The Essay FOR the Sacrifice Bunt

There are many arguments against the sacrifice bunt, by many sabermetricians and sports writers, all with the purpose of retiring its practice in baseball. The three main reasons not to bunt are that it gives away an out (out of only 27), the rate of scoring goes down (based on ERT by Tango), and that most bunters are unsuccessful.

For my argument, I will establish a more romantic approach and one I haven’t seen across the world of sabermetrics. With this approach, I will land on a conclusion that supports the sacrifice bunt and even speaks to the expansion of its practice.

Bunters can be successful

First, I’ll attack the last argument. If bunting is coached, bunters will be better. In my own research, as well as research done by others, I’ve found that there have been years when even the pitchers are able to bunt successfully over 90% of the time. Many people say that practice makes perfect, and while perfection might not be reached in the batters box, I wouldn’t be surprised if bunters were allowed to get close, or at least to their abilities in the 80’s.

Innings are more prosperous after bunt

The second argument is the main staple of this essay. In the world of analytics, general numbers are not good enough to explain why a phenomena is bad. Tom Tango’s famous Run Expectancy Matrix is used to make arguments against bunting across the Internet. Unfortunately, it’s assumed that the situations just exist rather than being set up the way that they are. It would be appropriate to use the table if a team were allowed to place a man, or men, on a base, or bases, and set the number of outs. However, as a strong believer in the principle of sufficient reason, I believe that there’s variability between a man on second with one out from a bunt and a man on second with one out from other situations.

For this reason, I set up my own analysis through the resource of Retrosheet play by play for the years of 2010-2013. To make things simple and not delve too deeply in varying circumstances, I will simply use larger data sets and noticeable differences to tell a story. First, I will look at only innings that start with men on base before the first out. Sacrifice bunts cannot happen when men are not on base, so it would be unfair to statistically compare innings with bunts to just innings without bunts. In line with Retrosheet’s system, I’m looking at all instances of SH, when they occur before (and usually result in) the first out.

To summarize, I’ll be looking at the percent chance that a team scores in an inning where they are able to get a man, or men, on base before the first out (as well as the average runs per inning when that situation is set up). I will compare this base situation to the percent chance that a team scores in an inning when they decide to sacrifice for that first out (as well as the average runs per inning when that situation is set up).

This data can be seen below with a total of about 53,000 innings across seasons where men were on base before the first out. In general, through the four years, teams score in about 26.8% of innings with about 0.478 runs per inning (RPI); when men get on base before the first out, they score 45.8% of innings with a .691 RPI. (In innings where a leadoff HR is hit, this does not count as men on base (nor will these runs count in calculation of either group, assuming men get on after the home run is hit, and before an out)).

Percent of Innings where a run is scored

Many managers, if not statisticians, understand this increase in the chance to score a run; after all, that’s why they do it. In 2010 and 2013, deciding to, and successfully laying down a sacrifice bunt resulted in a 13% increase in the ability to score that inning for the AL. And while it would make sense that the argument stops there, RPI also supports the sacrifice bunt (with data of the last four years). (Here, again, RPI = Runs scored after MOB B1O situation divided by number of innings of situation.)

Runs per Inning based on situation

This increase in RPI (seen as high as 0.137 Runs Per Inning larger than without bunting, 2012 AL) can contribute a decent number of runs over the course of a season. For example, in 2013, if the Oakland Athletics bunted a little less than once per series, they would have been on par with National League teams with number of bunts (in the 60’s). If they were able to bunt 47 more times (68, rather than 21), then their run total would have given them enough wins to have the best record in baseball (using Bill James adjusted pythagorean expected win percentage).

To summarize, an adjusted estimated runs table with respect to sacrifice bunt set up positioning and outs would produce more runs than the average table that does not take into concern how outs or players arrived at their position. This argument was suggested at the end of an essay by Dan Levitt, with earlier data in a more complex and subtle manner. RPI and the probability of scoring a run increase with a sacrifice bunt.

