/ baseball

What kind of pitches are harder or easier to bunt?

This article was originally posted in December 3, 2012 in my old blog and has been slightly modified by yours truly to correct gramatical and spelling errors and make it more readable.

A sacrifice bunt has conventionally been featured on sabermetric blogosphere for its relative lack of importance to ballgames and often criticized when it is dreadfully abused by a baseball manager on a given game. Even a sabermetric newcomer would have already read at least a couple of articles on the validity of bunt usage citing run expectancy or that kind of stuff elsewhere. The use of a sacrifice bunt has been decidedly one of the most controversial topics among baseball fans throughout the past couple of decades but there have been few articles published on it from the perspective of relative difficulties of executing successful bunts against different types of pitches. What I'm going to write today is focused on pitcher-batter confrontation on bunt attempts during the at-bats.

Let me first show some data before cutting to the chase since it is one of areas where I was a bit interested in. Remember that most hitters feel more comfortable facing opposite-handed pitchers and vice versa, usually known as platoon splits. Managers construct their team's lineup by paying some attention to an opposing starter's handedness on that day, and when the game is "on" and they are pressed for calling a pinch hitter or new reliever, some consideration of a platoon advantage always resides at some place in their mind, even though they might treat all players as having identical splits and ignoring uniqueness of players, or stick excessively to a result of recent match-ups and avert any warnings uttered by Regression God. But is there any such predisposition when it comes to a sacrifice bunt, and if that's the case, how much?

From 1993 to 2011, I took all events I define as a sacrifice bunt attempt. My definition is all situations where

  • a runner on 1st and less than 2 outs
  • a runner on 2nd and less than 2 outs
  • runners on 1st and 2nd and less than 2 outs.

Out of those situations, I regard a successful bunt as

  • 1st runner advanced to 2nd or further
  • 2nd runner advanced to 3rd or further
  • both 1st and 2nd runners advanced to at least one base ahead and no more than one out was recorded on that event

All the other outcomes, along with any pitches batters attempted to bunt but missed (i.e. swinging strike, foul tip, and foul bunt on a bunt attempt) are considered to be a failed bunt. I should also point out that my bunt attempt definition is based on pitch-by-pitch, so if a bunter tried to conduct a sacrifice bunt but fouled off two straight pitches, and finally succeeded in sending the runner on 0-2 count, he is credit for one success and debuted for two failures. However, for pitches that a bunter squared to try to do a bunt but a ball was off the zone and called ball, I don't include them in my analysis since I can't tell it in my datasets from generic non-bunt approaches. Same is true of a called strike with squaring bunt attempt but drawing his stick back during the pitch flight.

Then, for each batter I crunched bunt success rate against right- and left-handed pitchers respectively, and calculated the differences weighted by a lesser side of his bunt attempts. OK, have you caught up with so far? Let's check out the results.

According to my research, right-handed hitters successfully sacrificed on a bunt against righties 47.7% of the time whereas facing lefties their success rate ascends to 48.9%, only 1.2% difference inspected. How about left-handed hitters? Their success rate against righties and lefties is 46.6% and 46.4% respectively, only 0.1% difference (rounding aside). So basically, when it comes to a sacrifice bunt, it has little or nothing to do with a platoon handedness advantage.

