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 That’s a pretty robust dataset, but it’s unfortunately a bit out of date.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
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.Continue reading
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.Continue reading
This is one of my favorite plays:
Almost every team is coached to make their opponent fight for every inch. Skjei’s end-to-end rush cuts through those defenses and leaves his team in a much better position than when he started.
But just how much better off did he leave them? How does that compare to alternative outcomes? And which players are the best at making these plays? We have unanswered questions about transitional play. We’d like to study them in more detail, but the gif above doesn’t appear anywhere in the league’s play-by-play data to help conduct analysis.Continue reading
Analytics, so hot right now. But how do you get started? People from all sorts of background and levels of expertise have contributed valuable work to hockey analytics, but the journey can feel daunting.
In this post, I want to lay out my personal advice for what knowledge and skills are needed and how to get them. Your mileage will vary, but I think much of this will be useful to anyone who is interested in starting to do their own analytics research or writing.
My office was recently planning an offsite social event. During a team meeting, we brainstormed what activity to do together. Along with ideas like mini golf, hiking, and wine tasting, someone suggested karaoke. The team initially responded positively, so when everyone turned to me, I said “sure, that sounds fun”. Then someone put the options in a Google Form for us to all vote on privately. I opened it at my desk and immediately voted for karaoke dead last. I didn’t want to be a downer in public, but there was no way I was doing karaoke.
Being in public changes our behavior. It’s a natural trait and totally understandable. What’s interesting is understanding when and how it changes, and the NHL awards voting may have given us an opportunity to do just that. For the 2017-2018 season, the Professional Hockey Writers Association (PHWA) made their individual voter ballots public for the first time, and it appears that this may have affected how some writers voted.
“They don’t ask how. They ask how many.”
“But seriously though… how?”
To state the obvious: goal-scoring is an essential skill for a hockey team. Players have made long careers by putting the puck in the net.
But how do players create goals? Skaters rely on all sorts of skills to score; some are fast, some have a huge shot, and some know how to be in the right place for an easy tap-in. But we don’t have a rigorous view of what those skills are, how they fit together, and which players rely on which ones.
In this piece, I take 100 of the top NHL goal-scorers and apply unsupervised learning techniques to group them into specific goal scoring types. The result is a classification that buckets the scorers into 5 categories: bombers, rushers, chance makers, chaos makers, and physical forces. These can help players understand how to apply their skill set to goalscoring. It can also help teams make sure that their system is putting their top players in a position to score.