Is bunting a good thing? When is the right time to sacrifice? Or bunt for a hit? How about a good old-fashioned squeeze? There has been much debate over the years over the old-school approach taken by Gamecock skippers Ray Tanner and Chad Holbrook in regards to bunting. (Read: they LOVE the bunt.) But how often does it really work, and when is it not a good strategy?
This season, TRC will be the vessel for some deep-diving analysis from @chickenhoops, @featherdwarrior, and the team over at @DidtheBuntWork. The mission will be to put together some hard analytics to determine how effective the Gamecock bunt game is in 2017. Please enjoy this first installment from @chickenhoops.
Hi, you might remember me from such features as yelling about punting and Frank Martin’s lineups. Today, we’re here to talk about the third thing that drives me crazy – Chad Holbrook’s bunting.
I’m not exactly new to this topic, but with the help of others at @DidtheBuntWork, I’ll spend this year for the first time taking a systemic approach to Chad Holbrook’s fetish.
Let’s talk about the three bunts this week!
Feb. 23 – Kansas State
Inning: Bottom 6th
Score: 6-5 KSU
Batter: Danny Blair
Lineup: 1st
Runners on: none
Outs: 0
Did it work? No
Expected runs added/lost: No change
Actual runs added/lost: -0.31
This is a bunt where I take no issue – Danny Blair attempted to bunt for a hit and it didn’t work. My general rule here is that this should be a player-called decision, but I’m not going to fault either Holbrook or Blair for trying.
Feb. 24 – Wright State
Inning: Bottom 5th
Score: 3-3 tied
Batter: Danny Blair
Lineup: 1st
Runners on: 2nd
Outs: 0
Did it work? Not even a little bit – Wright State threw the runner out at 3rd.
Expected runs added/lost: -0.25 (anticipated one out, runner on 3rd)
Expected chance of scoring once: +3% (70% to 73%)
Actual runs added/lost: -0.76
Actual chance of scoring once: 38%
Now this is the type of Holbrook bunt that we’ve come to know and love. In the fifth inning of a tie game, Chad decides to lower our total runs expected by a quarter of a run in exchange for a piddling three-percent chance of scoring. He ends up being right, one run would be enough to win the game, but that’s a pretty massive bet with 12 outs to go.
Of course, Blair can’t get it down and the runner is nailed. Here’s why it matters to little that there’s not much upside to these bunts – because look at the massive downside. Blair’s bunt doesn’t work, and now the Gamecocks go from slightly more likely to scoring once (but less likely to score more than once) to unlikely to score at all and unlikely to score much. No reward, all risk!
Feb. 25 – Wright State
Inning: Bottom 6th
Score: 4-0 USC
Batter: Danny Blair
Lineup: 9th
Runners on: 2nd and 3rd
Outs: 1
Did it work? Yes!
Expected runs added/lost: -0.24 (anticipated one run in, runner at 2nd, one out)
Actual runs added/lost: +0.73 (one run in, runners at 1st and 2nd, no outs) 2.06 to 2.79
Actual chance of scoring once: 38%
This is the flip side of the coin above – sometimes a team can’t make a play and a bunt turns good. Here, the bunt works as well as possible – the run gets in and both runners are safe. That increases the Gamecocks’ expected runs in this inning from 2.06 to 2.79 runs. Of course, Carolina would go on to score a 6-spot in this inning, effectively putting the game away.
We’ll try to catch up with the bunts we missed earlier in the season, but for this week’s games alone, here’s how Chad’s bunting turned out – let’s follow along this season as we finally take our NEVER BUNT thesis and test it over the course of an entire year.
Season-to-date (only including KSU and WSU series)
Expected runs gained/lost: 0.49 runs lost
Actual runs gained/lost: 0.34 runs lost
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