*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. …

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…

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. …

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. …

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…

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…**

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…

Alan Ryder broke ground in 2004 when he published hockey’s first expected goal model titled “Shot Quality,” but it wasn’t until Dawson Sprigings and Asmae Toumi published their expected goal model in October of 2015 that expected goals ascended into hockey popularity. Today, expected goals have usurped Corsi (shot attempts) as the go-to underlying metric for analyzing teams and skaters, and NHL arenas have even featured expected goal data on the jumbotron at intermissions. …

If you’ve ever watched a hockey game, you’ve got an expected goal model built into your brain. Every shot you see, you calculate an expected goal value for. Unlike mathematically computed models, your model’s output doesn’t number between one and zero; it’s varying degrees of excitement when your team shoots and terror when their opponent shoots. You don’t calculate a precise goal probability each time a shot is taken, but you could give a solid estimate if you had to.

Your brain’s model is simultaneously tested and trained every time you watch a game. When a puck is shot, you…

After writing over 7,000 total words for parts one and two of this article, I’m excited to finally share the results. If you’ve been reading along so far, I’m sure you are too, and I’d like to thank you for taking the time to read my work. I hope you’ve enjoyed it.

If you haven’t been reading along, that’s okay. I do highly recommend that you read part one and part two before you read this; they aren’t short, but they will give you a good idea of why this is all important to me and why you should care…

Data Scientist