Justin is a part-owner of a goalie school up in Albany (www.macgoaltending.com) and he is in the midst of co-authoring a technical manual on the position. Needless to say, this guy knows his stuff.
For several years now, hockey analysts have searched for a more comprehensive and reliable alternative to traditional goalie rate stats G.A.A. (Goals Against Average) and Save Percentage. Michael Shuckers, a statistician from St. Lawrence University has released the most advanced metric for evaluating goalies that we have to date: Defense Independent Goalie Rating (DIGR). This innovative new stat was first presented at the MIT Sloan Sports Analytics Conference in March of this year.
The metric aims to normalize shot distribution, type and team strength across all goalies facing more than 600 shots (based on 30 shots per game, for min. 20 games). It utilizes smoothed spatial mapping of shots taken and the frequency in which shots from those locations produced goals. Unfortunately, the data used was from the 2009-2010 season, and I was unable find this past seasons data at the time of writing this post. Ultimately, it does not affect the analysis at all, I just prefer the most recent data.
Professor Shuckers used ESPN GameCast shot distribution data to plot every shot taken in the NHL for the ’09-’10 season, along with shot type (backhand, deflection, slap, snap, tip-in, wrap and wrist) and team strength (even strength, PP, or PK). Empty netters, penalty shots and shootouts were eliminated from the data. The total amount of each shot type taken throughout the year was weighted by frequency into the 30 shot per game sample.
Although not all goalies face the same distribution of shots during the course of the season, Professor Shuckers believes that the sample is large enough to directly compare the probability a goalie will allow a goal on a certain shot type from a certain location compared to all of the other goalies in the sample.
I won’t bore you guys with the complex calculations that Prof. Shuckers uses, but I will give you a familiar example. In 2009-2010, Henrik Lundqvist was the 5th ranked goalie in the NHL based on DIGR. His actual save percentage that season was .9208, and his DIGR was .9237. This is not a huge difference, but it suggests that Hank faced a more difficult distribution of shots than the league average, and would be more successful had he faced that distribution instead of the actual shots he faced.
The usefulness of this statistic is that it essentially puts all goaltenders on an even playing field and seemingly removes stingy or porous defenses from the equation. Unfortunately, it leaves far too many variables unaccounted for to be the all-knowing metric that we have been waiting for.
For example, rebounds and screen shots are not taken into consideration. I don’t have to explain to you guys that if a point man takes a big slapper through traffic, it has a much better chance of finding twine than if the goalie has a clear lane of vision. DIGR treats those shots the same.
Same goes for one-timers, the speed and angle of a pass that precedes the shot (for example, did a goal scored from the slot come from a rebound, or a pass from behind the net?) and deflections off the goalie’s own teammates. The location within the net that the puck crosses the goal line is also unaccounted for. However, Professor Shuckers suggests that it is possible to add some of these variables to the calculation in the future.
Additionally, the essence of this stat comes from predicting performance rather than observing performance. Since all goalies did not face the same distribution of shots, you are not observing statistics about the saves a goalie actually made, but what they likely would make. In my opinion, it does not seem to have much predictive value for future performance, but would serve more as a tool for evaluating past performance and comparisons.
I don’t mean to insinuate that this stat is not incredibly useful or innovative. Most advanced metrics in sports (which hockey is in it’s infancy of developing) have inherent limitations, and the analysis is all about understanding those limitations and keeping the proper perspective when drawing conclusions from them.