Bunting is symbolic of the greater good

The first and final argument to discuss is the idea that a sacrifice bunt throws away an out. In baseball, if a player bats out of order, or does not run out an error (among other mental mistakes), then that is giving away an out. And I believe that if a coach tells a player that he can’t hit, and to bunt because he can’t hit, then I wouldn’t argue that in those cases, you are giving away an out (knowingly removing the opportunity from the player to get a hit). So unless you believe that’s how coaches interact with their players prior to calling for the bunt, I will disagree with that notion.

The dictionary definition of sacrifice is “an act of giving up something valued for the sake of something else regarded as more important or worthy.” It’s the biggest theme in religious studies, the coolest way to die in movies, and the plot for heroic stories in the nightly news. Eliminating the psychological effects of a sacrifice, where they’re common place in our culture, seems slightly irresponsible after seeing the data.

This idea lends nicely to the discrepancy between American and National Leagues. Articles can be found, research has been done, and the common thought among those surrounding the game is that pitchers should bunt because they won’t do much else (in appropriate situations). In fact, an article by James Click gives the opinion that the lower the average, the more advantageous it is to bunt. However, my argument is the opposite. The amount they sacrifice, if they’re unable to hit is not valuable to those involved. If the pitcher is respected as a hitter, then their sacrifice is meaningful. Mentally as a leadoff man, if your pitcher is hitting sub .100, and there’s a man on base, he’s bunting because he cannot hit. That’s not a teamwork inspired motive, that’s a picking poison motive. The chart below shows data from the last four years when men get on base before the first out, it distinguishes that the National League is better than either league that doesn’t bunt, but far from as effective as AL bunters.

The argument can be made that the AL contains better hitters, and while I believe this, there would be a larger separation of the % scoring without bunting as well as the RPI of the innings where players get on before the first out.

Summary Chart

Because of this separation, I feel that bunting is not giving away an out, but sacrificing for something greater. Simply put, if my teammate sets me up to knock in a run with a hit, that’s easier that having to find a gap, or doing something greater. In many cases, I might need to just find a hole in the infield. Also, I know that my team, and coach, believes in me to be successful. Professional athletes can’t possibly feel pressure and confidence that emanates from teammates with the hopes of greater success, that idea would be ridiculous, right? Those ideas are practiced and taught in business places and self-help books around the world.


The data that I used was from Retrosheet, and while this data lists a lot of SH’s (sacrifice bunts) from where errors occur, to double plays, the main output is the standard sacrifice bunt. That being said, it does not include instances where the batter was bunting for a base hit (regardless of number of men on base), or other strange incidents of sacrifice failures (places where the scoring did not distinguish that an SH was in play). After recreating the analysis to include all bunts, the values of RPI and % scoring assuming men on base before the first out, values were still larger than without the bunt, but not as large as the sacrifice representation. This argument falls with the established idea that bunting could be more successful than most people think (especially when the bunt is a sacrifice). For instance, if the numbers above are reduced by as much as 85% in some cases, it still produces more successful results.

The next piece of opposition is that different circumstances have different weights in these situations, and that my case is too general to provide an advantage to a staff trying to decide whether to bunt. My argument is that upon analyzing circumstances, the most important element is the sacrifice bunt. In most situations, I feel that it will boost the team’s ability (and desire) to have success. With four years of data, my goal was to be able to refute the reliance on the simple Tango Run Expectancy Matrix, and how it is used, not to recreate one. In my opinion, in order for people to understand how historically successful situations have been, there should be hundreds of Run Expectancy Matrices highlighting how runners came to be where they are, as well as what batters follow.

The final piece of opposition has been created by myself during the generation of this essay or idea. The Heisenberg Uncertainty Principle relates to the ability to study the speed and position of a microscopic particle. Simply put, by studying one, you’re unable to observe the other. The act of observation limits the ability to fully observe. Because my argument is set up in a romantic sense, it could be argued that this principle relates. If coaches and teams start bunting every other inning, the act of giving oneself away for the greater good of the team will diminish and its advantage psychologically will wither away. In other words, the knowledge of how something effects one emotionally can limit one from being emotionally affected. I present this as an opposition because I feel that this might already be the case where if a pitcher is repeatedly bunting, teams will not think much of it as a quest for the greater good. However, when players are seen as an asset in the box, this advantage still exists; so teammates can still be sold on the relevance of the opportunity.

