Calling All Stat Nerds: Introducing the Swinging-Strike Z-Score Calculator
Whiffs aren’t just cool, they’re also indicative of how valuable a pitcher is or can be. As such, I’m excited to introduce our z-score calculator for pitch-type swinging-strike rates. To some degree, this tool will help us understand not only why a pitcher is effective, but also how effective he should be. You with me so far?
Swinging-strike percentage (Swstrike%) is defined as the percentage of whiffs per pitch (i.e., number of whiffs divided by the number of pitches thrown), and is not to be confused with “whiff/swing.” Swstrike% is more consistent year-over-year than whiff/swing, which is why all the cool people prefer to use swstrike% when they write about baseball things¹.
A z-score tells you how many standard deviations a score is from the mean, which in this context is how much better a particular pitcher’s pitch is than the same pitch from every other pitcher. Try saying that five times really fast. Each z-score also corresponds to a percentile ranking (see chart above). To give an example, a z-score of 1.0 means that this pitch is better than 84.1% of the rest of those thrown.
Let’s use Jake Arrieta as an example to test this tool out. What’s his most digusting pitch? I think it’s his curve. According to this tool, Arrieta’s curve draws more whiffs than 92.4% of MLB (z-score of 1.43). Yep, my eyes weren’t lying.
To find a pitcher’s Swstrike%, I like to use Brooks Baseball. It’s important to note, though, that that particular site refers to this stat as “Whiff Rate.”
To use the calculator, download the file(s) below and open in an excel document. There are macrotabs on the bottom of the doc for each pitch type, which comes from data Baseball Prospectus has pooled from the 2012-16 seasons (only pitchers with 200-pitch minimum were included). Simply enter in the swstrike% in the appropriate cell, and DO NOT enter in numbers for any of the other cells. To view data, click on “format” –> “row” –> “unhide.”
Because the ability to miss baseball bats is generally a big part of what makes a pitcher successful, I use this tool when writing most of my pitching analyses. We can also use it to extrapolate good performance in small samples to identify pitchers who might thrive in bigger roles. Way back when, Eno Sarris and Jeff Zimmerman used an iteration of this concept to argue that Carlos Carrasco was a diamond in the rough. They were right!
In addition, use the individual pitch Swstrike% benchmarks below to aid your interpretation. The last publicly available update was produced by Sarris and Zimmerman in July 2014.
For troubleshooting, email firstname.lastname@example.org.
¹Basic Pitching Metric Correlation 1955-2012, 2002-2012