Let’s Take a Closer Look at Hitter Swing Decisions

Swing decisions are generally evaluated with limited nuance. We consider whether the pitch was in the strike zone (as defined by your data provider of choice) and whether the batter swung. Over the course of hundreds or thousands of pitches, this provides an easy-to-comprehend method of effectively evaluating a player’s approach. With a sufficient sample, these binary classifications give us insight into how players approach their plate appearances relative to their peers, which hitters are better at discerning the strike zone and which are more aggressive.

I have a bone to pick, though: there is often no differentiation between pitches that just miss the defined strike zone versus those that miss by multiple feet, or pitches that just nick the strike zone as opposed to pitches right down the middle. A lot of swing decision analysis is done in the binary, but as many analysts have shown, looking at the gradations in the strike zone can be revealing. Granted, this distinction lacks meaning over many pitches; selective hitters with elite batting eyes will separate from their less fastidious peers with respect to chase rate over time. But in smaller samples, the lack of distinction between pitches and their proximity to the strike zone makes judging a player’s swing decisions difficult.

One method we can use is to group pitches by their probability of being called a strike. Similar to how pitches are evaluated for the purpose of studying catcher framing, I created a general additive model for gauging the probability that a given pitch would be called a strike. My model was trivial (relative to the research I linked above) in that I just considered pitch location and pitch movement; for the purpose of this exercise, I thought that would be enough to get the idea across. The model was trained on 80% of pitches called a ball or strike from the 2020 season, with the remaining 20% used as the test set. For the test set, the model was about 92.5% accurate, in that it correctly predicted whether a pitch was called strike 92.5% of the time.

I applied the model to all pitches from the 2019 and ’20 regular seasons, which yielded the probability of a called strike on every pitch. Pitches with higher probabilities of being a called strike if taken are toward the heart of the zone. Pitches at the edges of the zone have anywhere from a 40–60% chance of being called a strike. And pitches with expected probabilities closer to zero are nowhere near the strike zone.

I binned every pitch in increments of 10% of called strike probability. The following represents the swing rates in each of those bins:

Swing Rate by Called Strike Probability
CS Prob at Least (%) CS Prob at Most (%) Swing%
0 10 22.7
10 20 43.9
20 30 47.3
30 40 49.0
40 50 50.9
50 60 53.4
60 70 55.1
70 80 56.9
80 90 59.9
90 100 70.3
SOURCE: Baseball Savant
Data From 2019-20 Seasons

As one would imagine, the league as a whole swings at pitches that have higher called strike probabilities; the closer the pitch is to the heart of the zone, the higher that probability. Break those probabilities down even further, and you can see that the chance of a swing increases steadily with called strike probability.

Swing rates increase rapidly as the called strike probability approaches 0 and 100%. For the more competitive pitches, the changes in swing rate are much smaller. Intuitively, you would expect this relationship to be linear throughout the probability interval; for every 1% increase in called strike probability, the swing rate would also increase by some corresponding percent described by the slope of a line regardless of where you are along this interval. This is not the case.

My hunch is that once a pitch reaches a certain threshold of competitiveness (in terms of challenging the hitter to swing), the swing decision is not as tethered to the chance of the pitch being called a strike. Instead, the choice depends on the pitch type and what the hitter is guessing or picks up out of the pitcher’s hand. Addressing the rapid increase in swing rate on the lower end of the spectrum, I would imagine that many of these pitches are thrown in advantageous counts from the perspective of the pitcher — two-strike counts. While the lack of stigma surrounding strikeouts has been talked about ad nauseam in baseball circles, hitters still do not want to strike out. So if these less competitive pitches are often being thrown with two strikes, the swing rate increases are going to be more sensitive to any marginal change in called strike probability. Break it down by count, and you can see that that’s the case:

For the sharp increase on the higher end of the range, my theory is the same as the other end of the spectrum: Pitches approaching a 100% called strike probability are so enticing to swing at that batters will disregard the count to attack them. Murkier pitches will not really be swung at in 2–0, 3–0 or 3–1 counts, but if the pitch is close to an automatic strike, it must be toward the heart of the plate; a batter who has the green light will want to swing.