If these ideas spread, will this essay result in more bunts, especially when there are no outs? Probably not, because statisticians are stubborn. But it definitely provides an outlet for coaches who support the old school, traditional game of baseball.

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Thanks for this thought-provoking article.

I am definitely going to use my Father’s Day Barnes & Noble gift card to get a copy of “The Book” to see what they say about the “by-the-book” optimality of the sacrifice bunt. I’m guessing that the “formulaic” answer would be that the sac bunt would be more valuable based on (a) having a lead vs. trailing and (b) late in the game vs. early on. This is because the relative “utility” of trying to maximize Pr(>0 runs scored) vs. trying to maximize E(runs scored) is greater in later innings and greater for teams in the lead.

BTW – I love your use of the Heisenberg Uncertainty Principle here, even if someone can successfully refute the parallel to evaluating sacrifice bunts.


The Tango run expectancy with no outs and a man on first is, depending on the era, .83, .85 or .94. In your data, the run expectancy of “men on base before first out, no sacrifice” is only 0.67-0.70.

Assuming Tango’s data is more reliable, that would completely invalidate your argument. How do you account for the wide difference in your data from established historical norms?

Also, the principle of sufficient reason is a ridiculous argument and the self-sacrifice as a morally superior and thus psychologically beneficial approach is equally so. If there was a psychological benefit that produced more runs, it would show up statistically as more runs. Your data shows a slight bump compared to Tango in ‘man on second one out’ but your conclusion mostly seems based on a greatly reduced RPI for the null hypothesis (man on first, no out) than has been previously established.


Tip: don’t use grayscale for your graphs. Good stuff otherwise!

Jon L.
Jon L.

I read articles years ago that covered that there are situations in which sacrifice bunts increase your chance of scoring a run, although they generally decrease your chances of scoring more than a run. Thus, sacrifice bunts can make sense in certain strategic situations.

You set up a straw man when you broadly claim that statisticians do not recognize the potential value of a bunt and/or seek to wipe out the bunt entirely. Your article would be more compelling if it included a fairer presentation of the opposing arguments.


You are exactly right. Bunting situationally (first man on, tie game late) does make sense, because you only NEED one run. Run expectancies take into account multiple run innings across all innings and all situations. The same goes for the late stolen base. See Roberts, Dave, 2004 ALCS, BOS-NYY.

A couple of things though. Not all bunters, baserunners, or SB guys are equal. We know that all teams maximize their chances at a successful SB by knowing the pitcher’s delivery time, the catcher’s throwing time, and the runner’s reaction time and speed. Plus they try to pick a slow or hard to handle pitch to go on. This increases the chance of a successful steal. Duh. Why wouldn’t you do that?

Does this not apply to bunting as well? Teams also know who’s a 40% success rate bunt guy, a 60% guy, or a 90% guy. Again, add in the runner’s read, speed, and react time, and what type of pitcher you’re facing (FB? CU?) and you’re able to play the percentages to maximize your chance of success. So yes, when you need ONE run and you can get your odds of success in the 80%+ range, go for it. Are you gonna do it with David Ortiz on 1st and Napoli at the plate? Hell no. But if you can set up the right guys for the situation, it’s a good play.

Finally, last year I did a year-long study of one playoff team to find the value of advancing one base. Not surprisingly, the value of any base gained, be it by base hit, walk, error, or HBP, turned out to be 0.27 runs. Gee, there are 4 bases, each one gained gets you almost exactly 1/4 run on average. As mentioned, this was a playoff team. I’m sure non-playoff teams would be slightly less efficient at turning bases into runs, but not by much, maybe 0.22 runs per base gained. Now you can really see the value of the extra base in a tight, late game.

I’d love to see a full year analysis of runs per base gained for all MLB teams, but I’m betting it would be within thousandths of .250. The beauty of baseball is the sheer mass of numbers even out the statistical noise, if not over one year, then definitely over two.

Mark L
Mark L

I think the issue is the base data you started from. Is it better to have a guy on second with one out than it is to have a guy on first with no outs? Every bit of analysis I’ve ever read indicates it’s best to have a guy on first with no outs, that an out is a great deal more valuable than a base.

So, and I presume you’ve already done this, why is your data different to everyone else’s?