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07-28-2012, 05:32 AM
  #11
almostawake
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I'm quite certain that if can be to a large degree. A lot of the 'issues' that posters have identified in this thread aren't really issues at all.

The general approach for isolating a player's on ice contribution is regression analysis. A problem with this approach is that you need an opposition ensemble that has a wide range of player quality. But this is not so difficult, for most players you can come up with a decent ensemble. The real killer with hockey is players play with the same team mates so much. Basically what I'm getting at, is if a player is only on the ice with the same 4 team mates over a full season it is statistically impossible to decouple that player's contribution from the other 4.

There is another trend that seems to be emerging on this board is that people just focus on the currently available data. Even in baseball the amount of raw data has exploded in the last few years. Player tracking, pitch tracking, etc. Typical SABR stuff is about re-arranging a bunch of statistics to come up with a number the correlates well with a desirable outcome. But even baseball has moved way beyond this in the last few years. Now the game is about analytics. The quantitative guys being hired in MLB front offices these days aren't working with OPS, WAR, etc. They're developing and exploiting new data streams. Hockey is headed this direction to. Anything that can be done, likely will.

Just as a side note, I find the fact that American Football has been brought up a few times in this thread kind of funny. No sport is more difficult to quantitatively analyse. Short seasons mean there's little data, extreme position specialization, the Oline, etc. If you want to talk about a sport that's exceptionally ill-suited for statistical analysis, it's football.

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