Measuring the Importance of Structure on the Power Play

tl;dr

  • We can measure a team’s power play structure using shot location data, creating a Power Play Structure Index that quantifies their ability to establish and shoot from a structured formation.
  • A Team’s Power Play Structure Index is a stronger predictor of future goal scoring than past goals, but weaker than shot attempt generation.
  • When examined together with shot attempt generation, power play structure is a significant predictor of future goals, although slightly less important than shot attempt generation.
  • A team’s structure index can provide valuable additional insight into why certain power plays succeed or fail.

Edit 2017-02-15: An earlier version of this piece had a small error in the regression coefficient for PP Structure Index. While the article previously indicated the coefficient was -0.19, it should in fact be -0.30. The text both above and below has now been corrected.

Introduction

The importance of structure in a team’s power play is something that’s really easy to see. We’ve all watched a power play executing at the top of its game: the puck flies from player to player, leaving defenders pivoting in place to try to keep up. Each shot looks exactly like it was diagramed by the coach, with attackers working to set up a specific shot from a specific player in a specific location.

A solid structure doesn’t just look good; it actually produces better results. Arik Parnass has written extensively on the importance of structure to power play success, showing that teams who get set up in a dangerous formation score more goals than those who don’t.

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Sbisa, the Sens, and the Scramble: Evaluating Defensive Play Following a Shot Attempt

Luca Sbisa may be one of the players who best epitomizes the divide between the old-school, eye test view on hockey and the statistics-focussed analysts offering their opinions from their mother’s basements on fan curated sites across the internet. While GM Jim Benning clearly thinks Sbisa is a useful defender, rewarding him with a 3-year, 10.8MM deal, and consistently praising his defensive zone smarts, Canucks fans have been less bullish on the talents of the 25-year-old Swiss pointman. Correctly noting his less than stellar possession numbers, J.D. Burke commented that his first season with Vancouver featured few “extended stretches in which any pairing with Sbisa on it looked passable”. These aren’t just the criticisms of a bitter fan wishful for better years, Burke backed up his arguments with a detailed numerical breakdown of Sbisa’s many failings, and video evidence of some of his less than professional defending from 2014-2015. Burke, and the Canucks’ fanbase in general, seemed to paint a picture of Sbisa that stands in stark contrast to what Vancouver management observed. Where the fans saw a player who frequently found himself out of position at critical junctures when defending his own end, Vancouver’s brain trust viewed Sbisa as the ideal player to disrupt a cycle down low. How could two groups of people who watched the same games with such intense devotion come to such different conclusions?

One of the biggest difficulties with evaluating Sbisa, and defencemen in general, is that what the eye test says is important is often wildly out of sync with what statistics can currently measure. While stats-based analyses focus on a defender’s ability to prevent shot attempts (in other words, their Corsi Against per 60), most of the praise for defensively-minded defencemen tends to focus on hockey IQ, being in the right position, and winning battles in the corner. While ideally these less “quantifiable” skills should lead to favourable statistical results, issues with differences in player deployment and the teammate-dependent nature of defending often mean that what gets praised in post-game interviews isn’t what shows up on the scoresheets, leaving a divide between management’s view and the story told by pure shot attempt numbers.

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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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Schedule Adjustment for Counting Stats

Edit:There is another version of this article available in pdf which includes more explicit mathematical formulas and an example worked in gruesome detail.

Rationale

We all know that some games are easier to play than others, and we all make adjustments in our head and in our arguments that make reference to these ideas. Three points out of a possible six on that Californian road-trip are good, considering how good those teams are; putting up 51% possession numbers against Buffalo or Toronto or Ottawa or Colorado just isn’t that impressive considering how those teams normally drive play, or, err, don’t.

These conversations only intensify as the playoffs roll around — really, how good are the Penguins, who put up big numbers in the “obviously” weaker East, compared to Chicago, who are routinely near the top of the “much harder” western conference? How can we compare Pacific teams, of which all save Calgary have respectable possession numbers, with Atlantic teams, who play lots of games against the two weak Ontario teams and the extremely weak Sabres? Continue reading

Why NHL Stats and Scouting Must Work Together

File:2006nhldraft-stage.jpg

Photo by Arnold C, via Wikimedia Commons

I think it’s fair to say that people familiar with hockey scouting and stats analysis know that there is a bit of a rift between the two (not unlike what exists in baseball). The former, as in baseball, has a long history as the standard in hockey analysis, being at-or-near the forefront of drafting, trading, and free agency decisions for teams. The latter is expanding its reach exponentially into league offices, and has many a pro-stats person questioning the abilities of scouts to analyze players (and vice versa). There are at least preliminary attempts to reach out, on the part of Corey Pronman at Hockey Prospectus (and ESPN), but scouting and stats analysis both have a lexicon, methods, and best practices, and devotees of one probably don’t have much time to develop proficiency in the other.

Yet, therein lies a problem and a solution. There is a common thread between these two groups, the desire to usefully analyze hockey players. They each have their own approach, but neither necessarily contain such complicated concepts that they cannot be read by a conscientious analyst. But most importantly, they have something to offer one another that could improve both areas of analysis.
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Save Percentage vs the Experts: Round one, introduction of concepts

Photo Cred: Eric Hartline-USA TODAY Sports

Due to starting my dive into hockey statistics as a Winnipeg Jets fan, save percentage has always been a pretty big interest of mine, specifically in what it can and can’t tell us. The truth is, it is still a pretty rudimentary statistic and likely will be improved upon in the future. However, simple does not always mean bad or useless.

Of the three most common “goaltender statistics”, save percentage is the one controlled most by goaltenders. How can I be so sure of that? Well it can be provided with simple logic.
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