Why I’m Not Fooled by the Blueliner’s First Four Games. — The San Jose Sharks have started off the season 4–0–0. They’re one of only six teams who remain undefeated, and one of only three teams who’ve won every single game in regulation thus far. For a team that earned a regulation win in less than 30% of the games they…

Put down the remote control and pick up a spreadsheet! — With the 2021 NHL Entry Draft now officially in the books, it’s time for me to complete my final chapter of content from this draft and use the results of my NHL Equivalency and Prospect Projection Models to analyze how well everybody drafted. If you’re unfamiliar with these models, I…

How Powerful is This Draft Class? — 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…

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…

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…

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…

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…