This is an excellent primer.

http://ussmariner.com/2006/08/29/evaluating-pitcher-talent/

Main points to hammer home is that you want to eliminate any possible interaction from the defense when you evaluate a pitcher. And fielding batted balls is 95%+ the burden of the fielders and has nothing to do with the pitcher.

I still don’t get it all but I sure get it better than i did.

Basically when you determine the league average for statistics, it is how a player ranks relative to the middle of the pack.

VORP is an offensive metric that assigns players a certain run value over that which you could expect from a “replacement-level player” (basically the kind of guy you can find hanging around in AAA). It’s weighted for position, so if you have a 1B and a SS who post identical batting lines, the SS will have a higher VORP, because a replacement-level shortstop will be worse at the plate than a replacement-level first baseman.

It’s a complicated, secret formula that…I’ll be honest, not too many people use it anymore. It’s almost completely useless for pitchers, and for hitters, you’re better off looking at simpler, more intuitive stuff like BA/OBP/SLG and line drive rate and all that good stuff.

available at THT. You’ll also see UZR (ultimate zone rating, developed by MGL) and PMR (probabilistic model of range developed by David Pinto). They all try to measure the same thing in roughly the same way, none of them are that robust in figuring out an individual fielder’s actual value.

There’s also SAFE (spatial aggregate fielding evaluation developed by a team of Penn Statistics researchers [disclaimer: I was one of them]) that does a better job than any of the above three, but requires data from BIS and thus it’s only available for 2003-6.

As far as fielding evaluation, we’re pretty good at figuring out how a team as a whole ranks, and can do a decent job at differentiating between infield defense and outfield defense as unit, but getting to individual players requires at least 3 years of rankings from all the above to have any idea.

0 < x < 10 means that Jeff believes Jose Lopez’s defense is worth between 0 and +10 runs above average for a 2B.

and a lot of people still aren’t entirely convinced that pitchers fluctuate around an HR/FB of 11% anyway, so they have trouble stomaching xFIP. FIP isn’t bad, as long as you keep an eye on the home run rate to make sure it isn’t wonky/unsustainable.

“out of a pitcher’s control it is.”

MGL and Tango in response to Dave Cameron’s article on pitching components:

The author also says that there is little (or was it "no”) evidence that a pitcher has any control over the percentage of fly balls that are home runs. That is completely false. Pitchers very much have control over the distances of their fly balls and hence the percentage of them that go for home runs. This is easy to verify in a numbers of ways of course. I don’t know where this idea came from that pitchers have little control over HR/HR, but it seems to be "going around" in some ssabermetric circles.

I think there is a misconception that pitchers have "little or no" control over those HR/FB and that a pitcher’s true HR rate is almost compeltely a function of his FB rate. That is simply not true. In fact, in the research I have done, there is an indication that a pitcher’s true HR rate is very much independent of his FB rate. Or at least as important.

Here is some data on pitchers in 04 and 05. I ran a regression of HR/FB in 04 on HR/FB in 05 for pitchers with at least 500 TBF per year. The average TBF per year was 772 and the average FB was 209. A FB was any air ball, not including line drives (according to STATS).

"r" was .232.

If I only use outfield fly balls, which is defined as all air balls, but not line drives (again, according to STATS), to only the OF locations (an average of 176 OF flies in the 772 TBF), I get an "r" of .222.

The number of pitchers in the sample is 88.

I I decrease the min TBF to only 200 (an average of 530 TBF), I get an "r" of .081 for 229 pitchers.

That sounds like what THT got. We need a larger sample to decrease the uncertainty of these "r’s".

If we increase the sample to include 98 on 99, 00 on 01, and 02 on 03, we get 890 pitchers with at least 200 TBF per year with an "r" of .181 (rather than the .08 with a smaller sample).

For pitchers with TBF greater than 499, we have 317 pitchers with and "r" of .190 (rather than the previous .232 with the smaller sample.

Nothing is park adjusted.

I re-ran the correlations for players who switched teams from one year to the other, in order to make sure that the correlation was not being significantly influenced by the park HR factor.

For 94-05 (regressing 94 on 95, 96 on 97, etc.) data, there were 85 pitchers who had at least 500 TBF in each of two consecutive years and switched teams. The "r" was .203, so the suggestion is that the "r’s" that we are getting are NOT due to the parks only or even mainly.

In MGL’s first study, he did FB=209, r=.232

This gives us an "x" for the equation of x / (x+FB) of 692.

In this case, 692 FB corresponds to about 2500 TBF.

In his next study, he did FB=176, r=.222 . The "x" value is 617. TBF corresponds to about 2700.

In his next study, he did r=.081, with an unknown number of FB, but which I will guess is FB=120. The "x" value is 1361.

Now, there is a danger in how you do a correlation, if you don’t weight each sample appropriately. This is why with a straight regression, you want your samples to have a similar number of "n", amongst themselves, and in the paired sample. There is a better way to do it otherwise, but more complicated.

Looking at the first two studies, we see that r=.50, when TBF is around 2500 or so, meaning about 580 IP.

In may last sample of players who switched teams, the average TBF was 746 and the average FB was 197. "R" was .205 for 85 pitchers.

In the larger sample of all pitchers, the "r" was .190 and the sample size was 407, the average TBF was 760 and the average fb was 196.

I would use the "r" and the other data from the second sample, since it is much larger and the uncertainty from the "r" is much smaller.

There’s also some other stuff, specifically #30, where he shows the relationship between GB% and HR / FB% is weak.

For hitters, the best stuff is probably tango’s linear weights, but since that method is complicated and not really available anywhere, you’re okay just looking at the raw batting line, adjusting for park, and checking to see that the BABIP is sustainable.