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?Continue reading
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.Continue reading
Visualizing passes isn’t easy in hockey. In any given KHL game, there are between 700 and 900 Passes. Somewhere between 65% to 85% are successful*. If you wanted to focus on just the successful ones, you’d have to find a way to meaningfully and concisely represent 500-700 events. Let’s start with something simpler: the Power play. If we further restrict our target to passes by single teams during 5v4 power plays in the OZ, we still get between 40 and 50 passes per game per team. Looking at two random KHL games, you can see that this is still quite a lot of passes:
There are some trends to be picked up on, but it’s not very clean. And any semi-serious opposition scouting (especially of special teams) will take into account multiple games, which then leads to an unidentifiable mess when plotted.Continue reading
First of all, congratulations if you got accepted. Kudos to you if you got accepted to a conference like RITSAC, a very well run and well curated conference. This is a wonderful accomplishment, and you should be proud. Tell your friends and family. Celebrate. Bask in the adoration.
Well, maybe not that last part. But you get my point. Your work clearly has some perceived value and is based on solid reasoning and data analysis.
So now what?Continue reading
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.
Something in hockey has been bugging me for years. Technically a lot of things about hockey bug me, but let’s not get sidetracked right off the bat. The irritating aspect of hockey I want to focus on today are neutral zone faceoff wins. They rarely ever lead to anything interesting.
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?Continue reading
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.Continue reading
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.Continue reading
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.Continue reading