Are Hitters Who Swing At More Strikes and Fewer Balls Actually Better?

If you’ve read any of my articles of late, you would know that I am currently fixated on plate discipline. My piece on Jarred Kelenic sparked an article on take value and hitter approach. After that, a discovery that Darin Ruf is succeeding with one of the lowest swing rates in baseball despite not having phenomenal plate discipline on the surface inspired research into zone-swing differential and what it may tell us about a hitter.

Under the plate discipline section of player pages and on our leaderboards, we list both O-Swing% and Z-Swing%. On a handful of occasions, though, writers here have used zone-swing differential. Chet Gutwein defined this stat as D-Swing% in his piece about the NL West, and Justin Choi wrote about it in an article on the Blue Jays’ aggressiveness in early counts. In my most recent piece on Ruf, I cited zone-swing differential to conclude that while his overall swing rate is low, his discipline might not actually be that good, as he’s still swinging at a fair amount of pitches outside the zone, which you can see when you look at his below-average D-Swing rate.

The idea behind D-Swing% is simple: Hitters should be better when they swing at strikes and take balls. This isn’t the only way to succeed at the plate, but you would think that better hitters would have higher D-Swing rates on average. There were a couple comments about D-Swing rate on my Ruf piece, and that inspired me to look into it further. Is this a stat that tells us more about hitters than what we already have with the standalone O-Swing% and Z-Swing% stats?

This exact question was actually explored on the FanGraphs community blog back in 2017, where user Dominikk85 broke hitters into top- and bottom-30 groups by wRC+, ISO, OBP, and BABIP to see if O-Swing%, Z-Swing%, or what they referred to as Z-O-Swing% had the biggest impact in explaining the difference between the groups. They found that being more aggressive in the strike zone “helps the power but seems to slightly hurt the OBP,” but overall, they saw an advantage in using D-Swing% over the individual components.

I ran a similar study, but I wanted to control for more variables — zone rate and contact rate — to isolate the effect of D-Swing%, as many plate discipline metrics are interrelated. You may swing less at pitches in the strike zone if you are seeing more pitches outside, or you may choose to swing at pitches in the strike zone with which you can make contact (preferably hard), which may lower your Z-Swing% but raise your Z-Contact%. Without at least attempting to adjust for some of these variables, we may be missing out on conscious hitter tendencies that may be more the result of the pitches that they are seeing rather than their inherent swing choices.

That doesn’t make this method inherently better, though. While the relationships do improve when adjusting for these factors, we may be capturing the relationship between zone rate and, say, walk rate, rather than exactly what we’re looking for; this would be an example of overfitting. For complete transparency, I’ll also provide the non-adjusted figures to avoid this. Adjusting may be better, but there may be some important insights to be gleaned from the raw relationships themselves. For all models, I just used a linear relationship of 2021 hitters with at least 250 plate appearances.

Plate Discipline

First, since D-Swing% is theoretically a plate discipline metric, we should evaluate how it relates to top-line strikeout and walk numbers so we can see how the metric performs relative to O-Swing% and Z-Swing% in explaining strikeout rate, walk rate, and walk-to-strikeout ratio for hitters within our sample. “Controlled R-squared” refers to the r-squared value after adjusting for zone rate and contact rate.

First, here’s strikeout rate:

As you can see, all three of these swing metrics don’t correlate well to strikeout rate on their own; they need to be contextualized with zone rate and contact rate to better explain the differences. This does suggest, though, that strikeout rate is less dependent on a hitter’s swing decisions as it is on how often they see pitches in the zone and make contact when swinging, which makes sense. Otherwise, between the three swing decision-metrics, there’s not a ton of difference in the effectiveness of explaining strikeout rate for hitters.

That’s not the case with walk rate:

O-Swing% reigns supreme, and that also makes sense: Hitters who swing less frequently at pitches outside the strike zone will walk more. It also makes sense that Z-Swing%, and consequently D-Swing%, isn’t as useful in explaining walk rate. It doesn’t matter what you do on your pitches inside the zone; if you don’t swing, those will generally be called strikes, and if you do, these pitches could be put in play, fouled off, or whiffed at. But if you don’t put the ball in play, you will find yourself in a worse count (except for fouls in two-strike counts) than you were previously, which does explain the slight negative relationship between Z-Swing% and walk rate; the association is pretty muted otherwise.

