Graph courtesy of @MannyElk
ABSTRACT
Shot quality versus quantity was once debated intensely in the hockey analytics blogosphere; however, this has since diminished severely. Still, many in the general public struggle with the idea of something that is important for players and teams to strive for doesn’t add much in data analysis. This exercise helps demonstrate some of the concepts using data.
Photo by “User:Zucc63” via Wikimedia Commons, modified by author
If you’ll remember, one of the inaugural posts here was a regression prediction piece, using a combination of PDO and Fenwick Close to see who might improve or decline over the latter half of the season. I decided to put together a table of the teams I predicted would negatively or positively regress, just using the aforementioned data:
If you’ll remember, I pegged Anaheim, Colorado, Montreal, Phoenix, Toronto, and Washington for negative regression, and Florida and New Jersey for positive regression. So, even with really rudimentary predictors, this season I was able to be fairly successful building predictions from a half-season sample for the remaining season. In previous years, the fancy stats folks usually picked the much more obvious targets (Toronto being the big one this year), but it’s very possible to go further if you wanted.
This guy may be lying flat on his face like this more and more often as he’s reaching the big 35.
A few weeks back, I unveiled Hockey Marcels: an extremely simplistic system for projecting goalies performance going forward, utilizing just the last four years of a goalies’ play to do so. Building off of work by the great Eric T., I weighted more recent years more heavily than older ones, to try and give a better estimation to what we should expect from goalies going forward. In addition, I added a regression factor to Eric’s work, such that we could deal with varying sample sizes and the extreme variability of NHL goaltending.
But the one thing I didn’t include was an aging adjustment. This is an integral part of any serious projection system for the obvious reason: Using past years to project future data is sound, but players will be OLDER in the future and increased age generally results in worse performance (except for the really young). This is especially the case with hockey, where peak performance has been found to be at ages 24-25. If we really want to project goalie performance going forward, we need to find out how well goalies age.
A few people have looked at this before (both Eric and Steve Burtch have written about goalie aging in previous posts), but I wanted to actually get #s rather than just a graph on how aging affects goalies of all ages. So I used hockey reference to get the seasonal data of all goalies from 1996-1997 to the present season who had played 20 years, and tried to take a look.
Did you really think I was going to miss the opportunity to post the AMac with chains gif again? You thought wrong.
Islander Defenseman Andrew MacDonald is one of the hot names being bounced around during the trade deadline. On one hand, this makes sense: He’s making basically nothing on his current contract, he’s one of the time on ice leaders in the NHL this year and has handled top level competition for a few years now.
On the other hand, his conventional fancystats show a well…..massive decline:
Yikes. That 2013-2014 number is downright terrible, dropping MacDonald into the bottom tier of defensemen. And no zone starts and certainly not competition (see this article for an analysis of AMac vs various levels of competition) does not account for this. If you believed the fancystats, AMac isn’t just not a top tier DMan, but not even a 2nd or 3rd pairing guy who could help any team at all. Yet teams seem to believe he’s worth a high pick? So what’s going on? Is the conventional thought completely wrong here?
INTRODUCTION
I have always been of the opinion that Corsi is part of the larger puzzle in trying to gain greater understanding of the game and how a player can affect their team’s chance to win. Like all statistics though, it needs appropriate sample size and context, and will never tell you everything. Teammates, opponents, luck, system, strategy and what moments a coach deploys a player will always effect results… although, there can also be times where context is overly stressed. While Corsi does tend to need less context than many other hockey statistics, there are some things that need to be kept in mind in how two players with the same Corsi% are not always created equally.
Tyler Dellow wrote a piece on context that is definitely worth a read. In the article Dellow used two tables showing how Corsi changes dependent on ice time for the 2011-12 season.
We will revisit this article using a larger sample and look at both forwards and defensemen.
Hockey Graphs 