The Best Bunts, and Bunters, of 2019

I have a confession to make, one that might be uncool in the modern, hyper-optimized world of baseball analysis. I love bunts.

I know, I know. I’ve been spending most of a recent article series on old World Series tactics railing about bad bunts. I’ve read Moneyball; outs are bad and runs are good. That’s all true, but I can’t help it. I love to see a well-executed bunt for a hit. Drag bunts, sneak attacks aimed at shifts — I love them all. So today, I set out to find the best bunter.

A quick refresher of why bunting is bad: it makes outs. If you want some proof of this, look no further than a run expectancy chart from 2019:

Run Expectancy, 2019
Bases/Outs 0 1 2
000 0.5439 0.2983 0.1147
003 1.3685 0.9528 0.3907
020 1.1465 0.7134 0.3391
023 1.9711 1.3679 0.6151
100 0.9345 0.5641 0.2422
103 1.7591 1.2186 0.5182
120 1.5371 0.9792 0.4666
123 2.3617 1.6337 0.7426

If you haven’t read one of these before, no worries. Each number represents how many runs scored, on average, from the relevant combination of baserunners and outs until the end of the inning, across all games in 2019. The bases go down the left side, and the outs go across the top. If you have runners on first and second (120 in the table) with no outs, for example, you should expect to score 1.537 runs in the rest of the inning.

This doesn’t mean you’ll always score that many runs, obviously. But it’s a useful baseline. Unless you have some very weird batters coming up (very good or very bad would both do), you can estimate a player’s contribution to how many runs you’ll score by comparing the base/out state before and after their turn at bat.

Let’s try a bunt. We’re back in our first position; runners on first and second with nobody out. Johnny Batcontrol executes a perfect bunt, leaving us with runners on second and third and one out. Beforehand, we expected to score 1.537 runs. Now, we expect to score 1.368 runs. That little maneuver cost us 0.17 runs.

That’s the argument against sacrifice bunting that we probably all know by now. But the reason the bunt was popular in the first place is because it hedges against abject failure. Bunting in this spot costs 0.17 runs, but striking out costs 0.558 runs. That’s not really the way it works, because you won’t always strike out, but that’s the thought process behind bunting.

If you can bunt well enough that it’s only sometimes a sacrifice, and other times you reach base safely, things get more interesting. If you can bunt for a hit half of the time in the previous situation, your expected runs scored go up by 0.33 runs. Bases loaded with nobody out is a nice outcome, and second and third with one out isn’t a terrible fail state.

With this thinking in mind, I simply looked at every plate appearance in 2019 that ended in a bunt and applied this logic. I took the run expectancy before the bunt and after the bunt and assigned the difference to that player. This bunt, for example, was the best of 2019:

Two runs, no outs, and now a runner on third? That’ll definitely do. But Ben, you might say, didn’t a lot of the value come from the error? Well, sure, but bunts put pressure on the defense, and that situation results in an error a lot of the time. Maybe not that big of an error, or maybe the catcher just eats the ball and turns it into a single, but good bunts force defenders out of their comfort zones. I’m happy to count the results of errors as marks in the hitter’s favor.

By the same token, here was the worst bunt:

Oh boy. Let’s see that Lindor dance again:

Yeah, uh, that was a bad one. First and second with no outs becoming a man on first and two outs is going to cost you big time.

Enough with the explaining; who are the best bunters? Well, you’ve already seen one, and I’ll give you a hint: it’s not the guy who hit into a double play:

Most Runs Added By Bunting, 2019
Player Bunts Runs Added
Kolten Wong 24 4.5
Victor Robles 25 2.7
Jorge Polanco 11 2.6
Delino DeShields 23 2.5
Yolmer Sánchez 12 2.3
Terrance Gore 4 2.2
Adam Eaton 22 2.2
Leury García 15 2.1
Jason Kipnis 10 2.1
Aaron Nola 6 2.1

Wong added 4.5 runs of value to the team with only 24 bunts last year. That’s above Mike Trout-level performance; a batter with a .540 wOBA would project to add 4.5 runs over 24 plate appearances, and Trout checked in at a tidy .436 last year. It’s not a one-for-one comparison, because many of Wong’s bunts came with runners on base, in situations where success or failure change run outcomes by more. But it’s an absolutely stellar result.

I had an inkling that Wong might be a great bunter. But I wasn’t so sure about Victor Robles, and Aaron Nola is an interesting name. Earlier this week, Lucas Apostoleris looked into whether pitchers should bunt less, and the list of the worst bunters, on a run expectancy scale, is full of pitchers. But Nola’s position so near the top intrigued me. Is he the best bunting pitcher ever?

Sadly, it seems that he’s probably not. He had only three plays with a positive change in run expectancy, and all were errors on the defense. They weren’t even the high-pressure errors that Wong forced; Willson Contreras just missed a throw despite plenty of time to set his feet, Pete Alonso forgot to pick up the ball while charging, and Paul Goldschmidt made an ill-advised throw in an attempt to get the lead runner.

That’s one problem with this method of analysis, but I don’t think it’s enough to torpedo the results. Errors are a real part of bunts, and even if you should take extreme results with a grain of salt, assuming perfect play by the defense doesn’t reflect reality. You can look at Nola skeptically while still accepting the fact that forcing bad throws, in a large sample, is a useful skill.

