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09-10-2013, 08:35 PM
  #38
Wesleyy
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Quote:
Originally Posted by schuckers View Post
Kudos for all this work.

Couple overall comments. First, the rink effects on shot locations are non-linear so that the mean adjustment is generally inadequate for rinks. Might want to look at the Total Hockey Ratings (THoR) paper of Schuckers and Curro to see another possible adjustment. Second, as others have noted there is very likely a team effect (outside of rink) in your ratings that will need to be accounted for. You also should take a look at the Expected Goals model of Brian Macdonald as reference point for another expected goals model. Third, it was very nice of you to do the year to year correlation. More years would be very useful, especially considering that 2012-13 was a shortened year.
I agree that the rink bias is non-linear, but I wasn't aware of the alternatives that you mentioned. I'll look into those.

The team effect could be easily removed by using a relative variation similar to Corsi so it's not a big deal.

Quote:
Originally Posted by schuckers View Post
Kudos for all this work.
The reason that shot quality (which is what you're after here) is not utilized more is that its effects have been fleeting. I'm not one to total exclude the effects of shot quality (or average shot probability) but skepticism in this area is well warranted.
I actually have looked into many articles/studies that claim shot quality is more luck than skill, but almost everyone I've found have a flaw of some kind in their study and they also fail to explain how players are consistently able to produce the same offensive numbers in the same offensive opportunities season after season if scoring is really a roll of the dice.

Quote:
Originally Posted by schuckers View Post
Kudos for all this work.
Some technical details: Wilson's BPCI is not the one you want here (it's not appropriate for the type of data that you have) nor is it the one that is used in political polls. A (Bayesian) alternative would be to take (EGF + k)/(EGF+EGA+2k) where k is some number such as k=5 or 10 which would 'shrink' estimates to 50%. You might have to select k based upon a full season rather than on '12-'13.
I'm assuming a bpci is not suitable because my datatype is not exactly binomial? I'm still in highschool and my school doesn't even have a statistics course so I'm applying what I know from reading wikipedia articles, and a bpci seems to be the best solution that I know of. I know BPCIs aren't used in polls but I just wanted to familiar the reader with CIs which is why I chose to incude that part.

I'll look into the Bayesian alternative and more years of correlations when I have the chance to revise the article. Right now I've got a long list of study ideas lined up that I want to look into so it'll probably have to be after I run out of ideas.

Thanks for the comments, I really appreciate that a leader in the field like yourself took time to help me out.

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