Spin rate is the latest in metric. National broadcasts are citing it, Driveline Baseball is using it as an evaluation tool, and MLB organizations are tapping into its predictive potential. Spin rate is sexy. And though we don’t yet know everything about how it impacts performance, we can certainly make a few assumptions.
For instance, I started writing this post thinking the number of revolutions a given pitch made on the way to the plate would predict whiff rate. Welp, I was wrong. More spin did not always help Cubs relievers generate more swings and misses last year.
Carl Edwards Jr. led the Cubs in fastball spin rate and had one of the highest whiff rates in MLB last year, so he’s a good example in favor of my theory. But Alec Mills, who had one of the lowest whiff rates, and Brad Brach, who posted an above-average whiff rate, have essentially the same spin rate. The difference between the four-seamers of Mills and Brach is, of course, velocity.
Yet Steve Cishek, a guy barely touches 90 mph, has a whiff rate nearly twice that of Pedro Strop, who throws harder with roughly the same spin rate. How does Cishek generate so many whiffs with a fastball that is slow fastball and spin rate short of other relievers on the team? Oh, and how do we explain Brandon Kintzler’s above-average whiff rate with one of the lowest spin rates on the team? I don’t know.
No metric tells the complete story. Not spin rate, not velocity, not release point, and not location.
Granted, someone might be able to isolate the effect of spin rate while considering those others. And some have shown spin rate is predictive. In some of my analyses, however, I find things more interesting than spin rate. Like when I asked my computer to find Jon Lester’s most significant predictor of whiffs and the answer was release point.
Each pitcher is different than the next. If you sort a leaderboard only by spin rate, you might miss the next lock-down reliever.