San Jose Sharks

Practical Concerns: A Better Way To Talk About Hockey?

Jack.Han Practical Concerns Tags: , , , , , Leave a comment

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“The Pittsburgh Penguins won.” “The San Jose Sharks lost.”

Do those statements have the same meaning? The answer depends entirely on what you mean by meaning.

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Following the suggestion of my friend Arik Parnass, I’ve began re-reading the book Thinking Fast And Slow, which explores many ideas that can be applied to hockey.

One of the chapters in the book deals with the concept of framing – how people can be influenced to think about a certain situation depending on the words used to describe it. Going back to our initial example, did the Penguins win the Stanley Cup (because of their superior tactics, teamwork and talent level)? Or did the Sharks lose the Stanley Cup (because of their reliance on defensemen who are slow and can’t make a pass)?

There is no right or wrong answer, but we can see how a simple difference in phrasing can lead us down different avenues.

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Video Analysis: How The Penguins Extend Zone Time With “Total Hockey”

Jack.Han Practical Concerns, Uncategorized Tags: , , , , 1 Comment

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By any predictive metric, the Pittsburgh Penguins have generated a staggering amount of offense against the San Jose Sharks in the Stanley Cup Finals. Earlier this week, we looked at how the Penguins are able to create possessions with good defensive habits in the neutral zone. Today, we’ll examine how they create a volume of offensive chances via positional switches.

To fully understand the ideas behind the Penguins’ offensive zone play, it is necessary to study the “Total Football” philosophy:

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The line of thinking lends itself well to the speed and teamwork-oriented nature of hockey as well. While the Penguins are by no means the first team to apply these ideas, they are a good example of how they can be used effectively at the highest level of the game.

Here are some clips from Game 3 and Game 5 of the Stanley Cup Final illustrating the tactical benefits of fluidity and positional switches.

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Video Analysis: In the Penguins-Sharks Stanley Cup Final, Possession Starts With Good Defensive Gap

Jack.Han Uncategorized Tags: , , , , Leave a comment

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In the playoffs, details make the difference.

Heading into Game 4 of the Stanley Cup Final, the Pittsburgh Penguin have had the measure of the San Jose Sharks in terms of shot differential. Looking at the game tape, we can see that one of the contributing factors is the way both teams defend the rush.

As a group, the Pens’ defensive corps is fleet-footed and blessed with good offensive acumen. They are also undersized and prone to being muscled off the puck by San Jose’s skilled forwards. In order to minimize their exposure to defensive-zone breakdowns and to maximize the team’s speed and skill, the Penguins have been playing a very tight gap across the neutral zone, funneling San Jose puck carriers toward the end boards and standing up at the red line in order to encourage the Sharks to dump the puck in.

A hallmark of the Mike Sullivan-coached Penguins is the team’s attacking mindset on and off the puck, as evidenced by the way they suppress the San Jose transition game.

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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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Bayes-Adjusted Fenwick Close Numbers – An Introduction

garik16 NHL League-Wide Analysis Tags: , , 1 Comment

With the season upon us, and multiple stat sites now hosting team and player fancystats, it is pretty tempting for a hockey fan (well, one who’s into fancystats) to try and check how his team is doing in possession in close situations – in other words, in Fenwick Close (alternatively, score adjusted fenwick). The problem with this, of course, is that the sample sizes are currently so small as to make the #s pretty meaningless – some teams have played as few as 3 games, so you can’t make any judgments based upon these numbers on their own.

But, as I mentioned on twitter, we can still try and take these numbers and make something out of them, using our prior knowledge of the NHL to make judgments. For example, I can look at current fenwick close #s and pretty confidently state “Buffalo is going to be a terrible terrible team” at this point, despite the sample size, given our prior knowledge of what the Sabres are. In other words, we can incorporate current fenwick close #s into a Bayesian Analysis.

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2014-15 NHL Season Preview: The Pacific Division

Benjamin Wendorf NHL League-Wide Analysis Tags: , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , 1 Comment
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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How Will Joe Thornton Perform Through his Contract?

Jared Career Chart Tags: , 1 Comment

photocred Wikimedia Commons

Joe Thornton has been in the news a lot recently, with talk that the Sharks are looking to move him as part of what they are labeling a rebuild.

Thornton is elite. In the past five seasons, he’s tied with Corey Perry as the sixth best point accumulator in the league. The Sharks signed Thornton, who turns 35 within two weeks, to a three year contract with a cap-hit of $6.75 million back in January.

I’m sure any potential acquiring team is intensely interested in how Thornton may depreciate over the life of his contract.

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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

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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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