In July of 2016, Dom Luszczyszyn released a metric called Game Score.  Based on the baseball stat created by Bill James (and ported to basketball by John Hollinger) the objective of game score is to measure single game player productivity.

While it’s often easy to compare players across larger sample sizes, comparing two different players’ performance on a given night can be difficult. If player A has a goal, two shots, and took a penalty, did that player outperform player B who had two assists and one shot? Game score attempts to answer that question by weighting each of the actions of each player to give us a single number representing their overall performance in that game.

Unlike Dom, whose main goal was to create a better way to evaluate single game performance, mine was to create a better statistic to evaluate the total contributions of players. There are no advanced metrics, like Corsi For percentage, or even Goals For percentage, available at this time in the NWHL. Because of this, points are the best way to evaluate players, even though other box score stats are available.

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Yesterday, the third annual Rochester Institute of Technology Hockey Analytics Conference was held. Below are links to the slides for each presenter, as well as links to a stream of the morning and afternoon sessions. Please refer to this post for the time of each person’s talk or panel. More detailed recaps are undoubtedly coming from people, so this is simple a reference for streams and slides for those that missed the event or would like to revisit certain talks.

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Despite the aura of calm projected by Golden Knights owner Bill Foley, his nascent desert franchise is already on the clock. The recent announcement that the Oakland Raiders will be moving to Las Vegas in 2019, has already undermined Foley’s plan to be the only show in town. If the Golden Knights don’t win over the Vegas fanbase in relatively short order, it could prove almost impossible for hockey to get ever get a foothold in Sin City.

As a result, the team faced a variety of difficult decisions going into the 2017 expansion draft. On one hand, the team could try to win immediately with aging veterans like Eric Staal, which would allow them to establish a foothold in the market, but also put them at risk of years of mediocrity as older players lose their fights with Father Time. On the other hand, the team could tank in the hopes of finding stars at the top of the draft, but the resulting efforts could further exacerbate the fan bases preference for the incoming NFL juggernaut.

In order to evaluate the quality of the selections of GM George McPhee, I viewed each pick as a “trade” and applied prototype of a “Trade Machine” to look at each selection, given the choices available. For example, Vegas chose Clayton Stoner and Shea Theodore from the Ducks over Sami Vatanen, which means, in essence, the selection was a trade for Theodore and Stoner for Vatenen straight up. After looking at all 31 selections, I compared Vegas’ actual roster to one consisting of an optimal roster calculated using DTMAboutHeart’s GAR statistic. The results are below.

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Adam Stringham was joined by: Chris Watkins, Namita Nandakumar, Garik16 and Shayna Goldman to redo the 2017 NHL off-season!

Here are the rules that we used for the mock off-season:

• The rosters were rolled back to the start of free agency.
• The Salary Cap has gone up 10% to 82.5M
• Max contract length is 5 years
• There are player and team options (so Connor McDavid could sign a 4 year contract with a player option for the 5th year)
• All no trade clauses are void, and teams can go over the cap to sign their own players (up to $90M)
• There is no compensation for offer sheets. the team can either match or the player walks for free.

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Asmae Toumi, Editor-in-Chief
atoumi.cu@gmail.com

Hockey-Graphs
Mentorship Program

Overview

Inspired by Python’s Core Mentorship Program, we believe the best way to increase diversity in the hockey analytics community is to connect experienced and dedicated mentors with interested beginners. The aim of the Hockey-Graphs Mentorship Program (HMP) is to inspire people from various backgrounds, especially underrepresented persons, to contribute to the flourishing hockey analytics community.

This is where HMP mentors come in. Mentors will provide beginners with the support, guidance, and encouragement they need to 1) learn about statistics/analytics and 2) use that knowledge to answer questions pertaining to hockey. In addition to 1-on-1 mentoring, mentees will be given a tailored guide with additional resources to strengthen their knowledge and skills. Mentees will also receive priority access to various HMP-sponsored workshops and social events hosted at major hockey analytics conferences.

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