 5v5 shooting percentage of the same group (for Johnny Gaudreau, I assumed a forward league-average 9%), and the career 5v5 save percentage of the projected goaltenders (for Fredrik Andersen I assumed a goaltender league-average 92.1%), and ended up with a projected 5v5 season that looked like this:
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