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

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

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

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

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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The Hockey-Graphs Podcast Episode 4: The Stimson System

Ryan Stimson joined Adam Stringham to discuss his article The Stimson System. Ryan’s piece uses analytical insights to develop a unique on-ice system. Ryan and Adam discuss: risk aversion, organizational buy-in, player selections, proof of concept and more! Any comments are appreciated, the goal is to produce a podcast that people want to hear. Please subscribe to the podcast on iTunes!

The Hockey-Graphs Podcast Episode 3: Expansion Draft and GM Rankings

Welcome back to The Hockey-Graphs Podcast! Our third episode showcases Chris Watkins and Carolyn Wilke‘s recent articles; The 2017 NHL GM Report Cards (Parts 1, 2 and 3. Given the leaks that came out prior to the expansion draft; what is Las Vegas’ strategy? Are good general managers always good or do they excel in some areas and struggle in others? We discuss those questions and more in this episode of The Hockey-Graphs Podcast. Any comments are appreciated, the goal is to produce a podcast that people want to hear. Please subscribe to the podcast on iTunes!

Who You Calling Weak? Draft Class Variance

This year’s NHL draft class is weak. I don’t follow junior prospects closely, but that’s what I’ve heard from more knowledgeable sources. It’s a fair claim; Nolan Patrick and Nico Hischier seem talented but not among the game-changing talents that have recently been drafted first overall.

However, it’s harder to judge the draft class past the very top. Scouting is hard, especially for hundreds of prospects across the world. It’s possible that while there is no clear star in the draft class, the rest of the draft is as strong as ever.

That would have big implications for draft strategy. The conventional wisdom is that teams may trade more picks this year because they believe the weak draft class makes the picks less valuable. But if the draft is typical after the first few picks, that would be a poor use of assets.

We don’t yet know how well this year’s draft class will do in the NHL. But, we can use historical data to ask questions that establish expectations: how well does each draft class typically perform, and how much does this vary by year?

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The Hockey-Graphs Podcast Episode 2: Risk Aversion and the Draft

Welcome back to The Hockey-Graphs Podcast! Our second episode showcases Namita Nandakumar‘s recent articles; Exploiting Variance in the NHL Draft and Who’s Getting Drafted This Year?. Namita and Adam discuss: draft theory, risk aversion, player potential, central scouting rankings and how to best apply her research moving forward. Any comments are appreciated, the goal is to produce a podcast that people want to hear. Please subscribe to the podcast on iTunes!

FQG: Cumulative Hits in the Conference Final

May-15-2017 21-24-14 phaneuf hits guentzal

Throughout the playoffs (quarterfinals, semifinals), I have analyzed whether a team’s hits for and against were indicative of their success. Studying a team’s Corsi for percentage per game and expected goals for per game alongside their cumulative hits can help us spot high-level trends.

We’re seeking to determine the accuracy of the narrative that many hockey traditionalists love – that a team must increase their hitting to succeed in their quest for the Stanley Cup. This has been studied in recent seasons, including 2014-15 season, 2015 playoffs, and 2016 playoffs, yet no decisive correlation was found between a team’s increased hitting and success. So far in the first two rounds of the playoffs, this seems to hold true.

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