Month: August 2017

The Hockey-Graphs Podcast Episode 6: Mock Off-Season Part 2

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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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Hockey-Graphs Mentorship Program

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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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The Hockey-Graphs Podcast Episode 5: Mock Off-Season Part 1

stringhama Uncategorized Leave a comment

Adam Stringham was joined by: Chris Watkins, Namita Nandakumar, Garik16, Shayna Goldman and Carolyn Wilke 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.

Any comments are appreciated, the goal is to produce a podcast that people want to hear. Please subscribe to the podcast on iTunes!

Measuring the Importance of Individual Player Zone Entry Creation

Charlie O'Connor Neutral Zone Analysis, Player Evaluation 1 Comment

The importance of zone entries in hockey statistical analysis will come as no secret to anyone familiar with the public community at large. Back in 2011, then-Broad Street Hockey writer (and current Carolina Hurricanes manager of analytics) Eric Tulsky initiated a video tracking project that became the first organized foray into the zone entry question, and later resulted in a Sloan Analytics Conference presentation. Tulsky determined that “controlled” entries (those that came with possession of the puck) resulted in more than twice the number of average shots than “uncontrolled” entries, a key finding that provided concrete direction for additional research on the topic.

Tulsky’s initial Sloan project was limited, however, due to lack of data – only two teams had their full regular seasons tracked, and just two others reached the half-season threshold. As a result, further research would wait until a larger dataset became available. Luckily for the community, Corey Sznajder undertook a massive tracking project encompassing the entire 2013-14 season, and released the data to the public. Using this, there were more advances, including Garik16’s work on team zone performance and the repeatability of player performance in each individual zone.

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Introducing Weighted Points Above Replacement – Part 2

EvolvingWild NHL League-Wide Analysis, Player Evaluation, Theory Leave a comment

In part 1, I laid out the basis for Weighted Points Above Average (wPAA). Now it’s time to change the baseline from average to replacement level. A lot has been written about replacement level, but I’ll try to summarize: replacement level is the performance we would expect to see from a player a team could easily sign or call up to “replace” or fill a vacancy. In theory it is the lowest tier NHL player.

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Introducing Weighted Points Above Replacement – Part 1

EvolvingWild NHL League-Wide Analysis, Player Evaluation, Theory Leave a comment

Aggregate statistics in sports have always fascinated me. I might go so far as to say my need to better understand how these metrics work is one of the reasons I became interested in sports statistics in the first place. I also feel the process of developing them raises an incredible number of important questions, especially with a sport like hockey. Rarely are these questions raised in a more succinct and blunt manner than when a new aggregate stat first emerges and people see how good Oscar Klefbom is.

These questions mainly focus on how to value, weight, and interpret the various metrics that are available. For instance, should we value primary points per 60 more than relative Corsi for/against? How much more? Is there a difference? What’s the difference? Should we use some sort of feeling or intuition to determine which stats we like best? How do we address the issue of different metrics being used in conjunction to evaluate players? There have been multiple attempts to “answer” these questions (and many others) in hockey – Tom Awad’s Goal Versus Threshold (GVT), Michael Schuckers and Jim Curro’s Total Hockey Rating (THoR), Hockey Reference’s Point Shares, War-On-Ice’s (A.C. Thomas and Sam Ventura) WAR/GAR model, Dom Galamini’s HERO Charts, Dom Luszczyszyn’s Game Score, and most recently Dawson Sprigings’ WAR/GAR model… (Emmanuel Perry is also in the process of constructing a WAR model that I’m very excited about).

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