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Thursday, July 12, 2007
Bunts, bunts, bunts
Dan Fox checks in with all things bunts. Let’s focus on this:
Success Rate By Outs
0 1 2
Empty .441 .450 .488
First .307 .298 .492
Second .481 .518 .516
Third .359 .467 .498
First/Second .337 .259 .424
First/Third .333 .439 .502
Second/Third .444 .429 .495
Loaded .412 .339 .348
Let’s focus only on bases empty for now. The linear weight run value by base/out states is in Table 50 of The Book, but here’s another version of that:
http://www.tangotiger.net/RE9902event.html
So, bases empty 0 outs, the run value of a hit is +.39 runs, and an out is -.26 runs. The breakeven point is 26/(26+39)=.400. That means, when you think you have AT LEAST a 40% chance of making it to 1B with 0 outs and bases empty, you should bunt. The group average is .441, and so, it looks like players are bunting when they should in this situation. The average run value per PA is .39*.441 - .26*(1-.441) = +.027 runs per PA, or +16 runs per 600 PA. Since the guys bunting are likely below average hitters, they are really adding to their non-bunting numbers. If this were Barry Bonds however, his breakeven point would be far far higher than .400 or .441. So, you’d really have to look at it on a player-by-player basis.
(It should be noted that in Game Theory, you should do some seemingly unoptimal bunting every now and then, so that the defense thinks you may be bunting more than you should allowing you to hit away and punch hits through. Look up Game Theory in the Search box of this blog.)
The break-even point for 1 and 2 outs is 40% and 48%. As you can see in the above chart, the players do have a higher bunt success rate with 2 outs and bases empty. And since the breakeven point is much higher, we expect the frequency to be much lower. And it is:
Frequency By Outs
0 1 2
Empty .274 .153 .070
First .172 .070 .025
Second .064 .011 .003
Third .001 .007 .010
First/Second .084 .023 .003
First/Third .004 .010 .006
Second/Third .001 .002 .002
Loaded .000 .003 .002
In short, it’s likely that only the very best bunters try to bunt for a hit with 2 outs and bases empty.
The other major bunting situation is man on 1B and 0 outs. In this case, the run value of a hit is actually the run value of a walk in that chart (since a bunt hit and a walk have the same impact here). The break-even point is 44%, and yet the actual success rate is only 31%. It is an almost foregone conclusion that these “bunt for a hit” numbers includes an enormous number of sacrifice bunts that are not recorded as such.
Given that the frequency of bases empty 1 out (15%) is similar to man on 1b 0 outs (17%), it’s implausible that the success rates could be that different (45%, 31%). I’d also bet that the top 30 bunters in one situation will be markedly different from the second situation, further showing that we’re not really looking at a similar sample of bunters.
His count data is also fascinating:
Count Success Frequency
0-0 .422 .694
0-1 .369 .099
0-2 .090 .018
1-0 .438 .057
2-0 .506 .007
3-0 .500 .000
1-1 .409 .069
1-2 .116 .017
2-1 .440 .026
2-2 .136 .007
3-1 .526 .005
3-2 .125 .002
You can also figure out the run values by count. Just taking a quick glance at Dan’s numbers, I’d bet only the 0-0, 0-1, and 1-1 counts are appropriate for bunting.