A Defense of WAR from a WAR-Skeptic

Posted on April 3, 2017 by Matt Cane — Leave a comment
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This post originally appeared on Hockey Graphs.

Note: This was originally intended to be a tweet-thread which grew far too long and unmanageable, so you’re getting a poorly-written post instead. Apologies in advance.

Recently, David Johnson, owner of the awesome puckalytics.com has been on a bit of a warpath (pun intended) against the use of WAR/GAR. Most of David’s arguments can be found here and here, but there are some other comments in this thread.

I consider myself a bit of a WAR skeptic. I think Dawson’s work is great, but I think there are limitations/issues with it. A good summary of some of my concerns can be found in another ill-advised and long tweet thread.

With that being said though, I still think it’s extremely useful as a first pass to start discussion. WAR can be broken down into 5 useful components to see where a players impact derives from.

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Second Units and Zone Entries: Why teams should go all-in on the 4 forward power play

Posted on March 7, 2017 by Matt Cane — Leave a comment
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Using 4 forwards on the power play is generally a good strategy. Four forward units take more shots, score more often on those shots, and post a better goal differential than 3 forward groups do.

It’s also a strategy that has become more popular over the last few years. 4 forward units have accounted for roughly 56% of the 5-on-4 ice-time this season, up 4% from last year and more than 15% from 5 years ago.[1]

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Measuring the Importance of Structure on the Power Play

Posted on February 14, 2017 by Matt Cane — Leave a comment
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This post originally appeared on Hockey Graphs.

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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Quick Post: Do Past Sv% Variables Predict Future Sv% Variables?

Posted on August 19, 2016 by Matt Cane — Leave a comment
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This post originally appeared on Hockey Graphs.

The usefulness of on-ice save percentage (and derivative metrics such as Sv% Rel and Sv% RelTM) has been the source of many, many heated debates in the analytics blogosphere. While many analysts point to the lack of year-over-year repeatability that these metrics tend to show (past performance doesn’t predict future performance very well) as evidence of their limitations, others (primarily David Johnson of HockeyAnalysis.com) have argued that there are structural factors that haven’t been accounted for in past analyses that artificially deflate the year-to-year correlations that we see.

David’s point is a fair one – a lot can change about how a player is used between two samples, it’s not unreasonable to think that those changes could impact the results a player records. But we don’t just have to speculate about the impact those factors have – we can test the impact, by building a model that includes measures of how these factors have changed and seeing how it changes our predictions.

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Does aggressive play on the penalty kill pay off?

Posted on March 23, 2016 by Matt Cane — Leave a comment
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This post originally appeared on Hockey Graphs.

Late last week, Arik Parnass pointed out a particular peculiarity about the Ottawa Senators’ penalty killing so far this year.

While the Sens may be an extreme example, their numbers tell the story of a constant struggle that teams are faced with when killing a penalty: do you focus solely on your own end and do whatever it takes to prevent a goal, or do you allow your forwards to take the play to your opponents, trying for a shorthanded goal and forcing them to defend in a situation where they may not be expecting it.

This risk-reward question is one that’s central to the value of hockey analytics. It’s very easy to make decisions based on personal experience which is so often dominated by memories of things that are out of the ordinary – a coach will likely remember watching his winger get caught deep trying for a shorthanded goal, while forgetting the 2-on-1 opportunity he generated earlier in the game. It’s just as easy, however, for a fan to complain that his favourite team won’t put out their best forwards to aim for a go-ahead shorthanded goal without any data to back up their argument. The challenge for analysts then is to dig through the available data to figure out what past experience has taught us about the overall net impact of playing for a goal on the penalty kill, so that we can make an informed judgement as to what the potential costs and benefits are.

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Predicting Which Players Will Succeed on the Powerplay

Posted on January 13, 2016 by Matt Cane — 1 Comment
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This post originally appeared on Hockey Graphs.

Alexander Semin did not have a good season last year. After producing decent numbers in his first two seasons in Carolina, with 35 goals and 51 assists in 109 games, Semin struggled in 2014-2015, putting up only 19 points over 57 games and seeing his shooting percentage drop below 10% for only the second time in his 10 year career. With three years remaining on a contract paying $7MM per season, the Hurricanes decided to cut their losses, buying out the Russian winger prior to the start of the UFA period in July.

While at first glance Semin’s release seems like a reasonable response for a former top scorer who appeared to have lost the magic touch, if we look at little closer at Semin’s numbers a different story beings to emerge. Semin logged only 1.5 minutes of powerplay time per game in 2014-2015, down more than 2 minutes from his 2013-2014 total, and well below the 4+ minutes he would see at the start of his career in Washington. While other factors certainly played a role in his fall from grace (a 97.5 PDO at 5-on-5 doesn’t help), there’s no denying that the coaching staff’s decision to keep Semin off the ice when the ‘Canes were up a man cost him (and likely the team) points.

Although Semin is an extreme case, the general story of a player losing points as his powerplay time decreases is not uncommon amongst NHLers, and illustrates that opportunity often matters just as much as ability when it comes to a player’s results. Each team’s powerplay minutes are limited, and valuable to both the team and player, given the higher scoring environment that exists when a team is up a skater. Overall, teams scored roughly 25% of their goals on the powerplay last year, despite the fact that less than 20% of total ice time was played with a team on the man-advantage.

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

Posted on December 22, 2015 by Matt Cane — 1 Comment

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This post originally appeared on Hockey Graphs.

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 can we measure a goalie’s rebound control? Examining Pekka Rinne and James Reimer.

Posted on November 10, 2015 by Matt Cane — Leave a comment
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This post originally appeared on Hockey Graphs.

Pekka Rinne is good at controlling his rebounds. I know this, because people on the internet have made their opinions abundantly clear. Scouts and fanalysts alike credit Rinne’s quick glove hand with helping him catch a significantly higher volume of shots than most other goalies, leaving few opportunities behind for lurking opponents to deposit into his net.

James Reimer is not good at controlling his rebounds. I know this, once again, because people on the internet have made their opinions abundantly clear. Reimer’s (supposed) inability to prevent the shots he’s saved from bouncing into dangerous areas is often cited as one of the main reasons for why he should be the #2 goalie behind Jonathan Bernier on the Leafs’ depth chart.

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A New Way To Measure Deployment – Expected Faceoff Goal Differential

Posted on November 5, 2015 by Matt Cane — Leave a comment

This article originally appeared on Hockey Graphs.

Zone starts are not that great of a metric. Although certain players do tend to be put out almost exclusively for offensive or defensive purposes, the reality is that for most players’ zone starts have a relatively small effect on a player’s performance. And yet, many hockey writers still frequently qualify a player’s performance based on observations like “they played sheltered minutes” or “they take the tough draws in the defensive zone”. Part of the problem is that we’ve never really developed a good way of quantifying a player’s deployment. With many current metrics, such as both traditional and true zone starts, it’s difficult to express their effect except in a relative sense (i.e. by comparing zone starts between players). So when a pundit says that a player had 48% of his on-ice faceoffs in the offensive zone, it’s difficult to communicate to most people what that really means.

Going beyond that, even if we know that 48% would make a player one of the most sheltered skaters in the league, the question that we should ask is so what? Simply knowing that a player played tough minutes doesn’t give us any information that’s useful to adjust a player’s observed results, which is really the reason that we care about zone starts. We know that if you start your shifts predominantly in the defensive zone, you’ll likely see worse results, but zone start percentages don’t tell us how much worse they should be. Traditional deployment metrics are too blunt of a tool – they provide a measurement, but not one that gives any context to the performance numbers that we really care about.

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Why teams should use 4 forwards on the powerplay

Posted on September 29, 2015 by Matt Cane — 4 Comments

A few days ago, James Mirtle of the Globe and Mail brought up one of the first significant shifts in tactics under the Mike Babcock regime in Toronto.

While the change may be surprising to some fans, particularly given the lack of depth in the Leafs forward corps, it shouldn’t be altogether unexpected. Read more ›

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