tag:blogger.com,1999:blog-3299311926633621468.post2265537610795763046..comments2024-03-20T08:45:46.965-07:00Comments on Objective NHL: In Defence of OutshootingJLikenshttp://www.blogger.com/profile/02570453428274983835noreply@blogger.comBlogger5125tag:blogger.com,1999:blog-3299311926633621468.post-74942521966272149922013-05-03T14:05:33.993-07:002013-05-03T14:05:33.993-07:00he did a lot of research to make that kind of post...he did a lot of research to make that kind of post. there are many variables to account for. Host Pay Per Headhttp://www.hostpph.comnoreply@blogger.comtag:blogger.com,1999:blog-3299311926633621468.post-22388792769087796132010-11-25T12:26:23.382-08:002010-11-25T12:26:23.382-08:00Thanks Tom.
I decided to post this in order to il...Thanks Tom.<br /><br />I decided to post this in order to illustrate that a simple r^2 analysis, without more, can often lead to the wrong conclusions. <br /><br />In terms of your other point, I assume you're referring to the second part of my post, where I used EV-tied data (the first part of the analysis used all even strength data, regardless of game score). <br /><br />I agree that sample size can be a relevant consideration when using only data with the score tied. <br /><br />For example, in conducting a coin-flipping experiment to determine the percentage of variance attributable to luck, one will invariably find that the percentage increases as the sample size decreases. So comparing the % of variance attributable to luck with respect to EV SH% over the course of a single season with the % of variance attributable to luck in relation to EV SH% with the score tied over the same period of time is, to some extent, comparing apples to oranges - the latter figure will be lower than the former, but part of that is the result of the reduced sample size.<br /><br />I'm not sure if that concern arises here, however, due to the nature of the simulation conducted (i.e. comparing the observed correlation to the actual correlation). <br /><br />If I had to guess, I would say that EV outshooting truly is more important ( in terms of the extent to which it is indicative of team talent) when the score is tied, and that its elevated importance reflects a qualitative difference between the way that teams play with the score tied and the way that they play otherwise.JLikenshttps://www.blogger.com/profile/02570453428274983835noreply@blogger.comtag:blogger.com,1999:blog-3299311926633621468.post-75121621025824632022010-11-25T08:38:18.499-08:002010-11-25T08:38:18.499-08:00Well done. I'm actually surprised you bothered...Well done. I'm actually surprised you bothered to put up <br />a refutation of this; as Ryan points out, doing the in-sample<br />correlation is a major flaw and discredits pretty much anything<br />else. <br /><br />One danger of measuring only ES score-close is that it <br />reduces the sample size, which reduces the predictive value of <br />percentages but not of Fenwick. Percentages are more important<br />than ES-close analysis implies, but your findings hold<br />regardless.Tom Awadhttps://www.blogger.com/profile/09368984892070888703noreply@blogger.comtag:blogger.com,1999:blog-3299311926633621468.post-41482758568178123962010-11-24T14:20:40.482-08:002010-11-24T14:20:40.482-08:00Yeah, that's another angle that could be taken...Yeah, that's another angle that could be taken.<br /><br />IIRC, the predictive validity of 1st half Fenwick differential in relation to EV goal differential over the remainder of the schedule is on the order of 0.3 - 0.4. <br /><br />That may not seem like much, but it has to be considered in context. PDO over the 1st half of the schedule has very little repeat to the 2nd half , and its predictive validity is essentially nil.JLikenshttps://www.blogger.com/profile/02570453428274983835noreply@blogger.comtag:blogger.com,1999:blog-3299311926633621468.post-413562864784539052010-11-23T20:39:06.246-08:002010-11-23T20:39:06.246-08:00I think another major criticism of his work is tha...I think another major criticism of his work is that he basically just showed that past shooting percentage is a good predictor of past goals for. Which shouldn't surprise anyone. What we want to find out, though, is whether past shooting percentage is a good predictor of future goals for. You can do that by looking at shooting percentage in the first half of a season and goals for in the second half, or even games versus odd games, or randomly split the games up however you like.<br /><br />Vic's done all this, of course, and found that corsi/fenwick/shot differential are all much better predictors of future success than shooting percentage.RyanVnoreply@blogger.com