Month: May 2020

Quantifying the Value of an NHL Timeout using Survival Analysis: Part 1

Ian Ferer Data Analysis Tags: , Leave a comment

I’d like to thank Luke Benz, my mentor via the Hockey Graphs Mentorship Program, for all of his help in developing this project.

Introduction

Hockey, by nature, is a fast-paced sport that can be difficult to represent by discrete situations. While most other professional sports can be viewed as combinations of distinct in-game events – at-bats in baseball, plays and series in football, and even possessions in basketball – hockey is extremely fluid, with a constantly changing game state. This difference in game flow means that there are far fewer opportunities for a hockey coach to make any decisions based on distinct game states. While, for example, a football coach has several opportunities per game to decide whether or not to attempt a fourth-down conversion, a hockey coach has very few chances to make any comparable choice that can affect the outcome of the game. However, there are a few tools available to a hockey coach that can be researched so as to optimize their effectiveness in helping a team to win a game.

The most-researched of these decisions (thus far) for an NHL coach is when to pull the goalie in an endgame situation. There have been several papers published regarding the optimal time to pull the goalie, such as these two by Beaudoin and Swartz in 2010 and by Brown and Asness in 2018. (For even more great work on goalie pull times, you can check out Meghan Hall’s talk from the 2019 Seattle Hockey Analytics Conference and her Tableau dashboard, as well as the Goalie Pull Twitter Bot created by Rob Vollman and MoneyPuck.com.) All of this prior research has found that NHL teams should pull their goalies much sooner than conventional wisdom suggests, as teams are much more likely to score to tie the game if they pull their goalie earlier rather than later.

However, beyond pulling the goalie, there are still a few more tools at a coach’s disposal. Teams are allowed to challenge goals for certain rule infractions, use a 30-second timeout during a stoppage in play, or switch goalies if the starter is having a bad game, in addition to personnel decisions regarding line combinations or matching up players against the other team. This article focuses on timeout usage, but I plan to explore the other tools in future work.

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The State of Goalie Pulling in the NHL

Meghan Hall Uncategorized 5 Comments

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.
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Introducing NWHLe and Translation Factors

Mike Murphy NWHL Tags: , , Leave a comment

In April 2017, Rob Vollman tweeted out what he called “rough and preliminary” translation factors for women’s hockey. At the time, I was playing around with counting stats from two years of NWHL and CWHL hockey, and wanted to develop as many tools and resources as I could to better understand the women’s game. Curious to know what the competitive landscape of post-collegiate hockey looked like in North America and elsewhere, I began to keep track of data with the intention of building on Rob’s translation factors.

The world of women’s hockey in North America has changed dramatically in the three years since Rob’s tweet. My initial plans went up in smoke when the CWHL suddenly folded after the 2018-19 season. As a result, I shifted my focus to developing NWHL equivalency factors – or NWHLe – for NCAA DI, NCAA DIII, and USports. Unfortunately, it quickly became apparent that the sample size of USports alumnae to play a significant number of games in the NWHL was too small to work with.

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Using Sequences for Analysis: Expected Goals Contribution and more

Thibaud Chatel Data Analysis, Decision Science Tags: , , 2 Comments

In a previous article, I presented a way to cut and slice a hockey game into Sequences. A Sequence extends from the moment a team gets control of the puck and starts moving forward, to the moment the team loses it for good. The objective was to measure the importance of every event happening between the beginning of a Sequence and its end, from a zone exit to any shot attempts, to a zone entry or any high-danger passes in between. If a Sequence includes one or several shot attempts, its value is the sum of the Expected Goals of all those attempts.

The natural follow-up was the creation of an Expected Goals Contribution metric for players.

The thinking behind it was to answer one of the two main questions we face in the daily use of analytics with coaches: What is the real contribution of each player? Overall, there are the well-known GAR or WAR type of metrics, but these are beyond the comprehension of many staffs as they are not tangible enough for a daily use.

Now, if we use Sequences where the team has possession of the puck, it means Expected Goals Contribution would only look at the offensive side of the game. Still, instead of looking separately at transition or shooting stats to evaluate a player, the objective is to sum all offensive efforts into one metric, weighting those efforts (zone exit, entry, etc.) according to their contribution to the Sequence. It also makes playmaking more apparent statistically.

In other words, it means sharing the total value of the Sequence (in terms of Expected Goals), between the players responsible. This is what we called Expected Goals Contribution.

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