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2013-2014 Devils Passing Review: A Passing Stats Primer

This is a post dedicated to explaining what goes into tracking, analyzing, and presenting passing statistics. It will be updated from time to time as this process evolves. Read on for the details.

Ed Mulholland-USA TODAY Sports

Last season, I began tracking passes and shot attempts generated by the New Jersey Devils. I wasn’t looking for anything in particular, but as the process evolved and I learned how much data can be collected and analyzed based solely off of a player’s passes, I soon realized how invaluable this information would be. Like any process, I’ve taken a step back to see which data is truly important and what is filler. Below are links to my findings from the 2013 - 2014 season for the Devils and their opponents.

Summer Passing Series Links

Part One: Efficiency and Winning

Part Two: The Transition Game

Part Three: Offensive Zone Analysis

Part Four: Generating Goals

Part Five: Corsi Contribution

Part Six: Frequency of Offense

Part Seven: Does Accuracy Matter?

Part Eight: Team Comparison

Part Nine: Devils and the Metro

Part Ten: Devils and the Atlantic

Part Eleven: Devils and the Central

Part Twelve: Devils and the Pacific

From these pieces, there's a lot that's important and some that is of lesser importance. Passing alone did not tell us much last season, but shot generation and from where offense was generated were of vital importance in these contests. Each completed pass that results in a shot taken by a teammate counts as one "shot attempt generated" or "SAG." This is tracked to attempt to determine which teammates are better at generating opportunities to shoot. I included a "shot generated" or "SG" to track the highest quality of shot attempts. This is done to measure how much more efficient certain players are in generating shots. This is called SAGE, or Shot Attempt Generation Efficiency. Basically, which players convert more of their SAG figures into actual shots on goal.

Shots by Zone: I decided to separate where shots were being generated from: either from within the offensive zone, or on stretch passes or controlled entry passes from the defensive and neutral zones. This gives us a bit more information about how shots are being generated. You can identify a shot attempt/shot as being generated from the defensive/neutral zones as it has the D/NZ prefix, with OZ serving as the prefix for shot attempts/shots generated from within the offensive zone. The SC prefix indicates the shot attempt/shot was generated from within the Scoring Chance area of the ice.

Corsi Contribution: Finally, a stat you’ve heard of, right? About halfway through last season, I started to break down Corsi and find out a little more about how players contribute to it. Corsi, and Fenwick for that matter, is quite simple and tells us what happened, but nothing as to how or why.

I took the total number of shot attempts for when a specific player was on the ice (his Team CF) and started there. Each player has his own shot attempts (his iCF), so that helps identify a part of a player’s contribution. I then added the shot attempts a player generates (his SAG and A2 SAG) and divided the sum of those three numbers by the Team CF. What I had was a percentage of shot attempts that player directly contributed to: his own shot attempts and those that occurred from his passes.

Corsi Contribution Percentage (CC%) identifies the true shot-generators on each team and how much offense flows through them while on the ice. Depending on how it breaks down, it quickly identifies players that are more shooters as opposed to distributors of the puck in both potential primary and second assist forms. It can pinpoint from where on the ice offense originates from.

Using Time on Ice

I had the SAGE stat to illustrate who was more efficient at generating shots and setting up players with higher quality shot attempts, but I wanted to search for other ways to determine how efficient players were on the ice. This became the theme of why I continued doing this: there has to be more efficient ways to measure player performance. Identifying who did the most with their ice time was a logical next step.

I started dividing a player’s ice time by their SAG and SG figures to figure out exactly how long at even strength it took a player to generate a shot. As I continued adding data, I could look at how often a player was involved in creating a shot attempt, a shot, and a goal, and also from which area of the ice.

I had all of this data, but it was only for the Devils. It never really got out of the "that’s neat" stage because how was I to know whether the Devils were really any good at any of this?

Tracking the Opposition

As the season wore on, I began tracking the Devils opponents and comparing all of these stats against each other to see what correlated strongly to winning games and what were tendencies of the stronger teams/players in the league. Over the summer I finished tracking all 82 games for the Devils opponents.

The findings were quite intriguing. I was able to do two things with all of this data: 1) It allowed me to take data from every player and team in the league and find a baseline average for every stat category I’ve created; and 2) as I mentioned, it focused in on what truly mattered in a game and what gave a team a better chance to win.

2014 and Beyond: For the upcoming season, I've decided to expand upon shot generation and further break down offensive contributions. Going forward, you’ll also see an A2 SAG figure that represents the penultimate pass made before a shot attempt or shot was generated. I call it "A2 SAG" because it essentially tracks potential second assists. You’ll see this stat for all variations you would see the SAG stat (which you can also think of as a potential primary assist). I decided to track this after noticing that a breakout pass from a defenseman often went unrecorded as leading to a shot attempt if another pass is made afterwards.

Also, I'll be tracking passes and shot generation in the Scoring Chance area in front of goal (from the top of face off circles, down through the face off dots, and then to the goal). This will further separate offensive zone performance into higher quality shot attempts and shot attempts from less dangerous areas (i.e. point shots).

If you have any questions, comments, suggestions, or general statements, leave it in the comments or hit me up on Twitter and I’ll do my best to answer in a timely manner.