For context, league-wide swing rates have oscillated between 45–47% over the past decade. Swing rates on pitches with a called strike probability between 40–60% generally fell in this range in 2019 and ’20. It’s the extreme ends of the spectrum where hitter behavior changes most rapidly. We also saw that the count has a significant effect on the swing rates for any given pitch, especially those that were most and least competitive. So, we know the general league-wide trends and we understand why this is a more nuanced method in evaluating swing decisions. What about at the player level? I found a couple of interesting quirks. When you look at the players who are most aggressive on the pitches that are the most advantageous to swing at (those with a called strike probability of at least 90%), you get a mix of players who we think of as having good plate discipline and those who are more free swingers:

Most Aggressive Swingers on Most Enticing Pitches
Player 0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100
Ozzie Albies 27.8 49.1 54.2 58.6 61.8 67.8 76.5 62.1 71.1 83.4
Jorge Alfaro 44.3 62.4 72.6 69.2 70.2 66.7 77.8 74.6 68.4 82.8
Jay Bruce 28.3 57.1 73 68.2 59.5 57.5 64.7 54 75.4 82.9
Khris Davis 19.8 48 43.7 58.8 52.5 66.7 56.9 68.2 71.7 83.9
Freddie Freeman 20.2 45.3 49.3 63.7 62.4 59.1 65.5 66.9 71.1 84.1
Brandon Lowe 19.7 40.3 54 56.5 54.7 57.1 56.5 64.8 63.7 81.8
Jeff McNeill 27.5 67.3 62.3 69 77.5 80.9 69 78.9 76.2 87
Austin Reilly 28.1 54.7 59.6 61.4 67.6 62.7 77.1 73.1 82.1 81.2
Corey Seager 21.9 45.7 56.4 45.5 50.7 57.4 62.8 74.5 71 83.4
Luke Voit 19 52.6 49.5 48.3 56.5 49.3 56.3 67.3 67.3 81.2
League 22.7 43.9 47.3 49 50.9 53.4 55.1 56.9 59.9 70.3
SOURCE: Baseball Savant
Data from 2019-20, values equate to Swing%

Freddie Freeman, Luke Voit, Brandon Lowe, and Corey Seager are all examples of players we generally understand as having good plate discipline. They lay off pitches that have very little chance of resulting in a called strike and attack pitches that can result in positive outcomes on contact. This list also includes Jeff McNeil, Jay Bruce, and Jorge Alfaro, all of whom swing at pitchers at rates higher than league average no matter the location. This type of strategy can work for a player like McNeill, who has displayed throughout his career he is among the league’s best at making contact. For players like Bruce and Alfaro, this is a recipe for either falling out of the league (in the case of Bruce) or finding more time on the bench as time goes on (in the case of Alfaro). On the other end of the spectrum, the analysis is more cut and dry:

Most Passive Swingers on Most Enticing Pitches
Player 0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100
Harrison Bader 19.3 34.7 45 53.8 49.1 53.2 59.7 56.6 53.3 59.7
David Fletcher 18.7 32.4 32.6 35.3 29.6 41.2 45 42.6 43.8 50.7
Greg Garcia 12.4 31.5 25.5 27.1 37.5 24 51.7 34.7 39.6 57.5
Brett Gardner 16.3 32.7 25.4 32.4 35.4 43 41.9 50 52.5 57.8
Mitch Garver 12.5 37.2 37.3 19.1 32.5 46.4 32.5 32.8 44.7 58
Yasmani Grandal 15.1 25.4 37.7 37.1 45.1 44.8 50.9 43.7 48.2 59.1
Tommy La Stella 15.2 31.9 53.7 33.3 41.1 38.9 50 58.2 51.1 59
Eric Sogard 16.6 31.2 27.9 45.9 36.2 49.2 51.5 49.3 49 56.7
Josh VanMeter 17.3 30.2 38.6 64.5 37 29.4 44.2 53.8 61.5 59.6
Daniel Vogelbach 14.3 34.8 28.3 36.6 31.1 38.4 39 36.7 44.7 53.7
League 22.7 43.9 47.3 49 50.9 53.4 55.1 56.9 59.9 70.3
SOURCE: Baseball Savant
Data from 2019-20, values equate to Swing%

Here we have a list of players who we consider either disciplined or passive. These players do a good job of avoiding swinging at bad pitches, but it seems to be more of a product of just not swinging at all. It could also mean that these players are zeroing in on “their pitches to hit,” and can lead to very good seasons (see: Yasmani Grandal, Mitch Garver, and until this season David Fletcher, Brett Gardner, and Eric Sogard) but passing up good pitches can be problematic without either elite power or contact ability (see: Greg Garcia, Harrison Bader before 2021, and Josh VanMeter). This extreme passivity is a fine line to walk; as you can see after great combined 2019-20 seasons, Fletcher, Gardner, and Sogard have fallen off this season after posting very good lines previously. Fletcher especially is one of the best at putting the bat-head on the baseball, but his passivity may be catching up to him as the league has collected more data on his swing patterns.