And let me point out one more before going into details . How much is a successful sacrifice bunt affected by an opposing pitcher's batted-ball tendency? I computed GB% (the rate of the number of ground-balls on the total number of batted-balls a pitcher allowed in play) for each pitcher/year going through 1993, exclusive to pitchers who saw at least 200 balls in play in the particular year (all bunt events are excluded). Then, I took maximum and minimum 15% out of the data set and defined them as GB and FB groups respectively. For your information, mean ground-ball rate for those two groups are 38.2% and 53.4% respectively, while among all pitchers, it's 45.4%. Bunters successfully bunted 44.2% of the time against GB group while against FB group, they succeeded in bunting 47.6% of the time, the 3.4% difference. Is this a result from any difference of quality of bunters on each group? Bunters against whom pitchers on GB group pitched were able to do a successful bunt against the rest of group pitchers (i.e. belong neither to GB nor FB group above) 47.1% of the time. How about bunters that FB group of pitchers faced? Their success rate against the neutral groups were also 47.1%. So basically, there are no bias on the quality of bunters each batted-ball group of pitchers faced. But how about the quality of pitchers within each group? Pitchers on GB group allowed .328 wOBA and those who belong to the other end of the spectrum allowed .341 wOBA. Definitely and as you would expect, GB pitchers on the whole were better hurlers in terms of total performance in confrontation. So I took a brute force approach, tearing the poorest performers off the group of fly-ballers until their performance as a group jibes with the better one. However, bunters still take a bit more pains to bunt against worm burners, as only 0.4 percentages of points got narrowed by adjusting the quality. I'll take a deeper look at this later, but keep this in mind for the time being.

So with that knowledge in mind, let's ask always-awesome PITCHf/x.

Actually, here's my first post using PITCHf/x, so let me digress a bit to describe the underpinning of my resources. I use complete dataset from 2008 on, with pre- and post-season and All-Star games are omitted but called games are kept in. I also do some park-adjustments on location, movement, and velocity for all pitches on all stadiums where PITCHf/x cameras are implemented. As to each specific pitch type, I don't touch up any re-classification at this time, but would likely do in the future. Strike-zone definition is based on my own definition and computation, where the borderline is set at 50% probability of a called strike. This accords very well with Mike Fast's research horizontally, but differs slightly on vertical zone borderline, as I make use of raw sz_top and sz_bot parameters as well as a batter's own height. However, the reason of the slight discord is much more likely to originate from the fact that I plug more recent years (strike zone is actually expanding a little, especially on the bottom edge, in recent years) than the input of extra parameters fed to the equation. And even the difference is very tiny, about only one inch wider to the bottom if all of three parameters are set to average.

Now, let's finally get into the results...

Pitch Type

First of all, what kind of pitch types are easier or harder to bunt? From here to the rest of this article I classify all pitches as fastballs (four-seamers, two-seamers, sinkers, and cutters), breaking balls (sliders, curveballs, and knuckle-curves), or changeups (change-ups and splitters) and analyze it through these three partitioned categories.

Here's the result.

Success RateN
Breaking Balls35.9%2,648

Fastballs are considered to be the easiest pitch to bunt and on the other end of the spectrum lie breaking balls. As to platoon effects on pitch types, almost no difference can be detected other than fastballs, where 2 to 3 percentages of gap is detected. Another note is right-handed pitchers' breaking balls to right-handed hitters, where the rate drops down to 33.7% on 1,413 pitches.

You may wonder whether this difference is caused by the potential bias in batting counts, but when I tried to factor it into the calculation the effect still held true mostly, and that's why I display the combined figure to improve its visibility.

Investigating inside each specific bin, four-seamers are easier to bunt than other pitches in the bin (around a couple of percentage points; this is one reason bunting against GB pitchers is tougher as you've seen first in this article).



When an opposing batter looks like trying to do a sac bunt, where in the zone should a pitcher throw? The above graph shows the bunt success rate sees its pinnacle down the middle horizontally whereas the further pitches are away from a hitter's body the harder they are to bunt, and in terms of vertical location the ball thrown closer to the ground induces more failed bunts. Actually, this trend holds true whether what types of pitches are thrown and is also irrespective of an opposing pitcher's handedness, both vertically and horizontally.


How about velocity? Conventional wisdom says that the faster the fastball is thrown, the harder to bunt (or hit, anyway). Does this hold true?


This theory is supported by the graph above. Successful bunt rate drops down when the fastball is thrown with faster speed, though you have to caution yourself that there are great uncertainty at the speed of 95 mph or more because such fast pitches are not often seen in that range. Breaking balls are relatively constant with respect to relationship between velocity and bunt success rate. On changeups, you can find a bit awkward dip around early-80 mph. I'll explain more on this later.



