New York Rangers

Grit vs. Skill: Tanner Glass vs. Pavel Buchnevich

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After losing 4-1 to the Montreal Canadiens on March 4, the Rangers recalled Tanner Glass from the their AHL affiliate, the Hartford Wolfpack. Rather than attribute the loss to the Rangers playing poorly––since the Canadiens outshot the Rangers 35-27, won 63% of faceoffs, and had Carey Price in net––much of the blame for the loss was placed on the Rangers lack of “grit” and “toughness.” According to the Rangers, the difference makers in that game were Dwight King, Andrew Shaw, and Steve Ott.

Since recalling Tanner Glass, he has played in six games, and has recorded a goal and an assist. Many view having a tough player like Glass in the lineup as a deterrent. In his first game back with the Rangers against the Tampa Bay Lightning, Glass put his toughness on display early by fighting Luke Witkowski. Later that period, Gabriel Dumont of the Lightning boarded Rangers’ defenseman Steven Kampfer––something that Glass’s presence should have deterred, right?

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NHL Analytic Teams’ State of the Union

Josh W NHL League-Wide Analysis Tags: , , , , , , , , , , , , , , , , , , , , , , , , , Leave a comment

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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The NHL Systems Argument: Comparing Bruce Boudreau, Alain Vigneault, & Lindy Ruff

Benjamin Wendorf NHL League-Wide Analysis, Theory Tags: , , , , , , , , , , , , , , , , , , , Leave a comment
Bruce-Alain Ruff. Looks like the ghost of Gene Hackman. You're welcome for the nightmares. Composite of images by

“Bruce-Alain Ruff. Looks like the ghost of Gene Hackman. You’re welcome for the nightmares.” Composite of images by “DSCF1837” (Vigneault), Michael Miller (Boudreau), and Arnold C. (Ruff), via Wikimedia Commons*

Systems are without question the most elusive, yet most important, part of our understanding of hockey and the application of analytics. What works and what doesn’t? To what degree can a coach or team apply a strategy?

This led me to think about where we might most convincingly see evidence of a system at work. In the past, we here at HG have had a lot of skepticism about a number of elements of a “system.” For example, Garik’s pieces on competition-matching lines (here and here) and the use of the “defensive shell” to protect a lead, neither of which presented themselves as particularly effective ways of looking at or implementing systems. I have shown in the past that attempts to use extreme deployment in terms of zone starts doesn’t move the needle beyond a 60-40 range of possession, the range of shooting shares for forwards and defensemen haven’t seemed to change much over the last 20-25 years, and a plotting of even-strength shots-for with top and bottom possession teams do not suggest a major difference in shot location.

So where to go from there? Eventually, I decided that we need to get to an extreme enough situation, with robust enough data, where a team might have the best opportunity to dictate a system — in other words, we need to look at the powerplay. The most ideal opportunity for comparison, given the workable data for me, comes from the coaching careers since 2008-09 of Bruce Boudreau, Lindy Ruff, and Alain Vigneault. They all provide at least a couple of seasons with different teams, in addition to a robust set of coaching data from 2008 to the present. Let’s see what we can see…

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2014-2015 Season Preview: The Metro Division

garik16 NHL League-Wide Analysis Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , 5 Comments

Image from Michael Miller via Wikimedia Commons

Last year, in preseason, the Metro Division, was considered by far the strongest division in the East and the likely bet to take both Wild Cards.  The whole division, minus the Pens, promptly started the season by getting hammered, only recovering later in the season to grab one of the two wild cards.

This year again, the top 5 of the division looks strong enough to take two wild cards.  The bottom 3, particularly the bottom 2, are very weak, but the top 5 is strong and near evenly matched such that they could wind up in any order.  But, given the requirement to project the division, these are how I believe the division should finish up, from worst to first:

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

Benjamin Wendorf NHL League-Wide Analysis Tags: , , , , , , , , , , , , , , , , 6 Comments
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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Using NHL Coaching Changes to Identify Historically Good and Bad Coaches

Benjamin Wendorf NHL League-Wide Analysis Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , Leave a comment
Iron Mike no like. - Photo by "Resolute", via Wikimedia Commons; altered by author

Photo by “Resolute”, via Wikimedia Commons; altered by author

Having now looked at the overall effect a coaching change might have on a team, and identified some outstanding examples where a coaching change had a drastic impact on a team, it’s now time to shift over to some juicier matters. For the most part, I don’t think one coaching change is necessarily sufficient to say a coach is good or bad; there is a possibility the previous coach was just that bad. But if the coach returns the same signal a couple of times or more, you are probably getting closer to a true reading on what they might bring to the table.

Across the 140 or so coaching changes these last 60 years where both coaches led the team 20+ games, there were 69 coaches who were a part of that change twice or more (which, to me, is quite a remarkable number). The full list, followed by an explanation of the measures:
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NHL Team History, Possession, and Winning the Stanley Cup

Benjamin Wendorf NHL League-Wide Analysis, Theory Tags: , , , , , , , , , , , , 4 Comments

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

Benjamin Wendorf Career Chart, NHL League-Wide Analysis Tags: , , , , , , , , , , , , , , , , , , 4 Comments

File:BrendanShanahan.jpg

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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