Credit: Michael Miller – Creative Commons
I was watching hockey a few nights when I heard NBC’s Pierre McGuire describe a rookie defenseman in glowing terms. It was the same kind of praise he used to shower upon Dion Phaneuf about 10 years ago, and this young player had very similar attributes to an early-20s Phaneuf: a huge frame, a huge slapshot, and a willingness to use both in equal measures.
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.
From Wikipedia Commons
Over the past year, I based a lot of research off of former work by Tyler Dellow. It is a bit funny because I actually never read any of Dellow’s work until well after I started writing about underlying metrics in hockey. I knew of him, but mostly was brought up on Gabriel Desjardins, Eric Tulsky, Ben Wendorf (yes, Hockey-Graphs’ own Wendorff), and a few others. It is also a bit difficult now because Dellow’s website has gone dark with his hiring, which removed the work I quoted or built upon.
One Dellow article that will be severely missed is Two Graphs and 480 words will convince you on Corsi.
Dellow presented analytical data in simple and effective ways. It made understanding of complex concepts -such as regression in goal differentials- easy.
There is nothing perhaps more encouraging to fans of struggling teams than to see a rookie D-Man come up and put up big numbers right out of the gate. I speak of course, not just about goals and assists – in this case I refer to good possession #s (Corsi, Fenwick, and the relative versions thereabout). Fans of the Oilers (Marincin), Leafs (Rielly), Isles (de Haan, Donovan), etc, all seem to have higher hopes than they might’ve otherwise due to how well their rookie D has performed. After all, a top pair D Man (under control for cheap for years to come) can have such a great impact and they are extremely hard to find on the free market (or trade market).
But can these standout rookie D keep up their great performances? After all, we always hear about the so-called “sophomore slump” and it’s not like players disappointing after great rookie years is that uncommon. How certain can we be about the futures of rookie D-Men who come up and right away show strong possession #s? Let’s see how similar rookie D the last few years did.
For quite some time there has been a debate going on: those who think you should add a defenseman’s effect on save percentage into player evaluations and those who think that adding such information causes more harm than good to the analysis. Note that this does not mean defensemen do not affect save percentage. That is an entirely different stance.
When it comes to evaluating a player statistically, you want the number to account for two things: effect and control. If a statistic does not help quantify how a player improves their team’s chance at winning, it is useless in measuring effect. If a statistic has too much white noise or other contributing factors that it would take too large of a sample to become significant to the player’s contribution, it is useless in measuring a player’s control over the effect.
The following is a guest article written by Rob Vollman of Hockey Abstract and Hockey Prospectus fame. Enjoy!
Other than the goalie, a team’s top defensemen are arguably the most important players on the teams. Great ones like Nicklas Lidstrom, Scott Niedermayer and Chris Pronger can completely alter the outcome of an entire season almost single-handedly. Who were the top pairing defensemen this year, how will they used, and how effective were their teams when they were on the ice?
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Friday Quick Graphs are (initially) intended to revisit some of the better, potentially more-significant work I’ve posted over the past year on my Tumblr page (if you want to beat me to some of them, take a look at benwendorf.tumblr.com).
What you see above is a “Total Player Chart,” or TPC, a chart I developed about a year ago to visualize a player’s time on-ice (TOI) deployment. Using that chart, I took the NHL player population from 2007-08 through 2011-12 and recorded the year-to-year change in player’s TOI relative to their age and age +1 seasons. I took those trends and placed them upon an average 18-year old defenseman’s ice time, and tracked how that hypothetical player’s TOI would evolve if they played to the age of 40. The result is the GIF above.
For frame of reference, the hypothetical player is the dark blue triangle, the light, dotted triangle is the league average across the player population, and the light blue triangle is the league high in each situation.
As you can see, the trend is that young player’s tend to receive 5v4 minutes, and as they age they become more trusted with 4v5; as they get older, the 4v5 minutes stick around, but the 5v4 minutes fade.
It’s worth pointing out that this hypothetical defenseman, overall, is likely to be a decent player, by virtue of the fact that they would be getting NHL minutes at age 18 in the first place (and playing until 40).
Hockey Graphs 