How Can We Quantify Power Play Performance In Formation?

Screen Shot 2016-02-26 at 4.55.31 PM

Last week I wrote about a new metric, ZEFR Rate, which measures zone entry success on the power play and is relatively repeatable and predictive of future goal scoring efficiency. The metric was based around the idea that getting into formation efficiently — most frequently a 1-3-1 — is a catalyst for power play success.

But now let’s say you’re a team that has perfected your entry scheme, and you find yourself setting up in formation at a consistent rate. What now? How can one maximize one’s use of possession in formation to score goals at the highest possible rate?

Continue reading

Hockey Talk: Shot Quality

Embed from Getty Images

Hockey Talk is a (not quite) weekly series where you will get to view the dialogue among a few Hockey-Graphs contributors on a particular subject, with some fun tangents.

This week we started from a Twitter conversation suggesting that expected goals calculations (xG) might underweight “shot quality”. A topic that HG contributors are hardly short of opinions on. Continue reading

xSV% is a better predictor of goaltending performance than existing models

This piece is co-authored between DTMAboutHeart and asmean.

Analysis of goaltending performance in hockey has traditionally relied on save percentage (Sv%). Recent efforts have improved on this statistic, such as adjusting for shot location and accounting for goals saved above average (GSAA). The common denominator of all these recent developments has been the use of completed shots on goal to analyze and predict goaltender performance.

Continue reading

Practical Concerns: Supercharging the Eye Test with Microstats

Analytically-minded hockey people, as a group, tend to dismiss the eye test as biased. There is, of course, some truth in that position. “What you see is not all there is.”

However, I think it would be misguided to look at traditional viewing-based scouting and saying that it just doesn’t work. We shouldn’t forget what makes a human being different – and, in some ways, better – than a machine.

There is no camera as versatile as the human eye, and there is no computer as sophisticated as the human brain. So instead of disempowering those tools, why not try to make them better?

Let’s go back to high school for a second.

Continue reading

Another Shot Quality Quandary: League Variance, Evolution, Error

File:Alberti-Young-Hockey-Players-alb11bw.jpg

Young Hockey Players” by Piotr Alberti, via Wikimedia Commons

Hockey statistical analysis isn’t really capturing all of hockey, or seeking to package it; it’s about getting as close we can to the essence of the thing. All the ideas, conclusions, best practices that we’ve cobbled together over the years give us an approximation of the actions a team, a player, or a fan could make going forward to better grasp the game.

Within this fact lies the greatest bone of contention for the hockey stats crowd, and the frequent refrain of critics who can only chirp from the sidelines. “Have you considered measuring this? Have you considered measuring that? Have you removed the games when the Rangers lacked sufficient compete level? Have you adjusted for Hamburglar’s pre- and post-lifetime gift certificate to McDonald’s?” While some of these adjustments may be worthy, and others utterly ridiculous, “shot quality” has been a persistent critique of the use of all shot attempts.

Admittedly, there are some interesting developments in Ryan Stimson’s work on puck movement, which might shed some light on an area yet explored. Though it’s not necessarily his focus, I think his data can give us an idea of how possession is maintained effectively. The remainder of shot quality, or at least the way it’s being conceptualized, lies in these remaining areas: type of shot, where shot is located on net, screened/tipped/direct/clear-look shot data, shooting talent, and where on the ice the shot is taken from. The former two, according to Gabe Desjardins, didn’t really demonstrate themselves when he came across the data (nor when I asked him a month ago). Shot location has already died a partial death by Desjardins, who found it seems to have minimal impact on save percentage, though he also found a team talent component, to the tune of differences ranging up to 0.7 feet.

Let me put the location stuff to bed the rest of the way.

Continue reading

The NHL Systems Argument: Comparing Bruce Boudreau, Alain Vigneault, & Lindy Ruff

Bruce-Alain Ruff. Looks like the ghost of Gene Hackman. You're welcome for the nightmares.  Composite of images by

“Bruce-Alain Ruff. Looks like the ghost of Gene Hackman. You’re welcome for the nightmares.” Composite of images by “DSCF1837” (Vigneault), Michael Miller (Boudreau), and Arnold C. (Ruff), via Wikimedia Commons*

Systems are without question the most elusive, yet most important, part of our understanding of hockey and the application of analytics. What works and what doesn’t? To what degree can a coach or team apply a strategy?

This led me to think about where we might most convincingly see evidence of a system at work. In the past, we here at HG have had a lot of skepticism about a number of elements of a “system.” For example, Garik’s pieces on competition-matching lines (here and here) and the use of the “defensive shell” to protect a lead, neither of which presented themselves as particularly effective ways of looking at or implementing systems. I have shown in the past that attempts to use extreme deployment in terms of zone starts doesn’t move the needle beyond a 60-40 range of possession, the range of shooting shares for forwards and defensemen haven’t seemed to change much over the last 20-25 years, and a plotting of even-strength shots-for with top and bottom possession teams do not suggest a major difference in shot location.

So where to go from there? Eventually, I decided that we need to get to an extreme enough situation, with robust enough data, where a team might have the best opportunity to dictate a system — in other words, we need to look at the powerplay. The most ideal opportunity for comparison, given the workable data for me, comes from the coaching careers since 2008-09 of Bruce Boudreau, Lindy Ruff, and Alain Vigneault. They all provide at least a couple of seasons with different teams, in addition to a robust set of coaching data from 2008 to the present. Let’s see what we can see…

Continue reading

Hockey Talk: On player control over save percentage

Courtesy of Wikimedia

Welcome back to our semi-regular segment where I will touch on a few trending topics in hockey statistics in a less mathematical and more discussion-based format.

This week we will explore the debate on player defensive impact on shot quality and save percentage.

So let’s begin.

Continue reading

Spread of NHL Team Shooting Performances, Year-to-Year 1952-53 through 2013-14

Sort of a mid-week quick graph…I’ve been compiling data for a different project and curiosity got the best of me to see what the spread in team shooting percentages was in NHL history. We all know that shooting percentage in the NHL went up substantially during the 1980s, but what you’re seeing above is one of the reasons why we theorize that shot quality and team shooting talent might have figured more greatly in outcomes in the 1980s than it does today. With some exceptions, the standard deviation seems to have settled from about 1996-97 to the present at just under 1%, which suggests our expectations from one year to the next should only allow a team that much of a bump above or below league-average. It’s worth noting that sample will affect this measure, hence why our line is so spiky during the Original Six era, and why 1994-95 and 2012-13 might have not been as characteristic of a trend. Incidentally, this is shooting percentage for all situations.

Note: As mentioned by a reader, increased scoring is going to work together with this standard deviation to accentuate the differences between teams. League-wide, the shooting percentage and standard deviation move well enough together to cause this effect, usually portrayed by coefficient of variance, to regress heavily from 1965 to the present. The exceptions, though muted, would be the early 1980s and the more recent years of Dead Puck, so the standard deviation fairly accurately represents our variance above. CoV data:
Continue reading

Hockey Talk: Thoughts on Save Percentage and Shot Quality

(Image courtesy of Wikimedia)

Welcome to a brand-new, semi-regular segment where I -Garret Hohl- will touch on a few trending topics in hockey statistics in a less mathematical and more discussion format.

This week we will explore the debate on shot quality impacts on save percentage.

So let’s begin.

Continue reading

The State of Save Percentage

Image from Wikimedia commons

Currently save percentage is the single best statistic for evaluating goaltenders… which is unfortunate as save percentage is extremely rudimentary and a suboptimal statistic.

There are two important factors for a statistic to be useful: that it impacts wins and the individual can either control or push the needle. Save percentage has both. Continue reading