When you put it all together and consider walk-to-strikeout ratio, we once again find O-Swing% as the best metric for these types of discipline numbers:

The power of O-Swing% is why people love to cite chase rate for hitters. It is true that hitters who swing at fewer pitches outside the strike zone will walk more, and walking is one of the best things a hitter can do. With so much potential variability when putting a ball in play, a walk is one of the most consistently good outcomes for a batter when they’re at the plate. Barry Bonds hit a ton of homers, but it was his absurd walk rates (thanks to pitchers being afraid of him) that allowed for consistently great performance. When you get on base more than 60% of the time, it’s impossible to be a bad hitter. That’s why chase rate, and how it relates to walk rate, becomes really important.

Quality of Contact

But chase rate isn’t the be-all, end-all for hitters. As I found in my article on Kelenic, a hitter’s production is best modeled by how much success they have when swinging, and the top hitters in baseball differentiate themselves by doing a lot of damage on their swings. Can a metric like D-Swing% explain exactly how much damage a hitter does when swinging, which could potentially explain more about a hitter’s production overall?

To find out, I considered two quality of contact metrics, ISO and xwOBA on contact (aka xwOBAcon). There’s not too big of a difference between the two: ISO is production-based, and xwOBAcon captures the underlying exit velocity and launch angle data. There’s unsurprisingly a strong relationship between the two, but I used both to see if we might be missing something in the top-line power numbers that xwOBAcon would account for.

First, here are the relationships with ISO:

There’s no inherent relationship between O-Swing% and ISO, but after adjusting for both zone rate and contact rate, the former becomes a statistically significant predictor of the latter, with higher rates of chasing associated with reduced ISOs. What’s interesting here, though, is that adjusting for zone rate and contact rate doesn’t help the relationship between Z-Swing% and ISO as much. Of the three, D-Swing% is the most useful metric in explaining quality of contact after adjusting for zone and contact rates.

That also holds for xwOBAcon:

When we use the underlying data, all three metrics perform better. Again, D-Swing% looks like the best metric to use after adjusting for zone and contact rates, but it doesn’t stand out as that much better than the other two on the whole.

Overall Hitter Results

How can we generalize this? Can we extrapolate beyond discipline and quality of contact alone to answer the original question? Do better hitters have a higher zone-swing differential?

The answer might be yes. Consider the relationship between D-Swing% and wOBA. The former explains 16.2% of the variation in the latter with no adjustments, performing much better at explaining hitter production than O-Swing% and Z-Swing% alone. All three metrics do perform better after adjusting, but again, the model using D-Swing% has the strongest association. And it does appear that hitters who have greater zone-swing differentials perform better:

The same holds true when looking at hitter xwOBA, though it should be noted that O-Swing% is just as good as D-Swing% after making the zone and contact adjustments:

But while it seems like D-Swing% may be helpful as a catch-all swing-decision metric to describe hitter performance, it does not blow existing O- and Z-Swing rates out of the water.

With that in mind, it is fascinating comparing O-Swing% to Z-Swing%. We can see that avoiding swings outside of the strike zone appears to be much more important than always swinging when in the zone. This is in line with what I found when researching Kelenic: If the best hitters in baseball do the most damage when swinging, they better swing at pitches not only in the strike zone, but also in their own personal wheelhouse. Avoiding chases is a lot more important than always swinging when the ball is in the zone.

As for the usefulness of D-Swing% alone, more research needs to be done to see if it’s stickier year-to-year than the other swing metrics and if it does a better job at predicting future production rather than just associating with in-season marks. As long as my fixation on plate discipline doesn’t wane (why would it?), this might be something to come back to in the offseason. For now, though, it does appear that this metric may have some standalone value relative to those that are already published. But the conclusion is simple: Hitters should always swing at fewer balls. Whether they should swing at more strikes depends on the type of player that they are, and also on how pitchers approach them.