We showed in The Book that there is a small but palpable “pitching from the stretch” talent. That of course would effect a pitcher’s RA as compared to some kind of base runner and “timing” neutral measure like FIP or component ERA, or really any of the ERA estimators.

As well, a pitcher’s ability to tailor his approach to the situation, runners, outs, score, batter, etc., would also implicate some kind of “RA talent,” again, as compared to a “timing” neutral RA estimator.

A few months ago I looked to see if RE24 results for pitchers showed any kind of talent for pitching to the situation, by comparing that to the results of a straight linear weights analysis or even a BaseRuns measure. I found no year-to-year correlations for the difference between RE24 and regular linear weights. In other words, I was trying to see if some pitchers were able to change their approach to benefit them in certain bases/outs situations more than other pitchers. I was surprised that there was no discernible correlation, i.e., that it didn’t seem to be much of a skill if at all. You would think that some pitchers would either be smarter than others or have a certain skill set that would enable them, for example, to get more K with a runner on 3rd and less than 2 outs, more walks and fewer hits with a base open, or fewer home runs with runners on base or with 2 outs and no one on base. Obviously all pitchers, on the average, vary their approach a lot with respect to these things, but I found nothing much when doing these correlations. Essentially an “r” of zero.

To some extent the pitching from the stretch talent should show up in comparing RE24 to regular lwts, but it didn’t, so again, I was a little surprised at the results.

Anyway, I decided to try one more thing.

I used my “pitching sim” to compute a component ERA for each pitcher. I tried to include everything that would create or not create runs while he was pitching, like WP/PB, SB/CS, GIDP, roe, in addition to s,d,t,hr,bb, and so. I considered an IBB as a 1/2 BB in the sim, since I didn’t program IBB into it.

So now, for each year, I recorded the difference between a pitcher’s RA9 and his simulated component RA9, and then ran year-to-year correlations. This was again to see if I could find a “RA talent” wherever it may lie – clutch pitching, stretch talent, approach talent, etc.

I got a small year-to-year correlation which, as always, varied with the underlying sample size – TBF in each of the paired years. When I limited it to pitchers with at least 500 TBF in each year, I got an “r” of .142 with an average PA of 791 in each year. That comes out to a 50% regression at around 5000 PA, or 5 years for a full-time starter, similar to BABIP for pitchers. In other words, the “stabilization” point was around 5,000 TBF.

If that .142 is accurate (at 2 sigma the confidence interval is .072 to .211), I think that is pretty interesting. For example, notable “ERA whiz” Tom Glavine from 2001 to 2006, was an average of .246 in RA9 better than his sim RA9 (simulated component RA). If we regress that difference 50%, we get .133 runs per game, which is pretty sizable I think. That is over 1/3 of a win per season. Notable “ERA hack” Ricky Nolasco from 2008 to 2010 (I only looked at 2001-2010) was an average of .357 worse in his ERA. Regress that 62.5%, and we get .134 runs worse per season, also 1/3 of a win.

So, for example, if you want to know how to reconcile fWAR (FG) and bWAR (B-R) for pitchers, take the difference and regress according to the number of TBF, using the formula 5000/(5000+TBF) for the amount of regression.

Here are a couple more interesting ones, off the top of my head. I thought that Livan Hernandez seemed like a crafty pitcher, despite having inferior stuff late in his career. Sure enough, he out-pitched his components by around .164 runs per game over 9 seasons. After regressing, that’s .105 rpg.

The other name that popped into my head was Wakefield. I always wondered if a knuckler was able to pitch to the situation as well as other pitchers could. It does not seem like they can, with only one pitch with comparatively little control. His RA9 was .143 worse than his components suggest, despite his FIP being .3 runs per 9 worse than his ERA! After regressing, he is around .095 worse than his simulated component RA.

Of course, after looking at Wake, we have to check Dickey as well. He didn’t start throwing a knuckle ball until 2005, and then only half the time. His average difference between RA9 and simulated RA9 is .03 on the good side, but our sample size for him is small with a total of only 1600 TBF, implying a regression of 76%.

If you want the numbers on any of your favorite or no-so-favorite pitchers, let me know in the comments section.

Interesting. Now I’m curious about Jeremy Guthrie.

Thanks!

Not a big fan of Guthrie, at least with respect to his components. A friend of mine actually was his amateur pitching coach for a while when he was young and he like him a lot, at least personally. I only have until 2012 right now, but he is pretty “normal,” +.007 (runs per 9 innings) from 07 to 12. Plus means his RA9 is slightly higher than expected from his components. Of course .007 is the same as zero.

Did you think of doing this as a result of your work on team under/over performing and some of the pitching discrepancies you noticed?

Also, would be curious to see Cliff Lee maybe pre 2008 and post 2008. Thanks for the awesome free content!

To some extent, yes.

Lee is interesting. 03-07, he was +.344. His RA9 was worse than his simulated RA9. From 08-12, he was -.06, so slightly better than an average pitcher. Why did you expect a difference?

I was interested because I seemed to remember he had a point where he started to hardly ever walk anyone, thus “learning” the strikezone. Obviously a gross oversimplification, but that is interesting.

I’m interested in Javier Vazquez. Thanks, MGL.

Ya, me too! I forgot that he was another one who had a reputation for “not knowing how to pitch.”

Holy cow, he was bad! +.274 per 9 from 03 to 11. Even after regressing, that is .17 rpg. That is a lot! Nice catch!

Jeremy Hellickson, the FIP breaker (until 2013).

Haha, yeah, interesting. Well, I don’t have the 2013 data yet, but here goes. For 3 years, he is -.501, so maybe he is the real deal! Even after regressing, that is .121. Remember that FIP ignores BABIP and this really has nothing to do with that. Whether a pitcher has a true high or low BABIP, the difference between his regular RA9 and simulated RA9 is not going to be affected, since they both include whatever BABIP the pitcher happened to have.

Wow so has he been the best “contextual” pitcher as of late?

Good question. Let me check my entire list and order them. I’ll list the best and the worst for a min number of TBF. Of course in 2013, he reversed that trend and his RA9 was .55 run worse than his sim RA9, which brings him down to .239 in 4 years, which, after regression is .078, nothing shocking.

Is that how you would describe this stat? “Contextual runs better than expected?” A combination of hit/out sequencing and LOB%?

Sure. You can call it whatever you want. In the short run, of course it is mostly luck/noise. But there appears to be a skill, given the fairly decent “r” I am getting, although there could be some biases in the data that are creating this. And yes, we are taking about several potential skills, including pitching differently with runners on base, tailoring your pitching approach to the game situation, mostly runners and outs, at least with respect to minimizing runs (obviously my model is not picking up any “pitch to the score” skills since it is not including the score of the game), etc.

I am using the pitcher’s components, s,d,etc., even his wp, pb (doesn’t matter whose responsibility anything is), GIDP, SB, CS, pickoffs, sac flies, ROE, etc. and the sim is telling us exactly how many runs should score based on league averages for base runner advances, force plays, etc. Now, there are some things that might legitimately vary from pitcher to pitcher that is NOT included in the sim, but is also not part of the pitcher’s skill set, like the arms of the outfielders and the vagueries of the park that are not reflected in the components (like base runner advances – actually I use different numbers in the sim for Coors Field and Fenway park). There might be more that I am not thinking of. So I would take this work with a grain of salt to be honest.

I am writing a follow-up article where I list the best and worst pitchers over the last 10 years.

MGL, I’d appreciate this analysis for Kyle Lohse, who seems like a different pitcher starting in 2011, after he came back from injury. Thanks very much.

Chris Young.