Finally, here were the most aggressive and passive swingers on pitches with very little chance of becoming a strike:

Most and Least Aggressive Hitters on Likely Called Balls
Player 0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90-100
Jorge Alfaro 44.3 62.4 72.6 69.2 70.2 66.7 77.8 74.6 68.4 82.8
Hanser Alberto 42.4 60.4 77.6 67.3 71.8 64.9 71.9 67.1 68.9 76.6
José Iglesias 37.8 54.3 62.5 63.8 52.9 67.6 58.8 61.6 58.3 67.8
Kevin Pillar 37.2 63.7 67.8 69.4 69.2 58.1 58 65.6 72.2 73.4
Tim Anderson 36.7 62.2 66.7 65.3 57.1 67.6 62.8 64.5 73.5 77.4
Javier Báez 36 58.9 57.5 66.7 61.8 72.6 70.7 64.8 72.5 74.2
League 22.7 43.9 47.3 49 50.9 53.4 55.1 56.9 59.9 70.3
Juan Soto 11.2 29.9 38.1 32.4 37.1 43.2 47.2 55.3 54.4 71
Carlos Santana 10.8 34.9 30.8 35.4 39.1 42.9 51.3 39.8 55.5 68.2
Alex Bregman 10.7 28.6 25.7 38.6 39.6 34.5 42.4 42 43.1 61.9
Andrew McCutchen 10.7 25 28.9 30.2 45.6 38.9 41.3 48.4 43.9 61.5
Cavan Biggio 9.7 22.6 23.7 37.2 26.1 29.8 44.6 37.3 43.6 65.3
Tommy Pham 9.6 31.3 40.4 37.7 31.1 41.2 58 39.8 58.4 63.7
SOURCE: Baseball Savant
Data from 2019-20, values equate to Swing%

Unsurprisingly, batters who avoid swinging at the worst pitches tend to post good results. The other end is a bit of a mixed bag. Tim Anderson has gotten away with what we would consider poor swing decisions because of his demonstrated ability to post high-end BABIPs the past few years, a combination of hitting the ball at angles that result in singles and his foot speed. Javier Báez has posted excellent lines (2020 notwithstanding) by slugging his way to success. Without outlier skills, this sort of approach leads to lackluster performance. Before 2020, José Iglesias was not a good hitter in the majors. Kevin Pillar and Hanser Alberto have mostly posted middling results, and I talked about Alfaro’s issues above.

There is not a one-size-fits-all method of approaching plate appearances. A player’s ability to make contact and his power are the driving forces behind how often he should swing and which pitches he should choose to offer at. This conclusion is nothing revelatory but distinguishing swing decisions based on its chance of being a strike if taken gives additional insight into certain players’ plate discipline profiles. Freddie Freeman or Juan Soto, how swing clearly can track the ball very well and we know they have great discipline. But their plate discipline is different than a player like Yasmani Grandal, who has also displayed discipline throughout his career, though that discipline manifests itself in a much more passive approach. When parsing swing decisions by the quality of a pitch on a granular level, players can get by either through aggression or selectivity. This also shows that free-swingers are free-swingers, no matter the pitch. Baseball players and their skills contain multitudes. When we deal with samples in terms of pitches faced, it helps to further parse the information at hand to get a better understanding of how players struggle or perform well.





Carmen is a part-time contributor to FanGraphs. An engineer by education and trade, he spends too much of his free time thinking about baseball.

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tz
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Some good stuff here. I noticed that the top bunch in the last chart correlated super close to the leader list for lowest BB/K ratio – both high-K% guys like Alfaro and Baez and low-K% guys like Iglesias and Alberto.

I’d love to see a stat for individual hitters showing the correlation between their swing % and the strike likelihood measure you have here – in particular for situations with a two strike count.