Hockey Talk is a regular 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 had a spontaneous discussion on black box data and why analytics departments may be misused.
Listen; I get it. Some people are sick and tired of this supposed debate that’s been ongoing for over ten years now. But what really is the actual debate all about? What is it we are arguing on Twitter over? What should we be aware of?
It seems as if NHL teams are trying harder and harder to strike a balance with respect to the handedness of their defensemen. This idea is substantiated when looking at the falling percentage of available icetime played by D-pairings consisting of a defenseman playing his off-side…
On Monday, fans tuned into one of the quieter NHL trade deadlines in recent memory. Despite the slow pace of movements, Matt Cane (@cane_matt) and I set about making visuals to give our takes on each trade.
Here, we’ll look back on a few of our takes from the trade deadline. We’ll focus ourselves with three categories – a trade where we had a similar take, a trade we disagreed on, and our favourite viz from the day.
Visualization seems pretty easy, so it’s often left as an afterthought. But visuals can be an immensely effective—or destructive—form of communication. To that end, many, if not most, people fail to tap into its power because of they make prominent mistakes. (Sorry for the ego blow, homies.).
But that need not be the case. Although visualization is a process, not a result, once you know what to look for, you can easily cut down on those big mistakes and make graphs that—while not perfect—will be consistently good.
Here are a few things to keep in mind for hockey bloggers, adapted from Andy Kirk as well as Dieter Rams’ 10 principles of good design.
For our purposes, they can be summed up as “think about your readers while recognizing your practical limitations.”
More data! We’re up to around 275 games now (hope to hit 400 by the playoffs) and I thought it would be a good time to share some more. Today you’ll be getting something new from the previous releases: which players were on the ice for each passing sequence. Now, I’m writing this short post to explain a few things about this process.
Probable, ‘Cause is a new feature where the Hockey Graphs staff give their predictions about the big unknowns in the hockey universe today. We’ll offer a question and give the reasons why it will and won’t happen, and then our estimate of the probability that the answer will wind up being yes. At the end of each question you’ll have the chance to submit your predictions as well, and we’ll review the group’s answers in the next edition of Probable, ‘Cause. This week, we’ll look at Braden Holtby’s odds of entering the NHL record books, whether Erik Karlsson can keep up his hot hand, and how nervous broadcasters should be at the prospect of an all-American playoffs this year.
Introduction
One of the greatest challenges in sports analytics is determining the skill of a player independently of quality of teammates. While a number of tools already exist (e.g. WOWYs in hockey), their (mis)use lends itself to significant limitations and collinearity concerns. This is where regression-based approaches can provide a more rigorous alternative in isolating a player’s true talent.
An encouraging development in hockey analytics as of late has been Ryan Stimson’s Passing Project, which you can read about here. The goal of this post is to introduce a regression-based method to estimate an NHL player’s expected scoring performance independently of the passing strength of his teammates. To this end, player and linemate data from Stimson’s Passing Project and Muneeb Alam of the 2014-2015 season were used to devise a rate-based metric of a player’s projected goals. The difference between a player’s projected goals per 60 minutes and actual goals per 60 minutes will be called Delta Box Score or DBS.
One of the attributes that is often attached to defensemen is the ability to clear rebounds. You hear this quite often on NHL broadcasts, usually after a flurry of rebound shots that ultimately wind up in a goal. The colour commentator will jump in and imagine the goalie is saying to his defensemen, “I’ll stop the first three, you get the fourth one.”
But just how much is there to defensemen’s ability to prevent rebound shots?
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