Quick Thoughts: On Drafting Goalies

A few days before the draft this year, I tweeted out a few ideas I had on the “Should you draft goalies early?” debate. In the interest of not losing these thoughts (and to avoid having to do any actual writing while still generating #content) I’ve pulled all those tweets together here, to preserve my insight and/or ignorance for all time.













Posted in Uncategorized

Does aggressive play on the penalty kill pay off?

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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Posted in Theoretical

Trade Deadline 2016

With the trade deadline fast approaching teams are wasting no time stocking up for the playoff push (or, if you’re in the unlucky camp that will be golfing come spring, unloading their assets to the highest bidder). For those of you looking for the stats-savvy skinny on the deadline, there are several super smart number-crunchers tweeting out their thoughts on the deals and answering questions using the hashtag #NHLTrade16.

If you want my view (and really, why would wouldn’t you), I’ll be participating in the aformentioned Tweet-hang (pretty sure that’s the technical term), giving my hottest takes in this handy all-in-one chart I’ve come up with.

nhltrade16_phaneuf.png

If you don’t want to scroll through the terrifying depths of my Twitter feed (and I can hardly blame you) I’ve linked all the analyses below:

Ottawa acquires Dion Phaneuf and 3 prospects from Toronto for Jared Cowen, Colin Greening, Milan Michalek, Tobias Lindberg and a 2nd Round Draft pick.

San Jose acquires Roman Polak and Nick Spaling from Toronto in exchange for Raffi Torres and 2 draft picks.

Colorado acquires Shawn Matthias from Toronto in exchange for Colin Smith and a 4th Round Draft pick.

Chicago acquires Andrew Ladd, Jay Harrison and Matt Fraser from Winnipeg in exchange for Marko Dano, a 1st Round Draft Pick and a conditional 3rd Round Draft pick.

Chicago acquires Christian Ehrhoff from Los Angeles in exchange for Rob Scuderi.

Chicago acquires Tomas Fleischmann and Dale Weise from Montreal in exchange for Phillip Danault and a 2nd Round praft Pick.

San Jose acquires James Reimer and Jeremy Morin from Toronto in exchange for Alex Stalock, Ben Smith and a 4th Round Draft pick.

Florida acquires Jiri Hudler from Calgary in exchange for a 2nd and a 4th Round Draft pick.

Florida acquires Teddy Purcell from Edmonton in exchange for a 3rd Round Draft pick.

Florida acquires Jakub Kindl from Detroit in exchange for a 6th Round Draft pick.

Pittsburgh acquires Justin Schultz from Edmonton in exchange for a 3rd Round Draft pick.

NY Rangers acquire Eric Staal from Carolina in exchange for Aleksi Saarela and two 2nd Round Draft picks.

Washington acquires Daniel Winnik and a 5th Round Draft pick from Toronto in exchange for Brooks Laich, Connor Carrick, and a 2nd Round Draft pick.

Los Angeles acquires Kris Versteeg from Carolina in exchange for Valentin Zykov and a conditional 5th Round Draft pick.

Colorado acquires Mikkel Boedker from Arizona in exchange for Alex Tanguay and 2 prospects.

Colorado acquires Eric Gelinas from New Jersey in exchange for a 3rd Round Draft pick.

Dallas acquires Kris Russell from Calgary in exchange for Jyrki Jokkipakka, Brett Pollock and a conditional 2nd Round Draft pick.

Anaheim acquires Brandon Pirri from Florida in exchange for a 6th Round Draft pick.

NY Islanders acquire Shane Prince and a 7th Round Draft pick from Ottawa in exchange for a 3rd Round Draft pick.

Boston acquires John-Michael Liles from Carolina in exchange for Anthony Camara, a 3rd Round Draft pick and a 5th Round Draft pick.

Boston acquires Lee Stempniak from New Jersey in exchange for a 2nd Round Draft pick and a 4th Round Draft pick.

Montreal acquires Stefan Matteau from New Jersey in exchange for Devante Smith-Pelly.

Anaheim acquires Jamie McGinn from Buffalo in exchange for a conditional 3rd Round Draft pick.

This page will continue to be updated as more trades come in, so check back for the most important numbers to know as we head towards Monday’s 3PM deadline for deals.

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Posted in Trade Deadline

Predicting Which Players Will Succeed on the Powerplay

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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Posted in Powerplay

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

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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Posted in Defence

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

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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Posted in Goaltending

A New Way To Measure Deployment – Expected Faceoff Goal Differential

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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Posted in Deployment, Theoretical
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