Using Swing Speed To Measure Pitch Deception


Image credit: Edward Cabrera (Photo by Harry How/Getty Images)

Swing speed and exit velocity are largely two sides of the same coin, with one important difference: we get swing-speed data on whiffs too. This is important as we can detect when batters are using their A swings and coming up empty.

Today, we’re going to propose an intuitive methodology for breaking down how pitchers influence batters, specifically, we’re going to define an A swing and look at various pitch types through the lens of how often batters attack that pitch type with their A swing.

A Swing = Bat speed is at least 90% of the batter’s max bat speed for the season

Alternatively, we could define it as a swing which is in the top 10% of that batter’s swings. Here’s what the breakdown by number of strikes looks like, independent of pitch type and location:

We set the threshold at 90% for A, 85% for B, 80% for C, 75% for D and everything else becomes F. This produced a reasonable distribution that appears to make sense on the surface.

It’s also important to note that if a batter recognizes they are going to miss, they might be decelerating their swings mid-swing:

When a batter whiffs on a ball in the dirt, they slow down by almost five mph. This is akin to the “sword” metric on Savant. Let’s take a look at some A swing data, by pitch type and velocity.

The above chart is looking at pitches that resulted in a swing and miss only. Then we chart the % of time a batter uses their A swing based on the velocity. We see something fascinating: The harder the changeup (CH), the more likely a batter is to be using their A swing. We see a similar trend for sliders (SL) as well, which may suggest that the more the pitch looks like a fastball, the more likely the batter is to swing as if it’s a fastball. In other words, pitchers who have good deception on their secondaries should see a higher proportion of A swings against them, all else being equal.

When we look at swings that resulted in balls that were put in play, it’s a lot noisier for secondaries, but a very stark trend for sinkers and fastballs, where batters are not typically able to use their A swings much, if at all, if they want to put the ball in play.

If we try to map % of A Swings to whiffs (swings and misses per swing) we get no relationship:

We see that Edward Cabrera, Cooper Criswell and Martin Perez all have changeups that elicit the most A swings, but that this doesn’t necessarily make their changeups better for generating swings and misses.

There is another lens we can look at with respect to changeups, specifically, the tendency that batters have to roll over and hit a ground ball. Let’s take a look and see if there’s a relationship there:

We get a small relationship, with an R² of roughly 0.10, which is real, but not too significant. However, look what happens when we make the minimum 60 swings:

Our R² jumps to a very significant 0.29, and if we set the minimum to 70, it jumps all the way to 0.44, which is very strong. More importantly, this relationship only appears to exist for changeups, and not for fastballs, sinkers, sliders or curveballs. Do I have a huge amount of confidence that pitchers who are able to draw a high proportion of A swings on their changeups have changeups that will generate negative launch angles? I’d say I’m cautiously optimistic that there may be some evidence to back that up.

We’re presenting this today not as completed, ironclad research, but as a potential way forward for others to build upon. We’re still very much in the early days with these data, however, and we think that looking at A swings is a useful lens that may deepen our understanding.

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