Rebounds, Extended Zone Time, and the Quest For More Offense

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Long has it been argued that sustained zone time is a reliable way to not only prevent your opponents from scoring but as a way to produce offense of your own. The argument that is often made, or at least the one that’s often heard, is that the longer you are in the offensive zone the more likely it is that the defense will become fatigued and make a mistake that leaves someone open for a prime scoring opportunity. 

So let’s test that theory by asking a more data driven question; does sustained zone time lead to an increase in shooting percentage?

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The 2015 OHL Final Part Three: Erie Otters and Oshawa Generals Passing Data

The last two pieces of mine focused on passing network analysis for both the Erie Otters and Oshawa Generals from their 2015 OHL Final. The point of this short series was to look at how and why teams are successful over a playoff series. Generally, five-game samples aren’t large enough to give much credence to, and yet a sound game plan and tactical preparation can influence an upcoming playoff series. With the type of data collected and analyzed through passing networks, it provides a baseline of how influential the Otters and Generals players were in their series. From there, naturally, any good analyst will go to the video to find evidence of how these numbers occurred, to augment their conclusions. These first two series served as a opportunity to present new ways of analysis with our data this season, something that, come playoff time, will be given a test run in terms of series predictions.

This final piece in this series will focus on the production for each player and team in a more traditional sense. What does that mean? Lots of numbers and charts. For starters, Erie generated far more passing offense in this series than Oshawa. At 5v5, here were the Otters possession numbers for shot sequences from single, multiple, and scoring chance passes.

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Big Deals for Big Hitters: How Physical Players Age

The Detroit Red Wings made headlines recently when Ken Holland signed 28 year-old forward, Justin Abdelkader to a 7 year contract worth $4.25M per season. There was a fairly visceral and predictable reaction from the hockey stats community, noting the high shooting percentages he has been enjoying over the past year and the decline in performance we’ve historically seen from forwards aged over the age horizon of Abdelkader’s contract. However, the piece of the discussion that really struck home for me was comments around the wear and tear to Abdelkader’s body given his physical style of play.

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How Hard is It to Find Good NHL Goaltending?

Image courtesy Flickr user Harold Cecchetti. Use of this image does not imply endorsement

Is Jacob Markstrom still good?

Whether you come at hockey from the numbers or from traditional scouting, finding NHL-quality goaltending is a challenge. In order to have a good sense of a goalie’s talent (as measured by even-strength Sv%), you need to observe about 4,000 shots worth of work. On average, a goalie needs to play over three seasons as a starter (or eight seasons as a backup) to see that many shots. If they play poorly, few netminders will ever get close to that amount of playing time and most goalies are entering age-related decline by the time they’ve seen that many shots. As such, teams usually make decisions on goaltenders long before they’ve seen 4,000 shots and, unsurprisingly, teams make mistakes.

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The 2015 OHL Final: Oshawa Generals Passing Network

Last time, I took a look at the passing network for the Erie Otters during the 2015 OHL Final. Today, we’ll take a look at the passing network for the Oshawa Generals. Below you’ll see their network constructed using Gephi.


Another reminder on how to read the visual: The larger and darker the node (player), the higher number of edges (connections or passes) to another player or goal (shots) that player had. Rather than simply total up the passes and shots, I’ve assigned weights based on whether that pass led to an actual shot on goal, was a scoring chance, was a danger zone pass (from behind the end line or across the Royal Road), resulted in a goal, etc. The edges (lines) between players are weighted as well, so you can tell which players were setting up a higher number of chances for specific shooters.

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What determines coach salaries? A look at NHL bench bosses

This summer, the drama surrounding Mike Babcock drew my attention to the salaries of coaches in general. What factors play into how much money a coach earns? Babcock is known as a coach who’s won at every level. Are Stanley Cup wins a factor in what a coach gets paid? Maybe playoff wins? Regular season won-loss records? Something else?

Babcock’s contract – a mammoth 8 year long pact worth $50 million to coach the Leafs – brought the subject of coaching salaries to the forefront. At $6.25 million per season, Babcock earns more than double the annual pay of any other NHL coach with a publicly known wage.

For the Leafs, spending huge amounts of cash on team personnel makes sense – there’s no cap on coach salaries so that Leafs can wield their monetary advantage to sign the best bench boss available. For Babcock, it’s difficult to fault the long-time Red Wings coach for taking the big pay day. Beyond enriching himself (which he really, really did), Babcock has been very open about his desire to push coach salaries forward by setting a new standard. He probably didn’t imagine he’d earn more than Joel Quenneville and Darryl Sutter combined or that his term would extend three years past any other NHL coach. But, as perhaps the game’s best coach, the Leafs were willing to pay whatever was needed to pry Babcock out of Detroit.

But what types of thinking go into deciding how much a team is willing to pay its coach? Did Babcock earn the money because of his vast experience? Or maybe his excellent regular season record over a decade in Detroit? What factors correlate with coach salaries?

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Distribution of Quality of Competition and Teammates Metrics

The analysis community has studied these metrics in various ways. The purpose of this post is to lay out the way I understand the metrics, and identify areas of additional research.

The effects of competition and teammates on players are not new concepts in hockey.  We hear about it all the time in analysis and conversation: “Jonathan Toews is deployed by his coach to specifically shut down the top players of the opposition”,  “4th liners play against the opposing 4th line”, “Sidney Crosby makes his teammates better”, etc. etc.

Having analyzed the metrics used to quantify quality of competition and teammate, I came to two conclusions.

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