# The State of Goalie Pulling in the NHL

When people ask me how to get into sports analytics, I always suggest starting with a question that they’re interested in exploring and using that question as a framework for learning the domain knowledge and the technical skills they need. I feel comfortable giving this advice because it’s exactly how I got into hockey analytics: I was curious about goalie pulling, and I couldn’t find enough data to satisfy my curiosity. There are plenty of articles on when teams should pull their goalies, but aside from a 2015 article on FiveThirtyEight by Michael Lopez and Noah Davis, I couldn’t find much data on when NHL teams were actually pulling their goalies and if game trends were catching up to the mathematical recommendations. I presented some data on the topic at the Seattle Hockey Analytics Conference in March 2019, but the following analysis is broader and includes more seasons of data.

Data collection notes

• All raw play-by-play data is courtesy of Evolving-Hockey and their scraper.
• Data includes all regular season games from 2013-14 onward. All 2019-20 data is up until the season pause, through March 11, 2020.
• Only the first goalie pull per team in each game is counted for the average times. For example, if a team pulled their goalie while trailing by two and then later in the game pulled their goalie again while trailing by one, only the first instance is included in the average times. All extra attacker time is counted for the scoring rates.
• More details on this data set, particularly at the team level, is available here.

# An Introduction to R With Hockey Data

I have written a couple articles over the past few months on using R with hockey data (see here and here), but both of those articles were focused on intermediate techniques and presumed beginner knowledge of R. In contrast, this article is for the complete beginner. We’ll go through the steps of downloading and setting up R and then, with the use of a sample hockey data set, learn the very basics of R for exploring and visualizing data.

One of the wonderful things about using R is that it’s a flexible, growing language, meaning that there are often many different ways to get to the same, correct result. The examples below are meant to be a gentle introduction to different parts of R, but please know that this really only scratches the surface of what’s available.

The code used for this tutorial (which also includes more detail and more examples) is available on our Github here.

# 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.