Swing-Mirroring: Chronicling Contact Conformity

Vincent Carchietta-USA TODAY Sports

If you’re anything like me, you care what your friends think of you. I’m not talking about the middle school I’ll-do-whatever-they-do kind of caring; I like to think I’ve outgrown that. But I certainly still want to fit in. I think these feelings are fairly universal among adults, whether the in-group in question is composed of friends, co-workers, or just a collection of peers with whom you happen to share something.

My credence stems from a classical psychological study conducted by Solomon Asch, which spawned an entire category of literature under the “conformity” umbrella. The word has a negative connotation, but as long as it isn’t taken to extremes, conformity is a natural and adaptable human behavior. It’s likely even seen in baseball players (and not just in how they wear the same outfit to all of their games).

In Asch’s study, a subject was placed among a group of confederates, or research assistants posing as subjects themselves. The group was shown a series of “target” lines, each alongside another group of lines, and instructed to find the line in the group most similar to the target. There was always an obvious answer, and the confederates were instructed to never choose it. The majority of subjects agreed with the confederates at least once.

Post-study, the astounded experimenters asked the subjects why they agreed with the confederates. They gave two reasons: they either believed the confederates knew or saw something they didn’t, or the subjects knew the confederates were wrong and just didn’t want to seem out of place.

Last summer at Pitcher List, I applied Asch’s findings to batters. Specifically, defining their teammates as their in-group, I anticipated changes in First-Pitch Swing% (FPS%) based on the previous hitter’s outcome.

Using data from the 2021 season, I narrowed my scope to first pitches because not only is the previous hitter’s result most salient then, but there is also a dearth of pre-pitch information. At the start of an at-bat, a hitter has little to no sequencing information available, and it’s hard to tell which pitch is coming when there is no previous pitch to look back on. As such, hitters are even more likely to turn to the previous result for a clue. This is especially the case earlier in the game, or the first time a particular batter sees a particular pitcher that day. Yet, even if the pitch sequence doesn’t quite reset from a hitter’s first at-bat against the pitcher to their next, the count always does.

And according to StatCast’s zones, the average pitcher’s Zone% is typically very close to 50% (52.7% and 52.0% for 2022 and ’21) on 0-0 counts. For each of 2022 and ’21, this was the second-closest to a coin flip among all counts (49.1% and 49.5% for 1-1s). Essentially, with little to indicate the what and where of the coming 0-0 offering, hitters are especially susceptible to other cues, like their peers’ results.

I first looked at potential changes in FPS% following outcomes that intuitively might encourage more swings: hits. In these situations, I theorized batters would be adhering to the first Asch explanation, believing their predecessors knew something they didn’t — how to get a hit against that pitcher.

Bolstering my theory, FPS% was significantly higher after each kind of hit than after an out. These results proved robust across controls for the number of pre-pitch outs and men on base. With the 2022 season now in the books, I included those numbers below in addition to those from the original study. For context, the overall average first-pitch swing rates for 2022 and ’21 were 31.0% and 30.4%:

FPS% by Hit, Season
Post-Hit? 2021 FPS% 2022 FPS%
No 29.4 30.0
Single 34.6 34.8
Double 35.2 34.9
Triple 40.2 36.7
Home run 31.5 32.5

New for this article’s analysis, I also posited that tougher outs might encourage more first-pitch swings. The almost-hit nature of such batted balls might rub off on the following hitter as well, given that the batter must have done something right and was just a victim of poor luck. I defined tougher outs as those with an xwOBA greater than the average xwOBA for singles for each year (.534 in 2021, .535 in ’22). But even after controlling for number of outs, FPS% was about the same each year after tough outs compared to after easier ones:

FPS% by Field Out, Season
Year Outs When Up Post-Tough Out Post-Easier Out
2021 1 26.9 28.0
2021 2 28.4 29.9
2022 1 29.4 28.9
2022 2 30.5 30.2

Perhaps this is because, for as many hitters as feel encouraged to swing after a tough out, there were some discouraged by a feeling of helplessness at the hands of a good defense. In the latter case, batters may have figured (consciously or not) it would be easier to get on base via a walk than by taking their chances in the field. Both kinds of field outs led to fewer first-pitch swings than the overall average, so maybe it’s just that any kind of field out discourages trying to put the ball in play. But the effect of swinging strikeouts was more puzzling.

