Lateral Puck Movement in the NZ

Research shows that lateral/”east-west” puck movement in the offensive zone is beneficial to increasing one’s odds of scoring. But I have now heard from people in various positions within the hockey industry on why it might also be useful to generate east-west puck movement in the neutral zone. The theories – focused on lateral passing, lane changes and stretch passes, respectively – all boiled down to one point: When you rush the puck up ice, the defending team will focus on that side, leaving the other side of the ice somewhat more open, so there might be open ice to exploit.

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Passing clusters: A Framework to Evaluate a Team’s Breakout

Quick breakouts – trying to move the puck out of your zone right after gaining possession – make up roughly 38% of possessions and account for 22% of all shots and 22.4% of Expected Goals (at least according to my possession and xG definitions). Therefore, understanding what does and does not work when breaking out the puck against present forecheckers is important. There is evidence that passes from the defensive half boards by wingers inside produce more offense than those straight up ice. But the puck is more often recovered elsewhere, so these passes by wingers aren’t the first pass in a possession and are therefore presumably influenced by the previous play. It should be interesting to find out how the inclusion of the pass(es) that came before affects this conclusion.

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Exploratory Data Analysis Using Tidyverse

This post assumes beginner knowledge of R.

Welcome to the second article in our series on basic data cleaning and data manipulation! In this article, we’re going to use play-by-play data from two NHL games and answer two questions:

  • which power play unit generated the best shot rate in each game?
  • which defenseman played the most 5v5 minutes in each game?

In the process of doing so, we’ll cover several topics of basic data manipulation in the tidyverse, including using functions, creating joins, grouping and summarizing data, and working with string data.

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Combining Manually-Tracked Data with Play-by-Play Data

This post assumes beginner knowledge of R.

If you’ve ever analyzed hockey data, then you’re probably familiar with the NHL’s Real Time Scoring System, which produces what’s more commonly known as play-by-play data. These data are publicly available and allow us to see every event recorded by the NHL in a given game. Shown below are selected details about the first 10 events from two games on February 18, 2019: Tampa Bay at Columbus and Vegas at Colorado.

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The Importance of Pressure for a Successful Forecheck

Most of my posts so far have talked about zone exits from the perspective of the team trying to breakout out of their defensive zone. Now, let’s flip the script and discuss the team on the forecheck. This team does not have possession of the puck, but they are in their offensive zone, which is an advantage. So, how can they regain control?

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Team Level Zone Exits

From past posts, we have a general sense of the basics of zone exits: zone exits are important because they get you out of your zone and towards an opportunity to score. The key to a successful zone exit is maintaining possession, ideally by avoiding the temptation to dump the puck out.

But so far, we have only looked at zone exits league wide. Most fans care about one particular team more than the rest, but we haven’t looked at team-level results at all. So today, let’s see how each team has performed at zone exits over the past three seasons.

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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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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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