Did the Bunt Work? Midseason Report

With the season halfway through the books, @FeatherdWarrior from @DidtheBuntWork joins us to investigate…well, did the bunt work? (Note: statistics do not include last night’s game against Mississippi State.)

It’s been a long week folks. The 20-5 drubbing Carolina endured at the hands of the Tarheels has reignited the debate around the hotness of Chad Holbrook’s seat for a second straight year. The consensus seems to be that if it’s not hot, it’s definitely uncomfortably swampy. Given the intensity of that debate, I thought it might be a good time to take a detour and check in on our little project account @didtheBUNTwork.

The basics are pretty simple. To date, the South Carolina Gamecocks have executed 11 successful bunts out of 27 attempts. That’s a success rate of about .407, which looks pretty good if you’re comparing it to the team batting average (sitting at .274 at the time of this writing). However, keep in mind we’re defining a successful bunt as one that advances a runner who later scores, so comparing a bunt success ratio to a batting average isn’t exactly apples-to-apples. For one thing, even a “successful” bunt can result in an out. Assuming most of our bunts are sacrifices with zero outs, we’re essentially giving a bunt a whole inning of at-bats before we conclude whether it was successful. Therefore, I think I more appropriate benchmark would be to multiply the team batting average by 3 since, if the bunt weren’t an option, the team would have three outs to score the runner. In that case, a .407 success rate doesn’t look all that good next to an .822 success rate (.274 x 3), does it?

Before you say anything, I’m well aware of how crude this comparison is. This is the equivalent of a napkin scribble to settle an argument in a bar. For all the attention our bunting receives on Twitter, no one ever seems to address it in any kind of intellectually rigorous way. This is my attempt at doing so. I’m not trying to add Bill James’s sword to my throne or anything.

With that in mind, here are some other interesting nuggets I’ve pulled out from the data I’ve collected so far.

Eight bunts (.296) have failed to advance any runners. A major reason for utilizing the bunt is to guarantee advancing at least one runner at least one base. If we’re not even advancing a runner 30% of the time, how effective can that strategy be? Another 4 bunts (.148) only advanced a runner because the opposing team committed a fielding error. That means nearly 45% of the time (.444) our players cannot execute a bunt correctly. Granted, not all of our bunt attempts are sacrifices. Sometimes we actually bunt for a hit, but that’s extremely rare. So far this season we’ve successfully bunted for a hit a dismal 2 of 27 (.074) times.

For the season, the Gamecocks have logged a +31 weighted base runner differential. This is something I kind of made up, but basically you weight the bases 1-4 with 1st base being a 1 and scoring a run being a 4. So if a bunt advances a runner from 1st to 2nd, the WBD would be +1 (2 – 1 = 1), assuming the batter is called out. If the batter is safe the WBD would +2 (2 -1 +1 = 2). This means that, on average, the Gamecocks advance a runner 1.15 bases per bunt attempt. Six times a Gamecocks bunt has resulted in a 0 WBD and twice it has resulted in a negative WBD.

Danny Blair (.239 batting average) is the Gamecocks’ most prolific bunter with seven attempts.

Jonah Bride, the second best hitter on the team (.306) has accounted 6 of 27 bunts, twice the expected amount if you assumed the bunts were spread evenly throughout the order (9/27 = 3). Three of Bride’s bunts have been successful.

True to his word, Chad Holbrook has not bunted the 3rd or 4th place batter at all this season. He has, however, bunted the 1st and 2nd place batters a total 15 times.

So there you have it. It will be interesting to see how these numbers compare to the second half numbers considering our level of competition will have greatly increased. More than anything, I see this as a starting point for future discussions. If my attention span endures I’d like record Carolina’s bunting stats well into the future. I think it would be interesting to see how our success varies over the years. And I’d really like to be able to compare our bunting statistics with the rest of the SEC, but I just don’t have that kind of time.


Top 6 BAV. w/ At Least 50 ABs Batting Average Bunts Successful Bunts
LT Tolbert 0.324 2 1
Jonah Bride 0.306 6 3
Chris Cullen 0.303 1 0
Jacob Olson 0.298 0 n/a
Matt Williams 0.278 1 1
Alex Destino 0.277 0 n/a
Lineup Position No. of Bunts Successful Bunts
1 9 3
2 6 3
3 0 n/a
4 0 n/a
5 2 0
6 2 1
7 0 n/a
8 4 2
9 4 2
Inning No. of Bunts
1 0
2 1
3 2
4 2
5 4
6 4
7 6
8 4
9 2
10 2