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Last season, I began tracking passes that generate shot attempts in an undertaking to explain how offense was being created. This led to various metrics to measure the efficiency of an individual player as well as a team when attempting passes. I name I ascribed to this metric was SAGE, Shot Attempt Generation Efficiency, and it was quite simple.
If a player completes two passes and the recipient attempts two shots on net, forcing a save and missing the net, the player making the pass had generated one shot on two attempts for a SAGE of 50%. Simple, yes?
I began keeping track of how often a team won various categories of efficiency and how often they also won games decided in regulation. The results through 82 games last season and 134 games this season are that it efficiency matters a great deal. Today, I wanted to offer an early answer on the relationship, if any, between passing (shot generation) efficiency and how many goals a team scores.
This data represents every game the New Jersey Devils, Florida Panthers, and Chicago Blackhawks have played this season. It also includes data through November 26th for the New York Islanders and data through November 19th for the New York Rangers.
The Y-Axis represents a team’s SAGE percentage. The X-Axis represents the number of goals scored. All data is from 5v5 situations.
(Click to enlarge)
What we see is a strong correlation (R2 = .883) between a team’s 5v5 SAGE and the number of goals they score at 5v5 play. Now, this represents about 100 of the 133 games we’ve tracked, but it’s not for the entire season of the Rangers and Islanders, so let’s only look at teams we have all games tracked for: the Devils, Panthers, and Blackhawks.
Well, isn’t that something? Through the first quarter of the season for the Devils, Panthers, and Blackhawks, there is nearly a perfect correlation between how efficient they are passing the puck and how many goals they score during 5v5 situations. Why could this be?
It’s my logical belief that a goalie has less time to get set and diagnose a situation when players make crisp, effective passes. We see this on a nightly basis when watching games. I don’t expect this correlation to remain at this level, but I also don’t expect to drop off significantly.
This project is all about finding value in phases of the game and parsing out the noise that exists in Corsi, Fenwick, or even shot totals. How teams generate offense is more important than simply the amount of offense they generate.
I could not have done this without the aid of dutiful trackers. I want to thank Shane O’Donnell, Brian Franken, and Ryan Stoll for some of the data displayed and used in this article. I am hoping snapshots of the project we’re working on will attract more interest and more people will volunteer. I expect the value of this information to only increase as we collect more data.