Nola achieved those results in only six bunts, which makes him highly efficient on a per-bunt basis. Even though that’s kind of nonsense, let’s look at the other most efficient bunters (minimum five bunts in play):

Runs Added Per Bunt, 2019
Player Bunts Runs Added/Bunt
Aaron Nola 6 0.345
Chris Taylor 5 0.292
Brandon Belt 7 0.277
Kyle Schwarber 5 0.276
Bryce Harper 5 0.268
Mike Yastrzemski 5 0.244
Jorge Polanco 11 0.237
Matt Olson 5 0.228
Jason Kipnis 10 0.209
Wil Myers 7 0.193

My bunt bae Brandon Belt is on the list, naturally. He’s a cold-hearted bunting champion. Wong is just off the list in 13th place, impressive for such a high-volume bunter. This group features a lot of sneak attack bunters — Bryce Harper isn’t out there laying down perfect bunts through the teeth of a stacked defense. He’s simply taking advantage of shifts and inattentive defenders. The baserunners count just as much, though.

If this were the end of the analysis, it would still be interesting enough. Wong is a great bunter, and he should do it more. Victor Robles and Adam Eaton are menaces in the Nationals’ lineup. Nola is lucky. These are all fun things.

But while we have the data, we can go another step deeper. Run expectancy isn’t the final word in value. Consider a situation where the game is tied in the bottom of the ninth. A guaranteed one run is far better than a 50% chance at two runs. This is a place where bunts whose only purpose is a sacrifice start to make more sense. With a runner on second and no one out in the ninth, per our WPA Inquirer, a sacrifice makes the home team 1.4% more likely to win the game.

Here’s a list of the bunters who added the most wins to their team:

WPA From Bunting, 2019
Player Bunts WPA
Delino DeShields 23 0.47
Victor Robles 25 0.45
Manuel Margot 8 0.41
Yolmer Sánchez 12 0.34
Jorge Polanco 11 0.30
Adam Eaton 22 0.29
Magneuris Sierra 4 0.29
Kolten Wong 24 0.27
Sean Rodríguez 5 0.27
Nicky Lopez 11 0.25
Jason Kipnis 10 0.24

No one added even half a win, but DeShields’s 0.47 win probability added in only 23 bunts is a lot of value over such a small amount of trips to the plate. Christian Yelich led baseball with 7.43 WPA in 580 plate appearances last year. Pro-rate those 23 bunts up to 580 PA, and DeShields would have been worth 11.8 WPA. Bunting rules!

This comparison is nearly as absurd as my Wong/Trout hybrid from earlier in the article. But in WPA terms, we actually have a solution. We can use WPA/LI, a mouthful of a statistic, to make this work. To work this statistic out, you simply divide how much a given outcome changed the win probability by the leverage index before the play. This results in something called context-neutral wins — you don’t get bonus credit for coming up in big spots or get punished for great outcomes in low-leverage spots, essentially.

That leaderboard, again, makes Wong look good:

WPA/LI From Bunting, 2019
Player Bunts WPA/LI
Kolten Wong 24 0.26
Victor Robles 25 0.24
Hanser Alberto 18 0.21
Delino DeShields 23 0.20
Adam Eaton 22 0.19
Jason Kipnis 10 0.18
Brandon Belt 7 0.16
Starling Marte 11 0.16
Jorge Polanco 11 0.16
Yolmer Sánchez 12 0.16

To be clear, the rate is still out of this world. Yelich led all offensive players in WPA/LI, too, with 7.05. Wong would have been worth 6.2 WPA/LI over the same number of plate appearances. Kolten Wong, bunting, is nearly Christian Yelich with the bat, in addition to playing Gold Glove defense.

Zooming back out to the broader picture, bunts as a whole cost offenses last year. They were worth -80 runs, or -2.61 WPA. That undersells bunts, though: it’s because pitchers bunt a lot and almost never reach base. If you strip out pitchers, bunts added 36.4 runs and 4.7 WPA to offenses last year. It wasn’t a question of great hitters bunting, either: the weighted average of the bunters’ wRC+ was 85.

If you wanted to, you could head further down the rabbit hole here. We know how these players did on their bunts, but it also matters how good of a hitter they are when they don’t bunt. Mike Trout might be the best bunter in baseball, but we’d never know, because taking the bat out of Trout’s hands is a terrible decision.

But that’s a question for another day. Today, we’ve established a few things. First, some batters add a good amount of value when bunting. Second, pitchers are not those players — they’re almost always giving up runs and WPA when they bunt, because they’re almost always sacrificing. Lastly, don’t be Humberto Arteaga. A Francisco Lindor dance is fun — but not when it’s at your expense.

Ben is a writer at FanGraphs. He can be found on Twitter @_Ben_Clemens.

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3 years ago

“If you strip out pitchers, bunts added 36.4 runs and 4.7 WPA to offenses last year.”

Bunting for a base hit is good, but bunting to give the other team an out… probably not as good.

3 years ago
Reply to  Werthless

I would love to see this data compared to prior years. There’s been a definite shift in understanding regarding bunting in baseball, and it’d be super interesting to see how that’s changed over time.

3 years ago
Reply to  pepper33246

Especially since the rise of the infield overshift has caused a rise in bunting for base hits.