The Anatomy of a Pitch

Theodore Feder

A tactic in Major League Baseball that has become wildly popular in recent years has been the defensive shift. Teams shift by adjusting the positioning of their infielders to maximize the likelihood that a batted ball is converted into an out. While teams used fewer than 2,500 shifts in total in 2011, they deployed over 13,000 in 2014, with more expected in the years to come.

Teams are increasingly using infield shifts to convert more batted balls into outs. (Courtesy of bronxbaseballdaily.com.)
A Right-Leaning Shift. Teams are increasingly using infield shifts to convert more batted balls into outs. (Courtesy of bronxbaseballdaily.com.)

The rationale behind this tactic can be explained using a sabermetric called BABIP (batting average on balls in plays). BABIP is the proportion of balls hit into play (either by a certain batter or against a certain pitcher) that become hits. Historically, most pitchers’ BABIP has remained close to 0.300 (30%) over long periods of time (i.e., multiple years). In other words, pitchers have exhibited surprisingly little ability to consistently outperform the average rate that balls in play become outs.[1]

Given that around two-thirds of plate appearances result in a ball in play, progressive teams realized that there would be a huge benefit if they could find a way to systematically achieve a lower BABIP for their pitchers – that is, regularly convert a larger portion of balls in play into outs. As mentioned above, the most successful strategy to date has been the shift.[2] But, is there perhaps something pitchers could do to systematically reduce their own BABIP? Specifically, are there any kinds of pitches that they are under- or over-utilizing?

To explore this question, I gathered PITCHF/x data on pitches resulting in batted balls from the last two months of the 2015 season, which amounted to data on over 800 games and 40,000 pitches.[3] I then ran a logistic regression to determine which attributes of a pitch contribute most to the likelihood of a batted ball becoming an out. While my model included several variables, I will focus here on some of the most notable findings.

In looking at the speed of a pitch, I found the ball’s straight-line velocity has a rather large effect on the likelihood of an out. Specifically, one extra mile-per-hour on a pitch increases the odds of an out by 8%, which translates to nearly a 15-point drop in expected BABIP. This means that faster pitches make it more difficult for hitters to make hard contact and drive the ball away from defenders.

While harder pitches leading to lower BABIP makes intuitive sense, I was surprised to find a stark contrast between the impact of a pitch’s horizontal (side-to-side) and vertical (up-and-down) movement. For example, the ball’s vertical velocity and final vertical position have two of the largest effects on the chances of an out. A pitch that is an inch lower when crossing the plate results in nearly a 25-point lower BABIP than average.[4] This translates to to one fewer hit per 40 balls in play, which sounds low but would have a major impact over the course of an entire season! In contrast, the side-to-side movement of a pitch – as measured by horizontal velocity and final horizontal position – has a statistically significant, but minor effect on the likelihood of an out. An inch movement in the ball’s position (in either horizontal direction) only improves the odds of an out by 1.5%.

Batted balls become outs more often when the pitch crosses the plate lower in the strike zone, particularly against left-handed hitters. Horizontal movement has a minimal effect on the likelihood of an out. (Plots from the perspective of the catcher.)
Batted balls become outs more often when the pitch crosses the plate lower in the strike zone, particularly against left-handed hitters. Horizontal movement has a minimal effect on the likelihood of an out. (Plots from the perspective of the catcher.)

Relatedly, I found that the spin rate of the ball (measured in rotations per second) has a meaningful effect on the outcome of a play.[5] For instance, a pitch with a spin rate of 36 rps (the average is 31 rps) would shave approximately seven points off of the expected BABIP.

These discoveries are important, but they view each pitch in isolation. Suppose we want to understand the effect of one pitch on the subsequent pitch (e.g., when a changeup follows a fastball)? When I incorporated the difference between the speed, horizontal location, and vertical location of two pitches, the results were quite modest: a large change in speed or vertical position does reduce BABIP in the data, but the effect is not significant. (A large change in horizontal position actually increases BABIP slightly.) I would venture to guess that these results understate the importance of pitch sequencing, as it may take more than one pitch to “set up” a hitter and there are likely other important interactions not considered here.

What do all of these findings mean? For one, it is an indication that pitchers can influence BABIP on a pitch-by-pitch basis through careful pitch selection. While it is certainly not feasible for pitchers to overhaul their entire repertoire or to throw only one or two types of pitches, it would beneficial for them to include more fast pitches with downward movement (e.g., two-seam fastballs and splitters) and fewer slow pitches that move horizontally (e.g., sliders and some cutters). By doing so, pitchers could complement the shift while being less at the whim of lady luck when a hitter gets his bat on the ball.

[1] This means that the only outcomes that are exclusively determined by the pitcher and batter – and not the defense – are a walk, strikeout, or home run. These are referred to as the “three true outcomes” of baseball.

[2] Travis Sawchik’s book Big Data Baseball gives an excellent (albeit non-technical) overview of how the 2013 Pittsburgh Pirates relied on shifts to break their 20-year streak of losing seasons.

[3] Carson Sievert’s R package pitchRx is quite useful for downloading PITCHF/x data.

[4] Note that the strike zone is just under two feet tall.

[5] Counter to intuition, spin rate is not highly correlated with velocity. Curveballs, for example, have very high spin rates but low velocities.