Revisiting NWHL Game Score

In March 2018, Shawn Ferris of Hockey Graphs introduced his NWHL Game Score, which was based on Dom Luszczyszyn’s NHL Game Score. It was groundbreaking work in women’s hockey analytics, which is still very much in its infancy — especially at the professional level.

Game score is a valuable tool that can give us a better understanding of a player’s performance than points for skaters or save percentage and goals against average for goaltenders. It provides us with a single value that incorporates relevant points of data which we can use to compare the performances of two or more players in a single game or over the course of many games, including seasons and careers.

As Shawn noted in his work, game score is particularly valuable for analyzing performance in the NWHL because of the brevity of the regular season. Through the league’s first four seasons, the average length of a season was under 18 games. The 2019-20 season promises a schedule of 24 games, which is still less than a third of the length of the NHL season. That brief schedule creates an opportunity for shooting percentage factors to influence both a players’ production and our perception of their performance.

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Expected Goals Model with Pre-Shot Movement, Part 3: 2018-2019 Data

Yesterday we looked at the team and skater results from the 2016 – 2018 data that was used to train the xG model. That’s a pretty robust dataset, but it’s unfortunately a bit out of date. People care about this season, and past years are old news. So let’s take a look at the data that Corey Sznajder has tracked for 2018 – 2019 so far.

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Expected Goals Model with Pre-Shot Movement, Part 2: Historic Team and Player Results

Intro

In the last post, we introduced a new expected goals (xG) model. It incorporates pre-shot movement, which made it more accurate than existing public xG models when predicting which shots would be goals. However, we use xG models for far more than looking at individual shots. By aggregating expected goals at the player and team level, we can get a better sense of how each of them performs.

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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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