Madison Bumgarner figures to be the Giants’ biggest trade chip — I’m using chip instead of chit because chip sounds better — at this year’s trade deadline, and so it’s reasonable to presume that he’s being heavily scrutinized by the front office and every team in the league.
His season opener against the Padres in San Diego was remarkable in that he really looked like a solid version of his former self. No sign of the fatigued, laboring Bumgarner of last season and no evidence that the dirt bike accident in 2017 was still affecting his velocity and spin rate, as it seemed to even through last season.
It’s still very early in the season — comically so — but the Giants just made a trade yesterday — Bumgarner could be moved at any time! The early returns on his Statcast data (pitch spin rate and velocity) are very promising. Yes, he did give up a grand slam to Cody Bellinger last night on a fat pitch in the middle of the strike zone, but around that bad third inning, he looked fine. Not an ace, but fine. A fine pitcher with #1 starter potential (or a great #2) is the type of pitcher playoff-bound teams will be looking for very soon, and they’ll be looking at Statcast data to at least in part aid in their decision-making process.
A reminder about these averages before we get into them:
4-seam FB: 2,226 rpm / 92.9 mph
Cutter: 2,185 rpm / 88.0 mph
Sinker: 2,123 rpm / 91.9 mph
Curveball: 2,308 rpm / 78.2 mph
Changeup: 1,746 rpm / 83.9 mph
Bumgarner’s 2015-2017 averages
4-seam FB: 2,283 rpm / 91.9 mph
Cutter: 2,227 rpm / 87.0 mph
Sinker: 2,243 rpm / 91.8 mph
Curveball: 2,346 rpm / 76.7 mph
Changeup: 1,627 rpm / 84.2 mph
Bumgarner’s 2018 averages
4-seam FB: 2,173 rpm / 91.9 mph
Cutter: 2,136 rpm / 85.4 mph
Sinker: 2,084 rpm / 90.9 mph
Curveball: 2,296 rpm / 77.4 mph
Changeup: 1,465 rpm / 83.5 mph
So, Bumgarner had clear, distressing decline last season on his entire pitch arsenal. Again, two outings do not a bounce back make, but here’s what Statcast has so far in 2019:
Caveat: I’m not including Statcast’s measures for a four-seam fastball and slider, as he’s thrown each pitch only once and the slider is not typically a part of his repertoire.
Cutter: 2,415 rpm / 85.5 mph
Sinker: 2,364 rpm / 90.9 mph
Curveball: 2,549 rpm / 78.2 mph
Changeup: 1,491 rpm / 84.3 mph
A lot more spin on every pitch and a slight uptick in velocity. He threw his cutter 41 times last night, which suggests he trusts that grip a lot more than his four-same fastball. Unfortunately, the extra movement isn’t masking the low velocity. In the interview I posted Monday with Rapsodo GM Art Chou, he emphasized that velocity trumps movement. Bumgarner has allowed two home runs so far this season, both off that cutter.
But even if the early data is encouraging, there was still a noticeable difference between his first two starts, and that had to do with his fastball velocity. Statcast uses 2-seam fastballs and sinkers interchangeably, so when you see Sinker above, it also means 2-seam fastball. In his first start of the season, Bumgarner hit 91 mph or better on the radar gun in 22 times out of 96 pitches, all with that 2-seamer/sinker. Five of those were 92 mph or higher, topping out at 92.6 mph. He threw his sinker 28 times total.
Last night, he threw his sinker 31 times, and hit 91 or better just 7 times, maxing out at 91.5 mph. It wasn’t a linear drop, though. The pitch dipped to 89 in the 4th inning, but then he hit 91 twice in the seventh inning. Still, in the space of two starts, his sinker, virtually his primary pitch, has dipped in velocity just a little bit, and for a pitcher who didn’t have much to begin with, that’s something to keep an eye on.
The other difference between starts was exit velocity. A really small sample size, of course, and the Dodgers’ lineup is better than the Padres, but San Diego averaged an exit velocity of 90.6 mph in their 17 Batted Ball Events (BBEs) against Bumgarner while Los Angeles averaged 95.6 mph in 18 BBEs.
Two starts are so random it probably doesn’t mean much, but this is baseball fandom we’re talking about here — reacting to small sample sizes is what it’s all about...