When you say that measure X stabilizes at 200 PAs, for example, how is that reflected in your methodology?

If I understand correctly, you took 400 PAs and compared the 200 odd PAs to the 200 even PAs and looked for correlation.

But that doesn’t tell you that an individuals’ **first** 200 PAs correlate to his next 200 PAs, yet it’s being advertised as such. Instead, it means that it takes 200 PAs consisting of every other PA over 400 PAs for measure X to stabilize. That’s not exactly useful information.

Am I wrong in making this criticism?

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and

http://web.archive.org/web/20080112135748/mvn.com/mlb-stats/2008/01/06

/on-the-reliability-of-pitching-stats/

I’m guessing the marginal benefit of extra plate appearances to the stability would be equal to it’s fraction of the number needed to hit 0.7.

I won’t give too much crap about the 0.7. I do understand that sometimes you have to pick an alpha and stick with it, if only for consistencies sake. For lab standards we’ve gotta go with 0.99

]]>Those SLGs can’t be true. I will recalculate when I have time.

]]>Let me take an example of Eric Chavez against Jamie Moyer. Despite being known to be completely hapless against most other lefties, Chavez somehow managed to hit Moyer to the tune of 323/397/646 in 72 PA. I then did odd-even year split, and found them to be 366/387/766 (31 PA) in the odd years and 285/405/662 (42 PA) in the even years. Except for BAs, which is not surprising, other numbers look pretty good. So, are the OBG and SLG vs a specific pitcher meaningful after 72 PA?

By increasing the number of individual cases (and maybe using Pizza Cutter’s method of splitting into halves), one may be able to determine the sample size needed for “vs a specific pitcher” stats to stabilize. Although some caveats exist (such as these career stats covering many years), this sort of general information could be quite useful.

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