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THE BOOK--Playing The Percentages In Baseball

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Saturday, May 28, 2011

Recreational sabermetrics

By Tangotiger, 09:55 AM

Finding the barest of anomalies 13 times out of 600,000 situations.

Love that term used by Phil. 


#1          (see all posts) 2011/05/28 (Sat) @ 12:42

Would a more complex model, one that considered type of hit, not just hits walks and outs, be expected to find more examples of such discrepancies? It surely wouldn’t have elevated the result to any practical significance, but you think it might have made for a more interesting paper.


#2          (see all posts) 2011/05/29 (Sun) @ 12:21

It’s not entirely clear, but I think they ARE taking type of hit into account (it talks about how they assume how far runners are advancing on singles and doubles). I don’t understand why these assumptions are really necessary, since these percentages are fairly readily available, say, at BR (of course it would take some time to implement this, but it would be a more accurate model).

I’m more interested by the concept. This shows that scoring more runs isn’t always better. But I’m not sure that all this is necessary. I would think you could get a decent view of the same idea by simply measuring the (obviously weighted) mean and standard deviation. I suppose we’d need to also know what kind of distribution there is. But more importantly, there isn’t much of a chance that scoring fewer runs overall is going to win more games here, but under different run scoring environments, that might be more different.
Of course, there isn’t ever going to be much improvement, and I odn’t hink that saberists are going to be crying too much over the .001 runs per inning that gives decent chances for these differences to pay off - there’s other factors that come into play there anyway.


#3    Craig Glaser      (see all posts) 2011/05/31 (Tue) @ 00:56

WanderingWinder - That reminds me of a paper which has already been done.  I believe it was MIT-Sloan last year.

I believe they looked at teams which had the same wOBA (and therefore the teams would scores the same amount of runs on average in a simulation.)

However the teams with higher slugging won more games.  The reason was because the std of their runs scored was lower. 

Anyone remember the author? Really cool paper.  You can check it out here: http://www.sloansportsconference.com/research-papers/2010-2/past-years/beyond-pythagorean-expectation-how-run-distributions-affect-win-percentage/


#4    NaOH      (see all posts) 2011/05/31 (Tue) @ 01:20

It looks like he’s still writing with some regularity.

http://www.dugoutcentral.com/?author=15


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