First there was PITCHF/x and things were good. Things were great in fact since around 2006 when it was installed in ballparks all over the country. We had exit velocity to ponder. Exit velocity is a hilarious stat when you think about it. It’s the statistical equivalent of noticing that geese can fly. It adds a measure to the obvious, but that measure is still interesting in it’s own right when Mike Trout makes a 114 mph line drive out and you think, somebody caught that.
If ever there was a flaw with PITCHF/x it was in its ex post facto analysis of spin. It basically amounts to if an 84 mph slider breaks 6 inches from its nascent trajectory and ends up out of the strike-zone, spin is guessed to be behind this break but it isn’t an exact measure — it’s assumed based on the magnitude of the deviation as marked by release point and landing.
Now there is Statcast. Statcast did to PICHF/x what the internal combustion engine did to the horse-drawn buggy industry. Statcast can actually see the spin. Statcast is like a tiny Muhammad Ali butterfly with a bodycam that can video the rotation and count the spin with a tiny clicker in its proboscis. It’s so fast it’s invisible, like those super-fast invisible Star Trek people who tried to take over the Enterprise. I picture a bunch of them standing around a baseball with lab coats and clipboards as it takes several days to reach the catcher’s mitt. OK not sure exactly how spin-rate is measured but it’s fantastic trust me.
A stat is only as good as its utility and as such we are only now starting to see how spin-rate can be employed by the pros — namely more innovative front offices around the league. There is some early indication that spin-rate can account for a healthy and predictable swing-and-miss correlation. We knew for instance that Seth Lugo’s curve “falls off a table” but are now able to quantify it with a literally off the charts (and a record) 3,498 rpm measure.
Hopefully the Mets as an organization can capitalize on investments like Statcast (and it looks like they have a Peter Parker level spinner in Lugo). They sure missed the spin-boat on Colin McHugh though, who tweaked the spin on his breaking pitches and had that nice stretch in 2014.
I see spin rate data like it’s a formula for time travel that an alien species gives us so we can help them out in three thousand years or so. We have all this great information and we don’t know what to do with it. Also, it doesn’t (yet) have a real time application. I don’t think Collins is getting “make sure you put in Salas tonight — his spin-rates are mad trending” calls in the clubhouse. We only have after-the-fact analyses, which are nonetheless valuable. Spin-rate ultimately is an observational stat, which is part of its beauty — it quantifies (as opposed to interpreting) what is actually happening.
We didn’t need Statcast to know that Bob Gibson threw a ridiculously nasty slider, but with spin-rate we can quantify optimal zones for inducing swings and misses and there appears to be tremendous potential for that sort of thing. There is some indication, for instance, that slower spins at certain velocities assist in optimizing off-speed pitches. Fascinating stuff for sure, even for non-physicists it’s a brave new world of stats when we can put a real number on a freakish occurrence.
There’s another thing that spin-rates may assist with: pitch recognition. You might watch a game like Matt Harvey’s first start and determine that he threw 38 fastballs, 24 sliders, and 12 changeups. Someone else might say Harvey threw 36 fastballs and 26 sliders. Harvey throws a really hard slider, and sometimes it seems to break down more than in making it look almost like a sinker, or a fastball with a tiny curve ball tacked on at the end, almost like a cut fastball with some sink. Like that heavy bowling ball cut fastball that Bartolo throws that we all know Harvey is a big fan of (especially after a Bartolo start). That’s the part that’s hard to quantify. The stuff that’s going through a pitcher’s head.
If a pitch acts like another pitch but is technically a pitch of the first variety – if it looks like a cutter but is technically a slider because of the way it’s thrown – does it really matter if the result is the same? Bartolo’s sinker clocks in at around 88 mph with “obvious tail” but little actual sink (for a sinker). Doesn’t that make it a little like Harvey’s slider which shows a lot of sink for a pitch that’s supposed to also break horizontally? If it walks like a duck, right? The hope is that spin-rate can demystify some of these distinctions. I probably saw fewer good tailing Matt Harvey sliders and more of what looked like a cut fastball with late 12 – 6 break. So whether we start calling curveballs “dark-green-zoners” for their location on a swing-and-miss correlation chart remains to be seen, but change is in the air.