It’s been a while since I’ve looked a little closer at passing data, tracked manually by Corey Sznajder, with the methodology and tracking based on the passing project, originally administered by Ryan Stimson, whose pioneering work here has culminated in what will eventually become a cornerstone tactics manual.
A feature often overlooked in the tracked data is the summary Game Log, where Sznajder amalgamates game data offering it up in a comprehensive game log. Using the game tool on the Natural Stat Trick website, with a downloaded .csv file (a compressed version of a Microsoft Excel file), analysts can synch game by game data with the micro tracked data to get an even greater level of context at the team level. The possibilities here are endless.
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Tactics execution and identification with teams fervently going over video, picking out traits, similar signals for strengths and weaknesses appear in the data. In the examples we will explore below, we will identify how teams making passes what the data offers in terms of glimpses about forechecking tactics and pressure.
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The data contains sides of the ice to which passes are made. Without the proper delineation between forwards and defensemen, we can see how teams prefer coming up the wings – and which teams prefer either side.
Corey has tracked games from 2017-18 and a relatively smaller amount for the current season, but before we get into the game logs, I would be remiss to point out there’s a great visualization tool created by Christopher Turtoro that has been updated with the current season data.
Christopher (a worthy follow) has created a very valuable tool to compare the All Three Zone Project (Corey’s tracking project name) the microstats across players and categories, and it would be worthwhile time spent to go over and familiarize with this data and potential impact that it has on analysis.
The NHL is already playing around with player tracking technology to spread their nets to amass greater amounts of individual data points, and even though it’s woefully wishful thinking that any of that data will be made public, on the off chance a portion becomes available, using it in analysis would be easier with established familiarity. There are no limits to the vast amount and type of data will be available. For the purposes of analytics studies, it’s best to understand concepts that pertain to winning will only be enhanced with more finely tuned data. Stats have evolved from shot attempts, Corsi, Fenwick and the such, to everlasting metrics like shooting and save percentage, individual and on-ice, to expected goals (xG), WAR (wins above replacement) and GAR (goals above replacement) and the evolution will continue as long as analysts continue to integrate newer data into the basic concepts. Identifying the trends and using the data to back it up.
A great example of video and data merging, and the work involved is in this piece by Harman Dayal, focusing on the Vancouver Canucks counter attack.
Game Logs
Let’s Dive right into the game logs, but before I do, just a reminder that Corey does this tracking manually. He offers the data to subscribers of his patreon. The amount of work and dedication to get this type of data into the public sphere is overwhelming, so if you enjoy the analysis spearheaded by this data and the efforts, and you can spare a couple of bucks to ensure its continuity, please visit and support Corey’s efforts.
Game logs identify specific games, and are available in each team tracking excel file administered by Corey. I’ve amalgamated the team game logs available for 2017-18 and 2018-19 seasons. Games played are aggregated, while individual metrics/categories are averaged events in digestible sets to get a handle on team traits. Enhanced system analysis can be gleaned from these logs at the team level – in lieu of proper player tracking technology. I’ve only touched on a few metrics, there are a lot of others of interest.
The first table below identifies averages of per game multiple passes, low-to-high and Behind the Net passes per team – based on the sample size – from last season and this one. Passes represent a sequence with the end result being a shot of goal.
Each column represents a color scheme handling the range for teams, with a darker blue shade indicating leaders, and fading into deep red for teams at the bottom. For example, San Jose in 20178 have a deep blue color for all three categories, clear leaders in making multiple passes, often originating from behind the net and completing a sequence of low-to-high play for a shot on goal. No other team leads the league the way the Sharks do here in these categories.
Chicago data seems to indicate a lot of rush attempts, as a leader with multiple passes, but not really leading from low to high and behind the net passing. This tradition is continuing this season.
In contrast, Toronto in 2017-18 made very little multiple passes to generate a shot on goal, a lot of it originating from work below the goal line and getting pucks back to the point – swarming the goal. The Leafs can rush with the puck with a contingent of skilled, scoring forwards that can use individual skills to score.
Passing type and play is one thing – and if forechecking data was available, these metrics gain more relevance and value.
We can isolate where teams like to make passing plays. The table below denotes each season’s aversion of getting pucks up one side or the other. Intuitively, its clear teams would rather come up the wings rather than straight down the pipe. A team average appears at the end of the table, and I’ve highlighted leaders on each side, left, center and right for both seasons.
Philadelphia and Vegas really preferred the left side, while San Jose and Calgary preferred the right side. In comparison to the wings, the center rates of passing are lower. The standard deviation table is shown below:
There isn’t enough of a sample size yet for 2018-19, but as more data is tracked the standard deviation will normalize closer to the 2017-18 season data.
There are a lot of practical applications for this data and I hope to integrate more in future writings in this space.