I initially put swinging strikeouts in the “intuitively discourage first-pitch swings” category. But the numbers didn’t bear this out:

FPS% by K, Season
Year Outs When Up Post-SwStr Post-Other
2021 1 33.1 30.8
2021 2 32.7 31.1
2022 1 33.4 31.5
2022 2 33.3 31.2

Then, a reason jumped out at me. Maybe the second Asch explanation was rearing its head. Batters were swinging at the first pitch not because they thought their predecessors knew something they didn’t, but because they just didn’t want to seem out of place. If your peers go down swinging, so should you. This was the crux of my second Pitcher List article.

Also new for this piece, I looked at FPS% following called strikeouts, strikeouts on foul tips, double plays, errors (combined with all-safe fielder’s choices), and sac bunts. All in all, the full 2022 results are listed below. They are grouped by the number of outs when up and then by lead runner on base because some of the outcomes I looked at resulted in systematically more (or fewer) outs or runners on base for the next hitter. Additionally, more outs and runners on base typically result in higher first-pitch swing rates, likely due to an increased urgency for the hitter to either to get on base himself or drive the runner home. This explains why the bases-empty post-homer situation has a lower FPS% relative to batting after other types of hits. Keep in mind that there may be some (minimal) overlap across groups of outcomes.

First, the outcomes that typically resulted in a higher FPS%. Note that the “Average” column considers all first-pitch swings, not just those in the higher-FPS% category, and N/As signify either no data or not enough.

Higher FPS% Outcomes
Outs When Up Single Double Triple Home Run K Foul Tip Average
0 34.9 38.2 35.1 31.6 N/A N/A N/A 29.7
1 35.0 34.5 43.2 35.0 33.4 32.1 34.8 31.8
2 34.4 31.6 32.5 30.7 33.3 31.4 34.2 31.5
Lead Runner On Single Double Triple Home Run K Foul Tip Average
Bases Empty N/A N/A N/A 32.5 32.3 30.5 34.0 28.6
1st 33.9 N/A N/A N/A 36.1 34.6 38.5 33.5
2nd 33.9 33.9 N/A N/A 34.0 34.5 33.3 33.4
3rd 39.3 39.0 37.1 N/A 36.0 36.3 30.8 36.6

Triples didn’t outpace the average by much when controlling for lead runner. A good chunk of their FPS-increasing potential comes from the position of the baserunner rather than Asch conformity, but I included them because in 2021 they easily bested the average for lead runner on third (by 3.7%). Additionally, I wasn’t shocked that foul-tip strikeouts ended up in this category given their similarity to swinging strikeouts, but called strikeouts? While it’s worth noting that FPS% after called strikeouts was not significantly different from the average FPS% with one or more outs, we can at least explore a possible explanation. If I try hard enough, I can recall from my little league days a sentiment that swinging strikeouts are preferable to called strikeouts; at least you got the bat off your shoulder. Maybe the subsequent hitter swings at the first pitch just to avoid that same fate. Further, in addition to the second Asch explanation, this reasoning may contribute to the increase in FPS% after swinging strikeouts as well: swing early in the count so you don’t have to flail at a waste pitch later.

Next, the outcomes that resulted in a consistently lower FPS%:

Lower FPS% Outcomes
Outs When Up GIDP Post-Tough Out Post-Easier Out Average
0 N/A N/A N/A 29.7
1 N/A 29.4 28.9 31.8
2 27.4 30.5 30.2 31.5
Lead Runner On GIDP Post-Tough Out Post-Easier Out Average
Bases Empty 27.7 27.8 27.7 28.6
1st N/A 32.2 33.4 33.5
2nd N/A 35.7 32.6 33.4
3rd 25.9 39.8 35.1 36.6

Most post-field out situations came with no runners on because they excluded scenarios in which the previous batter got a hit. Post-tough out situations actually see significantly higher FPS rates with runners in scoring position, but because these occur less frequently than the bases empty state, they are nullified in the overall average. If I had to speculate, my guess is that even hard outs are encouraged with RISP because there is a chance to drive in runs via a sac fly or a ground out to the right side, whereas soft outs (especially fly outs) don’t get the job done as well.

