Every endeavor has its ultimate goal: some call it the finish line, the holy grail, the epitome, etc. I like to use what is called KISS: Keep It Simple, Stupid.
I love data, all kinds of it. I like to go through rows and rows of numbers in multiple tabs in multiple spreadsheets while trying to make sense of them. No problem at all. But here is the thing: most people hate numbers, algebra, and math in general. They just don’t like it, and that’s fine.
While putting these posts together, this particular endeavor, I usually find myself in the position of trying to wear the shoes of someone who doesn’t like numbers and I kind of got used to it. Not that I now hate numbers, that’s not the case, but instead, sometimes I would like to “show” or “see” the least of them while the “magic” (in this case, the math) happens behind the scenes. Sometimes I want concise and reliable info; more meat, fewer potatoes (although I will still post a lot of numbers, it’s in my nature).
While doing pitching analysis, we are used to swiping through a myriad of stats, comparing, relating, correlating, regressing, and such as we have the feeling that this will lead to a better result. It is understandable and usually true but it can also be exhausting.
That’s why in the past, I’ve been very eager in trying to corroborate that simpler indicators can work as good or better than others that are harder to calculate or as good or better than looking at 10 different stats and trying to make sense of them as a whole.
I’ve had success in using very simple stats as (k-bb)/ip and K%-BB% as good estimators of a pitcher’s future performance; they have provided appropriate guidance. But we know they are not an all around solution, there is not such a thing.
But we can always keep trying.
So in line with that, I wanted to have an indicator, a meta number of sorts, which could be a summary of a few others and work as a quick first sign of what is lying ahead in terms of pitching performance.
Don’t we have other stats for this kind of thing? Sure, and we must continue using them; I’m just trying to simplify part of the work without relegating or distorting the info.
Also, please let’s focus on “first sign”: this is not by any means a complete and catch-it-all solution, it is just a first step just meant to open the door; you still have to keep walking to go beyond.
The Kwindex is an aggregate index composed basically of other stats: (k-bb)/ip, CSW, pCRA, Zone% and F-Strike%. Why these? Well, I have been using them for some time by themselves, relying on my knowledge and instinct to gauge and decide which was more important in each case; that’s not wrong per se, but we know how subjective we humans are, our subconscious biases define our decisions more often than not, so I wanted to find a way I could bypass that process; the Kwindex is the result.
Also, these stats provide a balance between dominance by power (basically Ks) and dominance by control and location (Zone% and F-Strike%) with the ERA estimator in between.
This is a work in progress but it’s a start nevertheless, let’s jump in and see what does it has to offer.
Below you will find the pitchers with at least 16 IP (20 games by 0.8) and with a Kwindex higher than the average for this group, 49%:
The data is updated to include the games until the 17th of August.
Apart from several already proven pitchers, cases such as the likes of Gausman, Civale, Milone, Lindblom, Toussaint and the resurgence of Luis Castillo stand out. It confirms that Dylan Bundy is for real, too.
What do we do with this info? Well, for one part, the higher the Kwindex the higher the probability of continued success for that pitcher. There, it is nicely reassuring to see that guys like Bundy and Gausman are not necessarily over performing and could sustain their early success.
On the other hand, I’m also interested in the pitchers NOT appearing in the list, being Randy Dobnack the most prominent name. Also, Zack Greinke and Max Fried seem to be too low so that calls to be cautious about them.
I will post a weekly leaderboard for the Kwindex and we can watch how does it works (or not) from now on.
EE, Data geek, Baseball fan. Twitter: @camarcano