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06-17-2013, 09:29 PM
  #32
hatterson
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Quote:
Originally Posted by Cunneen View Post
Let's remember that even if we regressed every player's SH% towards the league average SH%, our predicative power would still be great using these new regressed Shooting Percentage numbers combined with shot differentials compared to using strictly goal rates. But we would be foolish to not refine our regression and be more precise.
Ultimately I think this is the biggest piece missing from this analysis, it's predictive power has not yet been established. Although I don't believe that was the author's goal as much as to provide the framework for the analysis and have others see if it truly means anything (a common approach in academia)

The two questions I believe are still outstanding that I apply to basically all metrics:
1.) How correlated are the results from a given chunk of time (say the 4 years in the study) to another chunk of time (say the 1.5 years since)?
2.) If the results do show a correlation, how predictive are they of a teams overall success if a team either has a large number of positive producers or negative producers?

Basically 1 is asking, is the metric even statistically relevant. If past performance under this metric cannot predict future performance then I really don't care about it.

2 is asking, if the metric can derive future performance from past performance, how well does it predict team performance based upon the summation of individual performance. If having a team full of high performers under this metric doesn't correlate with winning then again, I don't care.

Again, I don't believe that this is a failure of the authors by any stretch, it was simply out of scope of their paper and I look forward to future analysis to determine if this method is truly valuable.

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Last edited by hatterson: 06-17-2013 at 09:34 PM.
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