Hockey Graphs and Vancouver Canucks Co-Host Vancouver Hockey Analytics Conference 2017



Hockey Graphs is excited to announce that we will be co-hosting the Vancouver Hockey Analytics Conference (#VanHAC) with the Vancouver Canucks along with HockeyData and Canucks Army.

Date: Saturday, March 11th, 2017

Location: Rogers Arena, Vancouver, Canada


The call for speakers is currently open with a deadline of January 10th, 2017.  See the website for more details or go here to submit your submission.  

Registration has yet to open as we tabulate the final costs to host the venue, among other factors. Check back here or on Twitter for more information when it will open. (Note: Expect participants to be capped at around 100 people.)

Watch the VanHAC page for updates as they are released!

Introducing the 2016 – 2017 Forechecking Project

Passing and Zone Entries are so last year.

When Corey Sznajder decided to track microstats for the upcoming season and began incorporating my passing concepts into his work on last season’s playoffs, I wondered if we really needed to track this season. Instead, Corey and I chatted a bit and decided the best use of everyone’s time would be if myself and the other passing project volunteers continued to work on last season*, with the hope that we can build a solid sample by the time Corey finishes the 2016 – 2017 season. Having two (nearly) full seasons of data would be excellent to have.

However, this also gave another idea to explore something we really haven’t done a lot of: forechecking.

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Testing and Final Remarks

(Photo by Andre Ringuette/NHLI via Getty Images)

(Photo by Andre Ringuette/NHLI via Getty Images)

This is Part 5 of a 5 part series detailing my WAR model, Part 1 of the series can be found here, Part 2 of the series can be found here, Part 3 of the series can be found here and Part 4 of the series can be found here.


In the beginning of this exercise I set out to try and encapsulate the best estimate of a NHL player’s true value. An adjusted plus-minus system (XPM) was introduced to help contextualize shot attempt numbers. An box plus-minus system (BPM) was introduced to help contextualize metrics such as goals and assists. Ability to win faceoffs as well as to draw and not take penalties were also included

“WAR is not meant to be a perfectly precise indicator of a player’s contribution, but rather an estimate of their value to date. Given the imperfections of some of the available data and the assumptions made to calculate other components, WAR works best as an approximation. WAR is trying to answer the time-honored question: How valuable is each player to his team? Comparing two players offensively is useful, but it discounts the potential contribution a player can make by saving runs on defense or special teams. WAR is a simple attempt to combine a player’s total contribution into a single value.

The goal of WAR is to provide a holistic metric of player value that allows for comparisons across teams and years and a framework for player evaluation. While there will likely be improvements to the process by which we calculate the inputs of WAR, the basic idea is something fans and analysts have desired for decades. WAR estimates a player’s total value and allows us to make comparisons among players with vastly different skill sets.  (FanGraphs). 

The final study will examine the repeatability and predictiveness of the WAR components.

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Extras, Blending and Seasonal Adjustment

(Photo by Rich Graessle/Icon Sportswire)

(Photo by Rich Graessle/Icon Sportswire)

This is Part 4 of a 5 part series detailing the my WAR model, Part 1 of the series can be found here, Part 2 of the series can be found here and Part 3 of the series can be found here.


Now that we have covered the overall player models here and here, we will explore how to blend these two together to achieve maximum out-of-sample predictive power. We will touch on what I have coined the “extras” section made up of penalties and faceoffs. Faceoffs are a fairly standard and well accepted player skill, even though it is overvalued by many hockey “traditionalists.” Penalties are an aspect of player analysis that typically goes unaccounted for in most current analysis. Finally, we will implement a yearly adjustment most commonly used in baseball WAR.

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Introducing Box Plus-Minus

EDMONTON, AB - OCTOBER 23: Connor McDavid #97 of the Edmonton Oilers skates during a game against the Washington Capitals on October 23, 2015 at Rexall Place in Edmonton, Alberta, Canada. (Photo by Andy Devlin/NHLI via Getty Images)

(Photo by Andy Devlin/NHLI via Getty Images)

This is Part 3 of a 5 part series detailing the my WAR model, Part 1 of the series can be found here and Part 2 of the series can be found here.


