Archive for Velocity

For this fun exercise we are going to look into some pitchers who gained or lost velocity on their fastball. Usually a gain or loss in velocity could lead to a difference in performance. This season is a little tough because the question of the year is, were pitchers able to throw faster because they knew it was a short season? Or vice versa, did some of these pitchers lose their velocity because they didn’t have time to properly build up their arms? Or are they typically slow starters? Unfortunately these are questions that won’t be answered until the 2021 season begins. With that in mind let’s take a gander and these pitchers and what it could mean moving forward.

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If there were ever a baseball question that keeps me up it night, it’s this: how do the physical properties of pitches affect batted ball outcomes? Many researchers have tackled the subject with varying degrees of success and elucidation. My attempts have focused primarily on a pitch’s ability to generate swinging strikes and ground balls, the first of which used pitcher-level PITCHf/x data while the more recent of which used individual pitch-level Statcast data.

While modeling whiffs and grounders is interesting (and important, too), something strikes me as much more compelling and confounding: the relationship, if any, between a pitch’s physical properties and its batted ball outcomes, whether described as exit velocity, launch angle, or total base-run value allowed, as measured by weighted on-base average (wOBA) or even expected wOBA (xwOBA).

The ability to prove “contact management” as a legitimate and shared pitcher skill has long eluded the Sabermetric community. Assumptions of a league-average batting average on balls in play (BABIP) and, for xFIP, home runs per fly ball (HR/FB) pervade the common ERA estimators (FIP, xFIP, SIERA) we use to gauge talent and assign value. Those assumptions regarding BABIP and HR/FB imply a pitcher’s inability to control them — and there isn’t much evidence to suggest otherwise.

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You may or may not have heard that Yu Darvish is back. By any conventional measure, his latest back-to-back starts of six shutout innings, two hits, and seven-plus strikeouts rank among his best in a long time. By measure of “Game Score v2,” which FanGraphs includes in a pitcher’s Game Log, Darvish’s scores of 78 and 79 are his two best starts since 2017. Because some of his better Game Score starts went more than six innings: these are his two best six-inning starts, period. They happened very recently, consecutively, and he didn’t labor through them, either, throwing just 94 and 83 pitches, respectively.

The last two starts were a gift to those who took a leap of faith. Darvish, who walked at least three batters in seven of his first eight starts (33 walks in 36ish innings!) and 10 of his first 13 (44 walks in 66ish innings!), was an absolute mess. He had compiled a 4.88 ERA, 5.19 FIP, 4.49 xFIP, and 4.96 SIERA in 13 starts, the cherry on top being a walk rate (BB/9) of six. Six! Six batters per nine innings. As my 14-year-old self quoting Ron Burgundy might say: “I’m not even mad — just impressed.”

However, from June 10 to July 3 — a five-start window sandwiched between his early-season futility and his recent wizardry — Darvish struck out 33 and walked just five in roughly 31 innings, compiling a 3.68 xFIP and 3.64 SIERA. Read the rest of this entry »

This year, I’ve been caught up on pitching. I investigated the nuance inherent to swinging strikes, indirectly made a case for completely abandoning the sinker with this piece comparing pitch type outcomes, and (maybe) identified the keys to unlocking pitcher BABIP and HR/FB.

Here, I’ve modeled swinging strike and ground ball rates using only pitch velocity movement. Surely, this work can be improved; my quantitative tool set, while fairly robust compared to the layman, is meager compared to the professional or even hobbyist statistician. Regardless, I think it’s pretty cool, and I hope it adds to the conversation constructively.

Mostly, this serves to satiate my own curiosity. Unfortunately, it may be denser than I expected — few answers are ever quite as simple as you hope them to be, I guess.

Existing Research

I linked to several of my own pieces above. Dan Lependorf wrote about estimating ground ball rates in 2013 at the Hardball Times, although its conclusions have an anecdotal slant. (It thinks about velocity and movement but doesn’t take the requisite steps to bridge the logic.)
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While 2017 was the year of the dinger, it also looks like it was the year of velocity. Has baseball changed (both the sport, and the physical ball itself)? The signs point to yes – but on the pitching from, the importance of velocity has never been higher. Exhibit A:

The slowest fastball the @Yankees threw tonight was 96 MPH… That is completely absurd.

— Daren Willman (@darenw) October 10, 2017

That’s not average. That’s not maximum. That’s … the slowest. Let’s borrow a little bit of math from the documentary “Fastball” to just put into context how crazy this is.

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