Recently, I showed how passing data is a better predictor of future player scoring than existing public metrics. In this piece, I’m going to show that by accounting for shot quality via passing metrics we can more accurately predict a team and player’s on-ice goal-scoring rates. I’m going to do this by quantifying the pre-shot movement that occurs when a player is on the ice. Finally, I’ll spend some time discussing certain forwards/teams that caught my eye. All data is from 5v5 situations and special thanks to Dr. McCurdy for pulling the on-ice player data for me. All non-passing project data is from Corsica.
Last time, I showed how passing data is a better predictor of future player scoring than existing public metrics. In this piece, I’m going to spend some time talking about how we can more reliably evaluate offensive and defensive contributions from defensemen, which has been difficult due to a lack of data. Not only due to a lack of data, but from a lack of flexibility regarding the identity of the position. Traditionally thought of as existing to defend and “make a good first pass,” I feel this limits the scope of both how we evaluate the position and its responsibilities.
In order to better evaluate defensemen, we need to identify specific metrics that we can tie to future goals. In looking at entry assists (a pass occurring in the neutral or defensive zones that precedes a shot), both for and against, we can quantify how effective that defensemen is at generating offense in transition, as well as suppressing those chances. The importance of those things at the team level is something I’ve previously discussed (transition here and defensive work here with Matt Cane). Once we identify these metrics as having a strong impact on future scoring and goal-suppression, we naturally then reevaluate what the proper roles are for a defensemen, which in turn forces us to reevaluate how we evaluate them.
Personally, I’d like to see us think of them more as fullbacks or midfielders in soccer (this is part of a larger concept of redefining positions and responsibilities, which will be posted in the next month or so, I hope). There are still going to be various types of players based on their individual skill set and team tactics, but supporting play, overlapping on the attack, and distribution are all pillars of what teams should look for. Let’s get to it.
While I have spent a lot of time over the last several months digging into how we can quantify passages of play and inform better tactical decisions, it’s time to revisit how passing impacts scoring at the player level. We have only been using half of the picture in terms of individual shots and goals for player evaluation. Sure, we have primary and total points, but primary assists aren’t a very useful metric. The rate at which players create shot assists also appeared to have significantly more value than a player’s own shots in some analysis I did last year.
This piece will release individual passing data for the 2014 – 2015, 2015 – 2016, and 2016 – 2017 seasons, the latter of which tracked by Corey Sznajder, the former tracked by myself and many others. However, it is important to provide context and meaning to the numbers rather than simply inundate you with data.
At the 2nd Annual Hockey Analytics Conference at the Rochester Institute of Technology, Matt Cane and I presented on new ways of measuring and evaluating defensive play. We used the passing data from my project to take a look. I believe Matt will write up his half of the presentation, so this post will focus on my half of the presentation. You can access our slides and view our presentation here. I will go into greater detail in this post since I am without time constraints. All of the data we used can be accessed here. Everything in this post deals with 5v5 play.
The Passing Project headed by fellow Hockey Graphs contributor Ryan Stimson (@RK_Stimp) is one of the most exciting things happening in hockey analytics. The project consists of dozens of volunteers manually tracking the passes that lead to shot attempts in games across the NHL. Thus far, the project has compiled data for nearly five hundred games. Ryan laid the groundwork for analyzing the project’s data in his piece here. That pieces discusses primary shot contributions (PSCs), which is a counting stat comprised of shots and shot assists. Shots in this article is synonymous with corsi meaning all shots and not just shots on goal. Ryan has built on that original work with a piece on offensive zone play here and a piece on neutral zone play here. And following his line of thought, I wrote a piece for NHL Numbers that expanded on his approach to offensive zone analysis.
Last time, I showed how using data and video evidence can be combined to inform tactical offensive zone decisions. Today, I’m going to do the same thing in the neutral zone. Neutral zone play is something that has been a hot topic among analysts for many years, going back to this paper written by Eric Tulsky, Geoffrey Detweiler, Robert Spencer, and Corey Sznajder. Our own garik16 wrote a great piece covering neutral zone tracking. Jen Lute Costella’s work shows that scoring occurs sooner with a controlled entry than an uncontrolled entry.