In addition to field outs, groundball double plays also tended to lower FPS%. I left out other kinds of double plays because there were only 202 and 160 in 2021 and ’22, respectively, and the results were inconsistent. True momentum-stoppers, urgency is definitely lower after twin-killings. But even controlling for the number of outs and runners on base, FPS% is significantly lower following a double play. Maybe it’s more about the change in urgency than the absolute level of urgency. The behavioral economics theory of reference dependence states that people make evaluations based on the status quo; before the double play, the status quo was likely a run-scoring threat, making the current state of things seem even less urgent. But GIDPs result in two outs and the bases empty or two outs and a runner on third; FPS% was even lower after GIDPs and with a runner on third for both 2021 and ’22. In these situations, maybe it’s the crushed hope of a major rally (i.e., after having runners on first and second with no outs) that diminishes the FPS%.

Lastly, the outcomes that produced inconsistent results, either across the controls or across seasons:

Inconsistent FPS% Outcomes
Outs When Up Sac Bunt Error/FC All Safe Walk HBP Average
0 N/A 35.8 31.9 34.6 28.8
1 38.2 37.1 33.4 38.9 31.1
2 25.8 30.6 31.8 32.8 31.4
Lead Runner On Sac Bunt Error/FC All Safe Walk HBP Average
Bases Empty N/A N/A N/A N/A 27.6
1st N/A 35.2 31.8 33.0 33.1
2nd 36.6 33.3 32.2 36.6 33.7
3rd 38.7 36.6 34.7 41.0 36.5

In 2021, there were a lot more two-out post-bunt situations, which made bunts look worse in the lead runner control. These situations are preempted by a sac bunt with one out, which is typically not a good strategy unless a pitcher is at the plate. With the universal DH in play for 2022, one-out sac bunts all but vanished. Next, I expected walks and HBPs to result in a significantly lower FPS%. I reasoned that after witnessing a pitcher’s wildness, the next batter would be more inclined to take the first pitch. Yet, this was only true for walks when sorting by runners on base. This is likely because sorting by runners on base, we leave out bases-empty situations (since these rarely come up after walks and HBPs). Bases-empty situations produce the lowest FPS%, and their presence in the overall averages for the outs-when-up control made it seem like post-walk and post-HBP led to above-average FPS rates.

Like walks, HBPs saw their edge over the averages decrease or evaporate for the lead runner control in both 2022 and ’21. However, in 2022 they resulted in higher FPS rates than walks in all conditions; in 2021, the results were more mixed, but my explanation for higher FPS rates for HBPs would be that they could be downplayed as a one-off mistake, whereas walks require more consistent wildness.

So what does this all mean? Aside from Asch conformity, there are many other psychological forces at play in baseball. Whether it’s momentum, urgency, or reference dependence, that’s good news for me because I’ll have plenty more to write about. It’s also important to note that while Asch conformity is what inspired this line of research, and while the same outcomes that generated higher or lower FPS rates typically did so across both 2021 and ’22, other forces likely play a role in determining the effect the previous batter’s result has on the current hitter. There may even be more factors at play, such as individual differences (stay tuned for a piece on that) not only in Asch-susceptibility but also general first-pitch swing tendency, batter-pitcher familiarity, and a pitcher’s strategy more broadly against the hitter in question.

Regardless of the reasons behind this phenomenon, pitchers can use their knowledge of it to catch hitters off-guard. Maybe they’ll feel more comfortable nibbling at the corners with men on base (and especially after a hit); although pitchers may want to avoid the walk in these situations, the hitters will be swing-happy. Batters, in turn, can be aware of their increased (or decreased) tendency to swing at the first pitch based on the circumstance and make a more informed decision about whether they should sit on or jump at the first pitch. In the long run, perhaps these trains of thought will become another point of contention in the battle of wits between batter and pitcher.

Alex is a FanGraphs contributor. His work has also appeared at Pinstripe Alley, Pitcher List, and Sports Info Solutions. He is especially interested in how and why players make decisions, something he struggles with in daily life. You can find him on Twitter @Mind_OverBatter.

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1 year ago

Good stuff!

Could also be interesting to look at change in a batter’s FSP rate as they move around in the order, or to a different team, and have a more/less free swinging guy ahead of them. If such is significant, does it mean batting order matters again?