As to the vertical movement, it is generally harder to bunt if the ball is thrown with more downward movement. In fastballs, you can see the highest success rate around +9 inches, which is the point that most four-seam fastballs are thrown. Change-ups also see its peak on a point where most pitches of that ilk are thrown (check out density estimate on the left side of the plot). Breaking balls, however, looks consistently declining, being irrelevant of the distribution of pitches thrown. This is caused by the fact that lots of pitches on the upper part of the distribution that are classified sliders are actually cutters, and remember that cutters are much easier to bunt than breaking balls.

For the obvious reason, I restricted my sample to only righty-vs-righty match-up for horizontal movement. However, you're not able to see much of a meaningful result as my sample consistes of only 7,986 pitches and once you sliced it to three separated pitch category and given that on top of that there are a wide variety of values (from -10 to 10 mostly) seen in the range of pitch movement, you cannot take a look at the graph to come to any conclusion with decent certainty. However, doing nothing is always worse than attempting to detect some patterns even from less helpful dataset. So what kind of patterns can you see?


Like the plot of the vertical movement, the success rate is highest at the point that most pitches are thrown within each specific bin other than breaking balls, which see its trough instead. As I stated in the last paragraph I extracted only right-handed hitters and pitchers, though if the handedness of hitters are deregulated the similar tendency was detected. I doubt most pitchers, if any, can control the movement of their pitches at their disposal within the range of a few inches (at least until some comprehensive analyses are conducted and published), so anyway you would be better off just separating on each particular pitch types level rather than pursing too far into movement values.

And what does bring about the blip in change-ups you see in velocity graph? I rambled through a screen along with some codes and noticed that in early-80s mph, change-ups are more likely to be thrown in ahead count, with slightly more vertical movement (to the downward direction, of course) and slightly down in the zone. I'm never going to say that these are the sole factors driving the result in that way, but suspect that that pitchers throw more winning shots in that velocity range leads to the phenomenon. Also, in around 90s mph, you would see more misclassified pitches and it may raise the success rate a bit.

One more thing

We all know that a pitcher's job as a batter when a runner is on is mostly send the runner farther to set the table for the top of the order if the number of outs is less than two. In those situations, pitchers as batters are likely to choose to sacrifice themselves to send the runner through bunt attempts. So are those bunters able to move the runner successfully?


Actually, Pitchers are made of only starters; relievers are included in Hitters. But who cares? If a reliever somehow comes to the plate with a runner(s) is already on the base, managers most often decide to pull him down by calling a pinch-hitter... Ah, okay, okay, I admit my dataset was not able to separate relievers from other substitutes... Now, check out the table above and you can notice an interesting finding; pitchers as batters are indefensibly awful when they are fed with breaking balls. I suspect pitchers in general, even if they do hard-working enough to practice bunting, practice it against only fastballs and not breaking balls. So why not pick on them by sliders-curves-and-sliders? And for those pedants out there, this is hugely statistically significant (after all, Pitchers account for more than one third of the total records). Rather, hitting count is a bigger causative agent for driving this effect, as there would be reasons behind it that opposing pitchers bother to throw breaking balls to the pitchers at the plate in the first place. The data bear it out well that pitchers as batters are more likely to be fed with breaking balls in behind count than in early and/or ahead count, but even after accounting for the effect, Pitchers still don't see themselves get well, to the tune of 7.5 percentages of points off Hitters.


We dived into PITCHf/x and checked through whether there are any patterns to make batters incompetent to do a sacrifice bunt. We got to know that pitchers can control lots of factors at their pleasure that have an impact on the outcome of whether bunts come to a success or failure. Breaking balls are tougher to bunt than fastballs, and change-ups lie in the middle. Locating your pitches at your disposal do also have an influence; pitches thrown down and away are harder to bunt. Velocity also has a voice but it is actually a small voice. Movement is a bit tricky but basically more downward movement tilts the odds a bit to make it happen to your favor. Finally, pitchers as batters are desperate bunters when being attacked with breaking balls, so if you have an ability to earn strikes by breaking balls, there is little reason to turn only to your fastballs, which so many pitchers choose against pitchers as bunters in the current ballgames.