Practical Concerns: How To Replace Superstar Goal-Scoring

Photo Credit: Derek DrummondPhoto Credit: Derek Drummond

Earlier this week, I read an interesting story by Frank Seravalli on TSN.ca on Mike Babcock, Phil Kessel and the Toronto Maple Leafs, which is a good read for anything interested in how coaches think. For me, it also illustrate another way analytics could be employed to make a coach’s life a lot easier and take out some of the guesswork inherent in the job.

It was not too surprising to hear that Babcock was already thinking about how to get the most out of his team this season while on vacation, but I am very curious about his thought process behind how best to replace Phil Kessel. But before we start thinking about how to replace Phil Kessel (or his production in aggregate), we need to start thinking about how we are to measure a Phil Kessel’s offensive contribution.

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Hockey Talk: Should players care about their advance statistics

A Caucasian ice hockey player wearing a white jersey with a blue and orange circular logo with the word
Jordan Eberle – Edmonton Oilers” by Lisa Gansky from New York, NY, USA – IMG_1468. Licensed under CC BY-SA 2.0 via Commons.

Hockey Talk is a (hopefully) weekly series where you will get to view the dialogue amongst a few of the Hockey-Graphs’ contributors on a particular subject, with some fun tangents.

This week we look at whether or not players should care about their advance statistics (with a tangent on talent distributions impact on hockey):

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Rate Metrics Matter

The other day, @Moneypuck_ and @SteveBurtch had a conversation about the Prospect Cohort Success Model:

https://twitter.com/SteveBurtch/status/638216552085635072

https://twitter.com/SteveBurtch/status/638216835679277056

https://twitter.com/SteveBurtch/status/638217390518632449

While the PCS model is interesting in its own right, I found the discussion about the methods we use to analyze players to be interesting as well.

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NHL Analytic Teams’ State of the Union

Pure-mathematics-formulæ-blackboard

Fandom means a lot of different things to different people. But one thing unites us all: we hope our favorite team will win, and spend a great deal of time thinking how they can.

For those of us who dig a little deeper on the “how” side and use analytics, we hope that our work will eventually make its way to a front office. In some ways, it already has: numerous “hockey bloggers” hirings have been made recently.

But how many and for which teams?

With some research, I’ve culled a working document on all analytics hires for NHL teams and how they may be using analytics. The following descriptions comes from a variety of sources including Craig Custance’s Great Analytics Rankings [Paywall], fellow bloggers from across the internet, media reports, word of mouth and anonymous insiders.

It should be noted that just because a team has made an “analytics hiring”, it doesn’t necessarily mean that they value their input or use the analysis provided properly. In fact, hires can be made simply for PR reasons, and some teams may even give analytics tasks as secondary duties to staff members who do not posses any formal background in the subject. Teams may also have hired private firms providing proprietary data, which in reality may not provide any tangible, verifiable value than what is free and readily available online.

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Practical Concerns: What the US Open can tell us about where hockey is headed

2014 US Open (Tennis) - Tournament - Roberto Bautista Agut (14914449990)

I’m a big believer in looking to other sports for inspiration and ideas, whether it’s in terms of cross-training or in terms of analytics. Many smart hockey people I know are big baseball and soccer fans. I’ve never sat through an entire MLB game, and the part that fascinates me the most about soccer is the penalty shootout, so I’m not really part of that group. I think hockey has a lot to learn from Formula One in terms of how to adopt new technology, but I really wished more folks in hockey would pay closer attention to what is going on in tennis.

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Practical Concerns: Analytics as technology

As I alluded to in my previous post, the choice of words is very important when selling ideas to a coaching staff. Semantics lets us see the same idea from different angles, and can be a very powerful way to alter our understanding of a subject matter.

Recently, I’ve began to refer to hockey analytics tools (possession metrics, Player Usage Charts, HERO Charts, dCorsi, etc.) as technology, which has allowed me to relate better with those less well-versed on the matter and have all sorts of interesting discussions with people who otherwise wouldn’t give advanced stats the time of day.

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It’s not about the numbers: working in analytics for an elite hockey program

Recently, I’ve received some unsolicited emails from some very smart young people about working in analytics for a hockey team. There are definitely people more qualified that they could’ve tracked down, but most of them are not allowed to talk about their jobs, so I guess they were stuck with me.

It felt a little bit strange corresponding with these mathematics or engineering students, because theoretically, their backgrounds are a lot more suited to this line of work than mine (I graduated in Marketing). I apparently passed Calculus II 10 years ago (I barely remember taking it). I’m a mediocre programmer. And I don’t even work in the NHL.

But there are still a few thoughts I could share.

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