“Goaltenders are voodoo.” *Ahem* BULLSHIT. As a goaltender myself, I find this mantra lazy. After all, the position mostly comes down to geometry - a goalie’s size, angle, and depth at the time of a shot combine to formulate the percentage of net covered and therefore significantly influence the likelihood of a goal. Angle and depth are controllable, while size is mostly a product of genetics (and the size of your Vaughn set-up. Looking at you, Ryan Miller). Now, in a perfect world, the NHL would publish the player and puck tracking datasets that are allegedly arriving this preseason. With this data, a model can be formulated to derive the percentage of net covered, explore its correlation with save percentage, identify available goalies who excel at positioning, and identify rostered goalies who may benefit from additional skill engineering in this area. Until that time comes, we’ll make do with what we have - enriched PBP datasets.
Prior to his dismissal, consistency was a theme that now ex-coach John Hynes had been citing to explain the underperformance of his unit through November. Using statistical estimates of variance, we can provide a quantitative perspective to this claim with focus on to two primary questions:
7:45 PM on February 22nd, 2018. Vivid is the memory of where you were and what you were doing the moment the entire National Hockey League stood shellshocked in the wake of the season's most colossal trade... Well, not quite...
In the most moderately whelming transaction of the trade deadline, Michael Grabner was shipped across the Hudson to join the New Jersey Devils as a result of the first ever trade with the rivaled New York Rangers.
Flashback to two weeks ago. The Devils had just rung in the New Year with consecutive losses to Buffalo, Washington, and St. Louis. Amid the team’s struggles came a negative focus on the performance of the defense, and perhaps rightfully so. Specifically, much of the chatter was focused on the pairing that was Andy Greene and Steve Santini, a tandem that was posting some very poor number across the board. While I’m not fully willing to exonerate our Captain and his developing sidekick, I would like to add a discussion of context to the debate. Many of the comments and articles I read had incessantly harped on the results of their play which were admittedly poor. However, there’s more to this conversation than looking at one number and passing judgement; we must also give great consideration to the context in which these results were produced.
Using Multiple Linear Regression to Create a Weighted Average Adjusted xGF% Considering Contextual Factors
There is a timeless debate in the hockey analytics community regarding the use of singular metrics (GF%, xGF%, DTMAboutHeart’s WAR, Emmanuel Perry’s K or WAR, Dom Luszczyszyn's GameScore, etc) to quantify the result of a player’s performance in one all-encompassing number. There is also an equally extensive debate on how to evaluate these measurements while acknowledging environmental factors that influence a player’s on-ice result. Hockey has a notoriously high number of inputs that carry equally high degrees of complexity. How should we address a scenario where two players achieve the same end result under drastically different circumstances?
The Devils overhauled the blueline in the past few months, adding Will Butcher, Mirco Mueller, and Sami Vatanen. Let's see how it's been working:
With the holidays right around the corner, the Devils find themselves just a point out of first in the Metro. Let's take a look at how each forward personnel combination has been contributing to this surprising outcome:
Did The Devils Become A Better Team?
My heart says no, but my head says… well, let’s find out:
I wanted to write this piece 2 months ago before the season ever started. I sat down, fingers clacking away on my laptop, and began writing. The creative process was running on all cylinders until I went to get some data for the Devils 2017-18 roster and realized… 5 key players expected to contribute to the Devils this year had no data on them. Making assumptions on how guys like Nico Hischier, Jesper Bratt, Will Butcher, Mirco Mueller, and Michael McLeod would measure up statistically would be a frivolous act of data journalism on my part. So, here I am 12 games later (15% of the way through the season) to commit a marginally less egregious act: using small sample sizes to make bold claims! Let’s begin:
Much like I used a cluster analysis to analyze the defense in this post I did the same for forwards. There are two charts: one categorizing Player Type based on how many points he scored (P60) and goals against he conceded (GA60), and the other depicting the Scoring Type using goals per hour and assists per hour. Take a look, play with the filters, and notice that NJ players are circles whereas all other skaters are plus signs: