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…

The 6th Winningest Coach in NHL History has Some Good Things to Say.

Suppose that 16 months ago, I told an analytically-inclined hockey fan that over the next 16 months, one man would offer more insight into proprietary NHL analytics than anybody else has over the past half-decade or so. Then, say I followed that up by asking them to guess who. They may guess Eric Tulsky or Sam Ventura, two early pioneers of hockey analytics who are respectively employed by the Carolina Hurricanes and Pittsburgh Penguins, and who earned job promotions in that time frame. …

Your last minute hockey fix before the season starts

I recently built a projection model for the 2021 NHL season. A high-level overview of this model can be found here, but the gist of it is that I used regression to determine how good each NHL player was at each thing they do and how much they would do each thing for their team, aggregated these values on the team-level to determine how well each team would do these things, and then simulated the season 10,000 times to determine the most likely outcomes for the season. …

What you need to know about the newest game simulation model from TopDownHockey

A Forecast for the 2021 San Jose Sharks. (Image by Author)

I simulated the 2021 NHL season 10,000 times in order to determine the probability of each outcome. I’ve began sharing the results of my work on Twitter, and I plan to write a full season preview soon, but before I do so it’s essential that I provide an overview of what I did, so that readers can analyze the process and determine what they believe to be the strengths and weaknesses of the model.

I began by using extreme gradient boosting to build an expected goal model that determines the probability of each shot becoming a goal. More about this…

Moving away from the black box…

As I discussed in my Wins Above Replacement (WAR) write-up, I’ve used regression to obtain point estimates of an NHL player’s individual impact on the following six components:

  • Even Strength Offense
  • Even Strength Defense
  • Power Play Offense
  • Penalty Kill Defense
  • On-Ice Penalty Differential
  • Individual Shooting

The regression isolates a player’s impact by accounting for various external factors that surround them. These factors differ depending on the component which I am evaluating. For even strength offense and defense, I account for the following components:

  • All teammates and opponents.
  • Whether a shift started on-the-fly as the result of an expired power play…

What you need to know about the newest WAR model from TopDownHockey

I’ve built a Wins Above Replacement (WAR) model that provides a point estimate of the value that a player has added in a given season. I strictly use this terminology whenever possible because this is only a point estimate gathered from the best of my imperfect ability, and because the amount of value that a player has added is not always the same as how good they are. (In some cases, the two may be vastly different.)

This article is meant to serve as a high-level overview of the meaning of the metrics, the process, and the results.

The model…

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