Power play metrics
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08-15-2012, 09:34 AM
Join Date: Jun 2007
Originally Posted by
I honestly don't know how many times this can be gone over. Team shot volume is generally repeatable. Team shooting percentage is generally not - it tends to go back to league average.
Goals are a result of these two factors, and by looking at the maintainable and repeatable statistic, we have a better idea of future performance without adding randomness from shot quality and shooting percentage.
In addition, there are far more shot events than goal events and because of this it is a lot more impervious to small sample variations. You could easily have 10 PP goals in a year by blind, fluke luck, but you'd be hard pressed to generate 20% of your entire shot volume by luck.
I see a lot of people constantly talking about GF and goal differential being more useful than shots. The above reasons are why people with statistical backgrounds tend to favour shooting rates.
While some of us don't wish to dismiss the percentages, that doesn't mean we're statistically ignorant.
Shot volume and shooting percentage are the same in several ways. Both are a skill - different players have different abilities, and since teams are just collections of players, different teams have different abilities. Both are subject to random variation.
The difference is a difference in degree, not in kind. Shot volume stabilizes more quickly at the team or individual's "true talent" than shooting percentage.
What to do? Some people would have us evaluate players based solely on shot rates, to minimize the effects of random variation. This is one option, but not the only one. Here are some others:
If the time period is sufficiently long, we could look at goal rates instead of shot rates. What is a sufficiently long length of time? This is an empirical question that can be answered by finding the point at which the variation in shooting percentage is more signal than noise.
We could regress shooting percentage partially, depending on the length of time we are looking at. What is the appropriate amount of regression for a given time period? Again, a question for which the answer can be found in the data.
We could also incorporate additional information beyond the time period we are looking at. Even if we are just looking at a small number of games where shooting percentage has an unacceptably low signal-to-noise ratio, we don't have to assume all players and teams have equal shooting talent. We could use information from past seasons to estimate shooting talent, or we could make an estimate based on the shooting talent of similar players or players in similar roles. Any of these could provide a better estimate of shooting talent than assuming all players and teams are equal. We could also use these estimates instead of league averages when partially regressing the numbers.
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