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:

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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. …

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…

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…

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…

Table of Contents

• What is Python?
• Requirements
• Install Python
• Install and Upgrade Pip
• Install the TopDownHockey Scraper
• Install Jupyter Lab
• Open Jupyter Lab
• Start the Tutorial
• Import our Packages
• Scrape the Data
• Clean the Data
• Make the Data Look Good
• Prepare the Data for Analysis
• Analyze the Data
• Visualize the Data
• Conducting Deeper Analysis

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. …

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…

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. …