Tactalytics: Using Data to Inform Tactical Offensive Zone Decisions

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Much of the gains made in the field of hockey analytics has to do with player evaluation and roster construction. Identifying and quantifying a player’s on-ice shot differential while accounting for the context (score state, quality of teammates, quality of competition, deployment, etc.) is something the community has largely been successful at doing. When teams sign or trade for a player, we’re at a good enough place to determine if that was a positive or negative signing, for the most part. There have even been improvements in scouting and drafting that are analytical in nature.

However, we still are lacking in areas of quantifying a team’s system and how they play. We have made strides concerning two important phases of the game, namely the work done here on zone entries by Eric Tulsky, Geoffrey Detweiler, Robert Spencer, and Corey Sznajder, and also work done here by Jen Lute Costella on zone exits. These two pieces, among others written on these subjects, demonstrate a data-driven approach that can influence the tactical decisions a team can make on the ice. However, these are isolated incidents at the blue lines and structured play in the offensive zone remains difficult to quantify.

I attended the NHL Coaching Clinic held in Buffalo, NY the day before this year’s draft. During a presentation from Davis Payne, an assistant coach with the Los Angeles Kings, I noticed two distinct plans of attack being demonstrated and wanted to quantify them as best I could with our passing data. The decision on how to set up and attack in the offensive zone is largely determined by the coach. They will establish a structure within which their players have some latitude to create offense. Rarely do we see this aspect of the game quantified as it’s incredibly fluid and difficult to pin down. However, today we’re going to do just that.

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Introducing Player Radar Charts

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For the soccer fans on hockey twitter, you’ve likely come across Ted Knutson. Several years ago, Ted introduced radar charts for player evaluations across the five major soccer leagues. At the time, I was busy tracking passes and other things on the New Jersey Devils, but always wanted to have something like that for hockey. So, I finally got around to doing it. Links to the Forward and Defense charts are at the bottom, so skip down if you just want to access those.

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A New Passing Project Data Visualization

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Back when the season started, I started playing around with the idea of how best to visualize our passing data. There will be plenty of time to arrange it in a viz to evaluate players like we did last season (Forwards, Defensemen), but there’s another way to present this data and that is within the realm of tactics and opposition analysis. Last December, I wrote a little preview of what we can do with this data by focusing on tendencies of how and where teams generate offense. If you haven’t yet, I encourage to read these pieces (all are linked in the beginning of that piece I just linked) for the background of what I’ve been imagining for this data since I added in lane concepts last summer.

We already know that passing is a skill and an important one at that, so there is always the importance for the descriptive and predictive levels of analytics (what has happened, what will happen), but one we don’t often discuss is the prescriptive level (what should we do). Combining data visualization of these events and video analysis is the best way forward. In this post, I’ll go over exactly how to use our new viz to pinpoint areas of the game to analyze. If you simply want to go to the viz, scroll to the bottom of this piece.

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Sportlogiq, Passing, and Playmaking

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Last, week, two pieces were posted that talked about a player’s “playmaking” ability. I put it in quotes because it’s a term that gets tossed around a lot and sometimes isn’t defined by those using it. For example, in this piece from Andrew Berkshire (using data from SportLogiq), it is never clearly defined despite being in the title of the piece. Passes are categorized based on direction (north, south, east-west), zone (offensive zone, neutral zone), or some other qualifiers (to the slot, off the rush), but nowhere are they tied to shots. Passes are charted based on “successful pass volume” per twenty minutes. One can assume these are pass completions per twenty minutes. However, with hundreds of passes completed each game, many of them are simply woven into the noise of the game. We’re interested in what leads to events, specifically exits, entries, shots, and goals. Berkshire includes exit and entry passes, so at least those are present. Nevertheless, despite zero detail on what matters most – creating shots for teammates – Berkshire concludes that “we may be witnessing the beginning of the best playmaker of the next generation of NHL stars in Barkov.”

The other piece I referred to was this by Travis Yost on Joe Thornton. He clearly explains what he means by playmaker”: “To me, a playmaker in the NHL is the guy who routinely creates opportunity for his linemates.” Thornton is without a doubt a 1st ballot Hall-of-Famer and one of the best setup men in the league. It’s only natural to think of the man that once made Jonathan Cheechoo lead the league in goals as one of the greatest playmakers we’ve seen over the last decade. Yost’s definition of creating opportunities for teammates is one I would agree with.

If only there was a publicly available set of data that including passing and shot assist numbers for Barkov and Thornton. Oh, wait… All data is at 5v5 because no one cares about special teams except for Arik Parnass.

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Redefining Shot Quality: One Pass at a Time

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:

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.

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From this point on, I want you to forget whatever it is you think of when you hear the term, “shot quality.”

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2015 – 2016 Passing Project Data Release, Volume I

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!

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Toronto Maple Leafs Opposition Analysis

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.

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Toronto Maple Leafs Passing Lane Corsi

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.

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Toronto Maple Leafs Passing and Linkup Network

Recently, I wrote on some data I’d collected from the 2015 OHL Final between the Erie Otters and the Oshawa Generals. This primarily focused on passing network anaylsis that Steve Burtch introduced at the Rochester Institute of Technology Hockey Analytics Conference. Today, I’d like to use the thirteen games we’ve collected to examine the Leafs network.

I’ll be focusing on the weighted degree measure that weights each degree (pass or shot) that a player has within the network. These weights are assigned based on several factors (scoring chance, shot on goal, one-timer, etc.) so we know which connections were more likely to result in a goal than others. This weighting will be adjusted as we get more data, so it’s quite basic and likely not nuanced enough at this point in time.

I’ve used the weighted degree measure because I think it is the best way to use this type of analysis for this sport. This is for a few reasons, some of which I mentioned in the Erie piece, but the biggest is this: Not all players are on the playing field at the same time, so there are actually several networks withing a single game (first line and first pairing, second line and second pairing, and so on). This may level out of over the course of a season, but we’re going to look at the Leafs as a whole, and the Leafs top line network on its own as well. All data is at 5v5 unless otherwise specified. These types of metrics have a purely offensive-minded focus as well.

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