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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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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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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More data! We’re up to around 275 games now (hope to hit 400 by the playoffs) and I thought it would be a good time to share some more. Today you’ll be getting something new from the previous releases: which players were on the ice for each passing sequence. Now, I’m writing this short post to explain a few things about this process.

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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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