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
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 That’s a pretty robust dataset, but it’s unfortunately a bit out of date.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
“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.
Throughout the playoffs (quarterfinals, semifinals), I have analyzed whether a team’s hits for and against were indicative of their success. Studying a team’s Corsi for percentage per game and expected goals for per game alongside their cumulative hits can help us spot high-level trends.
We’re seeking to determine the accuracy of the narrative that many hockey traditionalists love – that a team must increase their hitting to succeed in their quest for the Stanley Cup. This has been studied in recent seasons, including 2014-15 season, 2015 playoffs, and 2016 playoffs, yet no decisive correlation was found between a team’s increased hitting and success. So far in the first two rounds of the playoffs, this seems to hold true.
After the conclusion of the 2017 Stanley Cup Quarterfinals, I looked at whether a team’s hits for and against were indicative on their play. By looking at a team’s Corsi for percentage per game and expected goals for per game, against their cumulative hits as their first round progressed, it could be observed whether a team’s production dropped due to being outhit.
As it was explained in the first part of this series, many hockey traditionalists point to an increased number of hits as a necessity to compete for the Stanley Cup. There is a preconceived notion by some hockey minds that a team will become worn out if they are consistently outhit in the playoffs and subsequently will not be able maintain their production. However, in the 2014-15 season, 2015 playoffs, and 2016 playoffs, no decisive correlation was found between success and hits.
On Monday, I introduced some work on quantifying and identifying team playing styles, which built upon my earlier work on identifying individual playing styles. Today we’re going to discuss how to make this data actionable.
What are the quantifiable traits of successful teams? What plays are they executing that makes them successful? How can we use data to then build a style of play that is more successful than what we’re currently doing? The way we bridge the gap between front office and behind the bench is by providing data to improve their matchup preparation, lineup optimization, and enhance tactical decisions.
This is what I mean by actionable: applying data-driven analysis and decision-making inside the coach’s room and on the ice. All data is from 5v5 situations and is either from the Passing Project or from Corsica.
As the Stanley Cup Playoffs progress, the intensity rises. This often leads to more physical play, thus an increase of hits. Hockey traditionalists, including players and coaches, have often pointed to increased hits as a part of playoff hockey. Some teams have altered their strategy to embody a more physical style, simply because it is the playoffs.
The impact of hitting has been explored before during the 2014-15 season, the 2015 playoffs (both by Garret Hohl), and the 2016 playoffs (by @yolo_pinyato). However, none found a decisive correlating success to hits.
One of the aspects of player performance that is discussed ad nauseam is chemistry. How well do certain players elevate their performance with one player or another due to some inherent ability to find the other on the ice? To know what a teammate is going to do? However, very little has been done to analyze this phenomenon. In this piece, I posit that by identifying playing styles, something that’s been done in the NBA, we can quantify how well certain players will complement one another.
All data is from 5v5 situations from the 2015 – 2016 and current season, totaling almost 900 games from the Passing Project volunteers and Corey Sznajder. Special thanks to Asmae for her guidance throughout this piece.
I want to stress that this is a first foray into this type of analysis and simply because a player has a different style than what I’ve named (which are relatively arbitrary) it doesn’t mean they are necessarily better than another player. Players may have similar styles, but some will simply be more effective due to their ability. Finally, given that each day we accumulate more data, a player with a smaller sample size could find themselves in a different cluster in future analysis.