Box Plus-Minus (BPM) is a box score-based metric for evaluating a hockey player’s quality and contribution to the team. It is very different than an Expected Plus-Minus type model, which is a play-by-play regression metric. BPM relies on a player’s box score information to estimate a player’s performance relative to replacement level. Box Plus-Minus type metrics have long populated basketball circles, there is a great summation of some of the original creations here, with many newer versions popping up including Dredge, DRE and Player Tracking Plus Minus. A version has even already been brought to hockey in the form of Game Score. Here I will attempt to create my own version of Box Plus-Minus for the NHL.

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Introducing Expected Plus-Minus


This is Part 2 of a 5 part series detailing the my WAR model, Part 1 of the series can be found here.


“Basically, anything that WOWY can do, I think can be done better with regression-type methods”Andrew C. Thomas, Lead Hockey Researcher Minnesota Wild

Adjusted Plus-Minus metrics were first introduced into NBA circles around 2004 by Dan Rosenbaum. The basketball community has since seen many iterations including; Steve Ilardi/Aaron Barzilai, Joseph Sill and Jeremias Engelmann. Soon after these metrics made their debut into the public sphere they were adopted for hockey and have themselves seen many different iterations; Schuckers/D.Lock/Wells/Knickerbocker/R.Lock, Brian Macdonald, Gramacy/Jensen/Taddy, Thomas/Ventura/Jensen/Ma and Emmanuel Perry. I even made my own attempt in the summer of 2015 which I coined Corsi Plus-Minus. These metrics have struggled to take hold amongst the hockey community for whatever reason, unlike in basketball circles.

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A Primer on @DTMAboutHeart’s WAR Model



Hockey stats have existed for about as long as the game itself. Simple boxscore stats such as goals and assists can be traced back almost a full century now. These stats have helped inform fans, coaches, and managers of the value held by players. Around the 1950’s the Montreal Canadiens began to track a player’s plus-minus with the idea that simple boxscore stats failed to capture many important elements of a game. Plus-minus was a good start towards tracking impact that is not realized in traditional boxscore stats, but has been recently shown to be quite incomplete and lacking by modern evaluation standards.

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2016-17 Hockey Graphs Top 50 Players

Embed from Getty Images

Welcome to the second annual Hockey Graphs Top 50 Players in the NHL list.

The main reason I put this together last year (you can view that here) was as a basis for comparison against the other, more famous, top 50 players lists. The annual list is a season preview staple for TSN and THN and the rankings are usually slightly controversial. Both lists are created via a poll of various people inside hockey, who are generally very smart people, but who are also prone to old-school thinking with value sometimes being shaped by recency bias, reputation and a winning pedigree.

This list is a bit of the opposite as it comes from mostly outsiders, people who study and analyze the game in the public sphere. That’s not to say these are necessarily smarter people, they just approach the game from a different angle based mostly on underlying trends and numbers over more traditional stats and what is immediately seen on the ice.

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Just How Important is Quality of Competition? Very. Also, not much. It’s All Relative.

*This post is co-authored by DTMAboutHeart and Ryan Stimson*

Recently, the topic of Quality of Competition has been at the forefront of Hockey Twitter. This post hopes to articulate some of the nuance associated with Quality of Competition, as well as Quality of Teammate, metrics and how impactful they are. To do that, we will revisit methods outlined here by Eric Tulsky, namely splitting the competition and teammate quality by position and measuring the impact of each split. Ryan recently wrote about this at the NCAA level, but it has not been looked at with much rigor at the NHL level.

Both Quality of Competition and Quality of Teammates matter. They also don’t matter. It depends on the position and metric you’re looking at. All TOI data is 5v5 and from Corsica. Ryan had the game files of who was on the ice during each 5v5 shot from Micah Blake McCurdy, so that data was used as well. Also, thanks to Muneeb for feedback during this process. Thanks to all!

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