However, for all the work that goes into zone entries, there have been few efforts to account for how predictable these metrics are. At the end of the day, what matters is how we can better predict future goal-scoring. Also, in looking at our passing data, what can we also learn about how actions are linked when entering the zone? Does simply getting into the offensive zone matter? Does it matter whether it’s controlled or not? Or, does what happen after you enter the zone matter exponentially more? Lastly, what decisions can we make to improve the team’s process using this data?
Shot quality has been a topic of late on hockey twitter and various sites. Only a few weeks ago, the Hockey-Graphs Hockey Talk was centered around this topic. Shot quality is a lightning rod and much of the talking at or past one another that people often do stems from a single issue: there is no agreed-upon definition of what people mean when they say “shot quality.” Well, I like what our own Nick Mercadante had to say on the subject:
— Nick Mercadante (@NMercad) January 17, 2016
Establishing a base, repeatable skill that accounts for pre-shot movement and an increased likelihood of a goal being scored are what we need to properly analyze player contributions. Quantifying passing also gives us another actionable piece of data that everyone understands and coaches can use as well. Often, the simplest metric or method is the best. And, we should able to do that now that we’ve obtained a significant set of data. This chart may look familiar, but it’s essential to understanding how important passing is to goal-scoring. This is from all tracked passing sequences from the six teams (Chicago Blackhawks, Florida Panthers, New Jersey Devils, New York Islanders, New York Rangers, and Washington Capitals) that we tracked last season.
From this point on, I want you to forget whatever it is you think of when you hear the term, “shot quality.”
This week, I wanted to illustrate several ways that passing data can be used to more accurately assess players, offer examples of advance scouting and opposition analysis, and identify how and where teams attack and defend. On Monday, I gave you the basics for passing data. Tuesday saw a deeper look at network and linkup data. Wednesday introduce lane Corsi concepts. Yesterday, I combined most of this to illustrate how it could be used to prepare for an opponent.
Today is much lighter. Today is about releasing our data to the community. Enjoy!
This week, I’ve introduced some of our latest and newest data and metrics. On Monday, I posted a refresher on what passing metrics were and why they were important. Tuesday had network and linkup data that really only scratches the surface on what we can do this season. Yesterday, I introduced lane Corsi concepts and how they might reveal a bit more information when examining how teams generate offense and defend against the opposition. Today’s post is rather ambitious in that I will take all the different ways of using this data, synthesize it into an advance analysis of the opposition, and then provide the data of the game in question to see how things matched up.
We’ve had a Toronto focus all week, so that will continue. The opposition will be the New Jersey Devils and we’ll focus on their clash from December 8th. This will be done under the hypothetical assumption Toronto’s opposition analysts have access to this type of information league-wide and how they could make use of it. All data is 5v5 unless otherwise stated. Leafs data is from 13 of their first 26 games in aggregate; Devils data is from their first 21 games.
So far this week I’ve introduced some of our newer metrics using our data on the Toronto Maple Leafs. We’ve looked at general shot contribution and on-ice data as well as network and linkup data. Today ,we’re going to look at something new that may help us understand more about how teams generate offense and where teams fail to defend the opposition.
Understanding where the offense originates while preparing for a specific opponent can provide great value. If I know which lane and zone a player is likely to linkup with another, perhaps I can scheme for such a situation. If a LW-LD combination is getting overrun down their side of the ice, yet the LD has decent left lane numbers apart from that LW and the LW’s terrible numbers persist irrespective of who is behind him, I know there’s either a communication breakdown between those players, or that the LW is more likely being propped up by the LD.
Digging deeper into how the game can be analyzed with new data is the first step in how we’re going to answer some of these questions. All data is 5v5 unless otherwise specified, and is through games completed as of 12/4. This represents 13 of the Leafs first 26 games this season.