NHL Analytic Teams’ State of the Union

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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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Friday Quick Graph: Possessing the Puck in 1969, 1981, and 2013

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Photo by Jim Tyron, via Wikimedia Commons

Just finished tracking possession times in a November 15th, 1969 game between the Flyers and the Leafs. This game, when compared to the games from this post, fits virtually in-between them, which is interesting because, unlike with the other two games, the Flyers and Leafs were two teams on the lower end of the spectrum in the league (8th and 9th in 2pS% in a 12-team NHL). Maybe that also contributes to their average possession time of 6.08 sec (n=349) compared to the 1981 game’s 6.15 (n=364) and 2013 game’s 6.17 (n=360). Another observation among these games: the standard deviation for the 1969 and 1981 games is right around 4 seconds, where it’s right at 5 seconds for the 2013 game. I’ll save any deeper ruminations until I have a larger sample, but it’s food for thought.

Not too long ago, I decided I wanted to try out tracking time of possession in historical games, with the hope of eventually having enough data to look into things. I realized it’s going to be a little difficult to get large enough samples of singular teams, but I also realized that we could potentially compare the game as a whole in different eras. I’ve always been of the mind that the game has evolved somewhat, but at its core there are a number of best practices that have kept it pretty much the same game from around the time that the red line was introduced in 1943. I wanted to test that as far back as I could go, though, so with this possession tracking I actually tracked each individual possession rather than just a total time of possession. For this chart, I displayed all those individual possessions as a distribution, longest possessions to the shortest. These three games, the Philadelphia Flyers vs. Toronto Maple Leafs in 1969 (Toronto won 4-2), Edmonton Oilers vs. Philadelphia Flyers in 1981 (Edmonton won 7-5), and Los Angeles Kings vs. St. Louis Blues (St. Louis won 4-2), had some surprising results when compared. As you can see above, the distribution is actually quite close, with the 1981 game seeming to have shorter possessions but then moving above the others in the middle of the line. The 1969 game actually seems like a trendline of the 2013 and 1981 games. The average possession time? 1969: 6.08 seconds, 1981: 6.15 seconds, and 2013: 6.17 seconds. Obviously, I need (and want) more data, but it is a really intriguing start.

The “possession battle” results?

All Situations Possession

  • PHI (47.1%) vs. TOR (52.9%), 1969
  • EDM (53.4%) vs. PHI (46.6%), 1981
  • LAK (51.7%) vs. STL (48.3%), 2013

Possession, Score Close

  • PHI (41.3%) vs. TOR (58.7%)
  • EDM (48.7%) vs. PHI (51.3%)
  • LAK (51.2%) vs. STL (48.8%)

NHL Team History, Possession, and Winning the Stanley Cup

Photo by “JulieAndSteve”, via Wikimedia Commons

Gabe Desjardins dropped a comment over at my Tumblr awhile ago, asking me if I could put together a graph expanding on a metric I came up with, 2-Period Shot Percentage (or 2pS%). 2pS% is an historical possession metric that takes shots-for and shots-against in just the first two periods of a game and expresses it as a percentage for the team being analyzed. The idea was that I was trying to get a rough possession measure from the period that would avoid score effects, or the tendency for teams with a lead to sit on the lead and thus give up shots late in the game. Having recently completed a database of period-by-period shot data going back to 1952-53, I have been able to test this metric a bit and the results were good for 2pS% as a possession measure. Returning to Gabe’s request, he wanted to know if I could chart the 2pS% data from year-to-year, with one line following the league leader in the metric and the other line following the Stanley Cup winner. I’d been curious about this myself; certainly there are a number of different ways to express the value of the metric, but this particular one could be interesting because it toes the line between what the Old and New Guard feel is important in this kind of analysis.

Well, I was right that it would be interesting:
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NHL Career Charting: The Pre-BTN Era and What We Can Still Do With Historical Data

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Photo by “IrisKawling”, via Wikimedia Commons

Hockey statistics have always been fairly historically limited; most of the so-called “fancy stats” have only been tracked (and easily track-able league-wide) back through the 2007-08 season. The prior years have a veil of fog over them, though there is fairly decent shot data going all the way back to the 1952-53 season (thanks to the Hockey Summary Project; I’ve been able to bring the data together), good game-by-game individual player data going back to 1987-88 (thanks to Hockey Reference via Dan Diamond & Associates), and gradually-improving TOI data going back to 1997-98 (thanks to NHL.com and Hockey Reference). Unfortunately, this has lead to a relative dearth of research into the years of the “Pre-BTN” Era, so-called because 2007-08 was the first year we received in-depth, league-wide data from Gabe Desjardins’ Behind the Net stats site and Vic Ferrari’s timeonice.com.

Having a background in history, and also having grown up as a fan of the league in this grey statistical era, I have spent the last couple years trying to compile and present statistics from the Pre-BTN Era in ways that can help provide a window into those years (and possibly inform our understanding of the present-day game). I’m somewhat indebted to Iain Fyffe, a guy who’s been doing similar yeoman’s work much longer than myself at Hockey Prospectus, though more recently he’s been sharing his work at his own site, Hockey Historysis.

The fact of the matter is that there is actually an enormous amount of information out there, and more importantly with graph work we can really do some interesting things. First case in-point is what I call “career charting;” essentially, charting a player’s shots in a game relative to their team’s shots in those same games. Using the metric %TSh, or percentage of team shots, this provides an interesting glimpse into player contributions, workload, and development in the Pre-BTN Era. Adding some artistic (and informational flourish), I present to you Pierre Turgeon:

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