edit: you should probably read this before reading this.
In what is one of my least favorite posts on this blog, I said this:
Young is a case that deserves some real hardcore research. Perhaps, in the future, I’ll be able to look much deeper into why CY has defied the odds for some 260 innings or so now.
You could probably consider this at least my first attempt to look at Chris Young a little more in-depth, although it’s not exactly for the reasons I had earlier planned. On July 24th Young strained an oblique muscle and was placed on the disabled list. Courtesy of baseball reference, here are his pre injury numbers: 118.7 innings, 114 k’s, 39 bb’s, 4 hr’s, 1.82 era. And how about after coming back from the injury two weeks later on August 9th: 54 innings, 53 k’s, 33 bb’s, 6 hr’s, 5.96 era. His strikeouts stayed at a similar level, but everything else was way off. Now the traditional sabermetric thinking here would be simply that Young regressed toward the mean, at least to a degree. After all, nobody is a 1.82 era true talent pitcher and therefore nobody is going to maintain an era in that area. However, especially when an injury occurs, there could be another reason (or reasons) why Young’s numbers showed such a decline after returning from injury. He could have simply been a different pitcher with different stuff and a changed style. That is not as much regression as it is a change in talent level and that’s what I’m going to attempt to look for.
So I have finally loaded all of Young’s starts (that have PITCHf/x data) into a spreadsheet and now I can play with the PITCHf/x data a little bit. Note that this stuff has already been done by many brilliant researchers. Many have gone far more in depth than I could ever hope to, but I don’t believe anyone has concentrated specifically on CY and his injury. First we’ll look at Young’s performance overall, then we’ll take a look at the data pre and post injury. This first graph is perhaps the least interesting, but I’ll throw it up anyway. These are all of Young pitches with start speed on the y-axis and end speed on the x axis (remember to click all images for a better view … edit: definitely be sure to click now, as I had to rescale them to even fit on the blog):
This stuff is admittedly sometimes tough to interpret and tough to display. There are a lot of 90-95 pitches but the are behind the 85-90 pitches. Anyway, it jibes pretty well with something like Josh Kalk’s CY player card, although he is using an algorithm to classify the pitches. Anyway, I think the majority of his fastballs have a negative horizontal break. The sliders range from ~75-high 80′s and have a horizontal break on both sides of 0. The curves are the slowest pitches and down toward the bottom right side of the graph. Finally, there are a few changeups overlapping the fastballs (they have similar break but of course a much different speed).
Now that we’ve looked at all of CY’s pitches, let’s break it down into pre injury and post injury and see if we can spot any noticeable differences. First the speed graph for pre injury (over 1,300 pitches):
Over 95 mph
Pre injury: 3.8% (51 pitches)
Post injury: .01% (1 pitch)
Over 90 mph
Pre injury: 54% (724 pitches)
Post injury: 19% (156 pitches)
How about average start speed
Pre injury: 88.59
Post injury: 86.43
Wow. Unless they were some major changes in the PITCHf/x system, this is pretty striking (I think). (I know there were changes throughout the season, but I am pretty sure that they wouldn’t have this large of an impact). Now, maybe Young went to more breaking and off speed stuff and that’s the cause for the lower velocity. I’m not sure. Either way, though, it appears he was definitely a different pitcher after the oblique injury. Now onto the other two graphs:
Not really sure what to make of this. It appears that the many sliders that were 80-85 pre injury are now in the 75-80 bucket. The curves are pretty non existent, as well. But, with these graphs being relatively tough to interpret, I’ll leave it up to you to decide their relevance.
So maybe we haven’t solved the post injury Chris Young debate (regression or change in talent level?). But perhaps we’ve at least laid out a bit of a methodology in assessing whether or not — and how — injuries impact pitchers. Or maybe we’ve just wasted a Tuesday night. Hey, it’s better than school work either way ; )