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Team Performance

Good Intentions: Analyzing How Command Can Affect Hitters’ Performance Using STATS Pitch Intent Data

By: Henry Ettinger

What separates STATS Pitch Intent data from other metrics is that it accounts for something they don’t consider.

The proprietary data allows us to take into account when a pitcher is intentionally trying to throw a ball rather than just assuming all pitches are meant to be strikes. It also gives us an opportunity to differentiate between a pitcher’s general ability to throw strikes (control) and his ability to throw pitches exactly where he wants whether that be in the zone or not (command).

Our most recent dive into this season’s numbers shows why it is so important to make this differentiation when evaluating pitchers. Here, we’ll show what the data says using three of our proprietary statistics – swing rate, contact rate, and whiff rate – for the best 25% of pitches in terms of command and the worst 25% of pitches in terms of command.

Let’s take a look at pitches that ended up outside of the zone, separated by pitch-type:

FastballSwing RateContact RateWhiff Rate
Bottom 25%16.064.25.7
Top 25%56.274.114.6
Difference40.29.98.9
Breaking BallSwing RateContact RateWhiff Rate
Bottom 25%16.138.310.0
Top 25%63.154.228.9
Difference47.015.918.9
OffspeedSwing RateContact RateWhiff Rate
Bottom 25%15.756.36.9
Top 25%71.062.726.5
Difference55.36.419.6

STATS Pitch Intent data backs up what we might have guessed to be true on pitches outside the zone. Well-commanded pitches that end up as balls are usually just off the plate. As a result, hitters swing and make contact with them more often because those are hittable pitches.

However, whiff rate increases dramatically for every pitch-type as well, which we believe is extremely important in terms of evaluating a pitch’s value. Pitch Intent data allows us to make that distinction between pitchers who are throwing balls because they are struggling with command and those who are missing the plate in an effort to get hitters out.

Now let’s take a look at pitches that ended up in the strike zone. If you were just looking at control, strikes would be a positive result that would factor into statistics like zone percentage and walk rate. But using Stats Perform’s advanced metrics, you can see that there is actually a big difference between correctly and incorrectly commanded pitches.

FastballSwing RateContact RateWhiff RateAvg. Inches from Center of Zone
Bottom 25%62.184.19.89.36
Top 25%70.384.610.98.16
Difference8.20.51.1
Breaking BallSwing RateContact RateWhiff RateAvg. Inches from Center of Zone
Bottom 25%61.884.09.98.28
Top 25%66.179.613.56.96
Difference4.3-4.43.6
OffspeedSwing RateContact RateWhiff RateAvg. Inches from Center of Zone
Bottom 25%64.686.19.08.88
Top 25%83.175.420.48.52
Difference18.5-10.711.4

Swing rate is relatively unchanged because all of the pitches end up as strikes. The best-commanded breaking balls usually start in the middle of the zone, causing more swings. But the most important takeaway is that better-commanded pitches result in more swings and misses, and for non-fastballs, a lower contact rate.

You might be thinking, duh, poorly commanded pitches that still end up in the strike zone are probably more down the middle which is why they have a higher contact rate and a lower whiff rate, but if you look at the column that shows the average distance from the center of the zone, that actually isn’t true.

Arizona Diamondbacks’ Zack Greinke pitches during the first inning of a baseball game against the Miami Marlins, Friday, July 26, 2019, in Miami. (AP Photo/Wilfredo Lee)

That means there is a huge difference between a correctly commanded strike and a poorly commanded strike even if they are the same distance from the center of the zone. Presumably, that’s because pitchers are targeting hitters’ weak spots in the zone and using target areas as part of a larger pattern meant to confuse hitters. Without Pitch Intent, it wouldn’t have been possible to see this distinction.

Advanced analytics that confuse command and control fail to properly measure a pitcher’s ability or explain why a hurler is getting certain results. We have always known that not all balls and strikes are equally effective (or ineffective), but now, because of STATS Pitch Intent data, we have more tools to measure the difference in effectiveness for every pitch.