Expected Goals Model with Pre-Shot Movement, Part 1: The Model

There are few questions in hockey analytics more fundamental than who played well. Consequently, a large portion of hockey analysis has been focused on how to best measure results. This work is some of the most well-known work in “fancy stats”; when evaluating players and teams, many people who used to look at goals scored moved to focusing on Corsi and then expected goals (xG).

The concept of an xG model is simple: look at the results of past shots to predict whether or not a particular shot will become a goal. Then credit the player who took the shot with that “expected” likelihood of scoring on that shot, regardless of whether or not it went in. Several such models have been developed, including by Emmanuel Perry, Evolving Wild, Moneypuck, and many others.

However, there remains additional room for improving these models. They do impressive work based on the available play-by-play (pbp) data, but that only captures so much. There are big gaps in information, and we know that filling them would make us better at predicting goals.

Perhaps the biggest gap is pre-shot movement. We know that passes before a shot affect the quality of the scoring chance, but the pbp data does not include them. Thankfully, Corey Sznajder’s data does. While it does not cover every single shot over multiple seasons, it is a substantial dataset; when I pulled the data for this model, it had roughly half of the 2016-2017 and 2017-2018 seasons included: 72 thousand shots from 1,085 games. While the number of games tracked varies by team, we have at least 43 for every team except Vegas, for which we have 26. We can use this data to build the first public xG model that incorporates passes.

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Exit Types Don’t Affect Entry Quality (Much)

Last time, we saw that a team exiting its defensive zone with possession is much more likely to enter their offensive zone. Do the advantages end there, or do possession exits also improve the quality of zone entrances? Perhaps leaving the defensive zone with possession makes it easier to keep possession as they enter the offensive zone, and that leads to more shots per entry. Maybe pass-outs create space for more passes in the offensive zone, which improves shot quality.

It turns out that there is not much of a difference in entry quality by exit type; exiting with possession makes it more likely to gain the offensive zone, but the advantages quickly dissipate. That said, there are some interesting variations in how those zone entries play out. The differences are small enough that they could be random chance, but it’s worth taking stock of what we know with the data we have.

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Why Possession is the Key to Zone Exits

If there’s anything you know from neutral zone analytics, it’s probably this: carry-in zone entries are better than dump-ins. In the linked piece, Eric Tulsky finds that “maintaining possession of the puck at the blue line (carrying or passing the puck across the line) means a team will generate more than twice as much offense as playing dump and chase”.

But what about zone exits? Is possession equally important there? Work by Jen Lute Costello suggests that it is, but her data was limited to one playoff series. Today, I’ll expand on her work to show that maintaining possession is crucial for successful zone exits, and breakouts should be structured with this in mind.

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Introduction to the Transition Project

This is one of my favorite plays:

Almost every team is coached to make their opponent fight for every inch. Skjei’s end-to-end rush cuts through those defenses and leaves his team in a much better position than when he started.

But just how much better off did he leave them? How does that compare to alternative outcomes? And which players are the best at making these plays? We have unanswered questions about transitional play. We’d like to study them in more detail, but the gif above doesn’t appear anywhere in the league’s play-by-play data to help conduct analysis.

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Evaluating Nordic Drafting – A Potential Market Inefficiency

Over the last decade, teams have taken significant steps to improve their NHL entry draft approach. To do this, a number of teams have bolstered their analytics staff to identify the current “gaps” in prospect scouting. Whether it’s the Detroit Red Wings being the first team to dive head first into drafting Russian players, and then later Swedish players, or the Tampa Bay Lightning prioritizing small, skilled forwards, teams are looking for any available edge. More recently, the Pittsburgh Penguins have put a premium on overage players, as Namita Nandakumar found that overage players make the NHL faster. What’s the next big market inefficiency?

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When Can You Trust Your Intuition: The problem with having played the game

A common retort that many in the hockey analytics community have gotten is: “have you ever played the game?”

The insinuation, of course, is that if you haven’t played hockey at a high level, let alone in the NHL, then you can’t possibly understand the game. Certainly not as well as those who have. And that when it comes to evaluating players or making decisions on how best to improve a hockey team, the former players and lifelong hockey men that populate the league’s front offices can always fall back on their instinct for the game in ways that no one else can.

But let’s talk about relying on your gut instincts to make decisions.

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Projecting NHL Skater Contracts for the 2019 Offseason

We recently released the final version of our contract projections for the 2019 NHL free agent class (they can be found here). Our initial projections went up in mid-April, and even though it’s only been a few weeks, we’ve had numerous questions about how the model was designed, how it works, what it means, etc. I thought we might be able to answer all the questions about it on twitter, but alas it was just a dream. A quick recap: this is our third year doing contract projections for the NHL offseason. While the model/projections this year may seem quite complicated, our first version was very simple: a few catch-all stats and a linear regression model to predict salary cap percentage (cap hit / salary cap). We use cap percentage to keep salaries on the same level as the salary cap changes. Over the last few years, we’ve developed a few new methods, and this year we took quite a bit of inspiration from the method Matt Cane used for his 2018 NHL offseason salary projections.

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It’s Time To Stop Talking About Analytics

Look, nobody knows what analytics actually is anyway, so why are we still talking about it? At its most basic, analytics is simply a tool. Much like a hammer is a tool.

Maybe too much like a hammer. As the old saying goes, when all you have is a hammer, everything looks like a nail. The same may be true for analytics. At least in some contexts. Yes, analytics is simply a way to draw meaning out of data, but just because you finally figured out how to apply gradient boosting to your ridge regression model doesn’t mean you should.

Once you think of analytics as a tool, a means to an end, then it’s much easier to see that it’s not just a tool, but an entire toolbox. And when you reach into that toolbox, the tool you take out should depend on what you want to accomplish.

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Statement from Hockey-Graphs about Jason Baik

On Wednesday Night, Hockey-Graphs became aware that one of our contributors, Jason “jsonbaik” Baik, had been convicted of Sexual Assault in Allegheny County, Pennsylvania (Pittsburgh). To be utterly clear, Hockey-Graphs condemns these actions absolutely. Upon becoming aware of this horrible news, we have terminated our relationship with Mr. Baik and all contributions from Mr. Baik have been removed from this site.

We here at Hockey-Graphs wish to express our support for those who have been victims of Sexual Assault, Rape, or related crimes. As such, we encourage our readers to support organizations dedicated to help support victims of such heinous acts. If you can, please consider a donation to National Organizations like the Rape, Abuse & Incest National Network (RAINN) or local organizations such as the Pittsburgh Action Against Rape (PAAR) and the Women’s Center and Shelter of Greater Pittsburgh.

Sincerely,

Hockey-Graphs Editorial.

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The 1st Annual Hockey-Graphs NHL Awards: 2018-2019

It’s that time of year! The ’18-19 NHL regular season ended on Saturday, and that means the time to argue about the NHL player awards has begun. Now of course, the actual awards are voted on by PHWA members, General Managers, and the NHL Broadcasters’ Association for each respective award. However, we (Josh and Luke) decided it would be interesting to see which players the HG writers (and fellow hockey statistics minds) would choose to win the various end-of-season awards. The group of voters is made-up of as many Hockey Graphs writers as we could pester into completing the annoyingly buggy google survey, along with various other writers and hockey people who are in some way associated with the hockey statistics community. Continue reading