How Powerful is This Draft Class?

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The overwhelming consensus regarding the 2021 NHL Entry Draft Class is that a pick in this year’s draft is not as attractive as the same pick in last year’s draft would have been or the same pick in next year’s draft will be.

What isn’t so clear is whether this consensus exists because the draft class itself is weak, or simply because scouts have seen less of this year’s prospects due to the pandemic.

According to Dylan Griffing, a Hockey Scout at Elite Prospects who covers Russian players in particular, the consensus exists mostly due to the former:

The 2021…


Bringing Together the Best of Both Worlds

The second step of this project was not unlike the first in that it took heavy inspiration from the established work of one of hockey’s top public data scientists: Byron Bader. Bader runs Hockey Prospecting, an NHL-Level Analytics Tool which he describes below in his own words:

Hockey Prospecting standardizes player scoring across the board and uses historical performances to chart how prospects will perform in the NHL.

You’re probably familiar with Bader and/or Hockey Prospecting by name, but even if you aren’t, you’ve either been living under a rock or you’ve seen one of his trademark prospect comparison cards:

Image from Byron Bader


A Few Minor Updates on an Established Framework

In Part 1 of NHL Equivalency and Prospect Projection Models, I made a few references to scoring outside the NHL. This would be an easy thing to measure if all prospects played in the same league. It would even be fine if they all played in a small handful of leagues that were similar enough to compare players in each of them, like the 3 leagues which make up the CHL. …


Making Use of the Best Information Available

At the 2013 NHL Entry Draft, the San Jose Sharks traded the 20th and 58th overall selections in said draft to the Detroit Red Wings in exchange for the 18th. San Jose would go on to select Mirco Mueller with the 18th, while Detroit would select Anthony Mantha with the 20th and Tyler Bertuzzi with the 58th.

I don’t have psychic vision, so I had no idea that Mantha and Bertuzzi would both become very good NHL players, or even that both would be available with the picks San Jose surrendered. But as a Sharks fan, I still hated this…


A few minor tweaks. a lot more data, and clarity into how much it’s really worth.

Just over six months ago, I released two descriptive models for evaluating NHL skaters and goaltenders: Expected Goals and Wins Above Replacement. For anybody who’s either unfamiliar or wants their memory refreshed, here’s a quick run-down on each model:

  • Expected Goals leverages extreme gradient boosting, an advanced machine learning technique, to calculate the probability that an unblocked shot attempt will become a goal based on factors like shot distance, angle, and the event which occurred prior to the shot. Expected goals can be interpreted as weighted shots.
  • Wins Above Replacement (WAR) uses ridge regression (RAPM) to isolate the impact skaters…


Let’s hold him to the same standard as John Chayka…

The Toronto Maple Leafs have just been eliminated from the Stanley Cup Playoffs by the Montreal Canadiens. It marks the 3rd straight year since inheriting a 105-point Maple Leaf team that General Manager (GM) Kyle Dubas has seen them fail to win a single playoff series. It’s the second straight in which he’s seen them eliminated by a team that finished the regular season with a negative goal differential.

These numbers alone paint an unflattering picture of how good Dubas’s Leafs have actually been. They generally controlled the flow of play in the series they lost to Columbus and Montreal…


Your fast track to a base-level understanding of Python.

You will build this chart as part of the tutorial.

Table of Contents

What is Python?

Python is an open-source programming language that can be used for a wide variety of applications such as data analysis, data science, and data visualization, software and web development, and writing scripts for systems. …


A fun case study on somewhere I went wrong

Martin Jones and Devan Dubnyk celebrating a rare win.

If you follow me on Twitter, you probably know how I feel about goaltending: It’s basically random. A quick search finds 3 instances of me Tweeting the exact string “Goalies are basically random” and you can probably another 20 or so where I’ve shared that sentiment. This isn’t a belief I’ve always held as a hockey fan; I once freely used terms like “elite goalie,” and believed you could project future goaltending performance with a solid degree of confidence. But the overwhelming body of evidence I’ve seen has led me to change my position.

To give a quick example of…


A glimpse into what proprietary tracking data can tell us.

Note: This article is my submission for the 2021 Big Data Cup. Before I go any further, I’d like to thank Stathletes for hosting the event and giving me the opportunity to work with their data set!

From October through January, I worked extensively with the NHL’s play-by-play (PBP) data and built various analytical models. …


Putting a number on the vapid “limitations” I tell people to consider.

Six days ago, I released What to Make of Paul Maurice’s Crusade Against Public Hockey Analytics?, an article in which I scrutinized various comments Winnipeg’s bench boss made about hockey analytics. I came to the conclusion that outside of a bad faith deflection he made in a defense of his captain, he was mostly right about everything he said, and he also offered some valuable insight into proprietary analytics and solid advice for the public crowd.

If you haven’t already read the article, I recommend you do. Not only is it my best work so far, but it’ll give you…

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