While the 2022 Beijing Winter Olympics are still over a year away and the memories of Pyeongchang are still fresh in many fans’ minds (with only one World Championship taking place since then) centralisation for both Canada and the USA is rapidly approaching. Countries historically pick their rosters around late May, beginning of June in the year prior to the Olympics to allow time for players to train, bond and participate in exhibition games before the final roster selection occurring just a month before the big event. What goes on during those 9 months prior to skating out of that Olympic ice surface is largely kept a secret with roster decisions often being announced in a somewhat cut-throat manner and additional players often being drawn in from outside the bubble to the surprise of everyone. Throughout this article, we will be looking at the survival rates of skaters on National Teams over the past 30 years and investigating what this means for roster selection heading into Beijing.

In 2018 between the two teams there were only 3 first time players. Cayla Barnes and Sidney Morin both lined up for the USA on the big stage while Sarah Nurse did the same for Canada. That is of course not to say these players didn’t have prior international experience. Nurse made her national team debut at the 2015 4 Nations Cup and had also represented Canada at a U18 level. Cayla Barnes while just 18 at the time of centralisation had played for the United States 3 times at U18’s including Captaining them to a Gold medal that very year while Morin had previously represented the USA at the 2017 The Time Is Now Tour. While there were only 3 ‘true’ rookies between the two teams that was not to say this was the same line-up as the previous Olympic in Sochi with Team Canada having 8 players missing from their gold medal-winning Sochi side, and the USA missing 7.  I have put their names below as we will return to them later.

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.

Below are the presenters and slides from the 2019 Sports Analytics Conference held at the Rochester Institute of Technology on September 14th. All videos are time-stamped save for one which could not be recorded. Please reach out to Ryan Stimson if you have any difficulties accessing the slides and/or video. Enjoy!

Thanks to everyone who attended and/or presented!

Continue reading →

In the last two posts, we’ve looked at the big picture outputs of the xG model for players and teams. Now, let’s zoom in on the model itself to try and understand it better. How is it making its decisions? Which variables provide the most important information and in what way do those variables affect the outcome?

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.

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?

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.

Continue reading →