FanPost

The key to successful bunting is knowing when to do it

David Richard-USA TODAY Sports

So bunting doesn't work very well. Giving up an automatic out to advance a runner is normally a losing proposition. There are many ways this has been dissected and analyzed over time. I'd like to introduce one you may not have seen, with some results that might surprise you.

Usually bunting strategies are analyzed by comparing run expectancy for various situations and asking the question "would you be better off with a sacrifice in this situation?" A handy set of tables with this information is available here. Now, bunt attempts don't always result in a successful sacrifice, they sometimes fail and sometimes succeed beyond your wildest dreams. They do occasionally empty the bases and leave the batter standing on third after one or more errors.

outs runners Chance of
scoring
Runs
expected
0 1B - - 0.426 0.853
0 - 2B - 0.623 1.102
0 - - 3B 0.84 1.34
1 - 2B - 0.411 0.678
1 - - 3B 0.664 0.943
1 - - - 0.153 0.252

(This table uses tangotiger's numbers for the 1969-1992 period)

This table shows six of the 24 base-out states. The first row is no outs, runner at first. The last row is one out, bases empty. In any situation where you have no outs and a runner at first, you might expect to score about 43% of the time. When you do score, you will sometimes score more than a single run, so if your team saw this situation one hundred times, you might expect to score a total of 85 runs.

Because you have 4% better chance of scoring with a runner at third and one out, than with runner at 2nd and no out, there might be a use for bunting. The downside is that your total runs expected drops from 1.102 to .943, which means you will score less runs in total, although you come up empty less frequently.

Sacrificing with a man on first reduces your total run scoring by about 1 run for every 5 times you "successfully" sacrifice in this situation. To make things even worse, it actually reduces your chance of scoring at all from .426 to .411 – not good. In fact, it's downright awful.

This as far as you need to go to identify the ONE SIMPLE TRICK to bunt successfully: don't do it unless there are runners in scoring position. (RISP)

Now, that we've found the Secret of the Ages. All that's left is to get an idea of how useful bunting with RISP is. I've previously attempted to show the break-even point for a sac bunt attempt with a runner on second. This time around I'm going to examine the actual historical outcomes for RISP sac bunt attempts. Baseball-Reference.com's play index is super-duper handy in this regard. And it spits out the Win Probability Added for each attempt.

Win probability goes a step further than run expectancy because it takes into account the current score and inning. This is crucial to a realistic assessment of bunting, since the later the inning and the closer the score, the more crucial any single run becomes.


PA WPA
2015 683 0.3
2014 591 4.3
2013 600 2.9
2012 789 0.2
2011 834 5.6
2010 796 6
2009 740 1.5
2008 685 -0.6
2007 670 0.3
2006 798 -0.4
2005 836 0.8
2004 799 -0.3
2003 817 1.5
2002 766 8.6
2001 705 8.3
2000 602 2.7
1999 677 0.2
1998 787 3.9
1997 814 3.4
1996 757 -0.6



average 14746 48.6

This table shows the WPA for non-pitcher bunt attempts with less than 2 out and RISP.

Turns out that Major League Baseball players have been pretty successfully bunting with RISP for more than two decades. (Why they keep bunting with a man on first, I don't know.) Over the last twenty years, hitters have won an estimated 48 games by utilizing their bunting skills with RISP.

By now you're probably thinking "that sounds OK, but how good is it really?" I did a little digging and guesstimated that it's similar to the value that hitters with a wRC+ around 115 bring their teams. Non-pitchers bunting with RISP have averaged .003295989 per plate appearance. We will now compare that with WPA per PA of various hitters.

The following tables show the hitter's wRC+ for their career, along with WPA and Leverage Index for plate appearances with RISP and less than two outs. They also show a comparison with the WPA theoretically available to an average bunter, factoring in the LI.

BtB stands for "better than bunting," it shows how much better the hitter did versus the average RISP bunt over his career. The final column shows how many additional games the hitter would win per 1000 decisions to swing away. If this last column is negative, the hitter would have been better off bunting.

One caveat on this last column is that bunting happens more often in close games and later innings. Not every PA in these tables fits that description, so bunting wasn't totally appropriate. Nevertheless, it gives you something to compare with bunting's WPA per plate appearance.



career RISP, < 2 out Avg Bunting

wRC+ PA WPA LI WPA
/ PA
PA WPA LI WPA
/ PA
WPA
/ Bunt
BtB Per 1000
Miguel Cabrera 170 8322 56.0 0.99 .01768 1418 10.4 1.51 .00733 4.0 6.4 4.5
Mike Trout 167 2877 21.2 0.93 .04387 364 7.8 1.57 .02143 1.1 6.7 18.5
Joey Votto* 161 4757 38.6 1.00 .02591 767 10.0 1.56 .01304 2.2 7.8 10.1
Jose Bautista 154 5642 19.2 0.98 .05104 866 3.6 1.55 .00416 2.5 1.1 1.3
Andrew McCutchen 152 4504 24.3 0.97 .03992 687 5.0 1.57 .00728 2.0 3.0 4.3
Giancarlo Stanton 149 2958 15.3 1.05 .06863 472 1.2 1.62 .00254 1.4 -0.2 -0.5
Paul Goldschmidt 148 2648 19.3 1.01 .05233 432 4.8 1.58 .01111 1.3 3.5 8.2
David Ortiz 148 9465 45.4 1.00 .02203 1604 8.8 1.52 .00549 4.6 4.2 2.6
Ryan Braun 143 5255 26.4 0.95 .03598 832 3.9 1.55 .00469 2.4 1.5 1.8
Buster Posey 143 3078 15.3 1.04 .06797 513 3.4 1.66 .00663 1.6 1.8 3.5
average










5.4



career RISP, < 2 out Avg Bunting

wRC+ PA WPA LI WPA
/ PA
PA WPA LI WPA
/ PA
WPA
/ Bunt
BtB Per 1000
Jackie Jensen 120 6077 18.7 1.07 .00308 1021 5.6 1.63 .00548 3.1 2.5 2.4
Paul O'Neill 120 8329 20.1 1.01 .00241 1486 4.6 1.53 .00310 4.2 0.4 0.2
Darrell Evans 120 10737 39.0 1.03 .00363 1607 8.0 1.65 .00498 4.9 3.1 1.9
Carlos Beltran 120 9929 29.0 0.98 .00292 1583 8.0 1.5 .00505 4.4 3.6 2.3
Mark Grace 120 9290 37.8 1.02 .00407 1476 9.7 1.63 .00657 4.5 5.2 3.5
Dave Parker 120 10184 35.6 1.03 .00350 1725 10.0 1.6 .00580 5.2 4.8 2.8
Andy Van Slyke 120 6495 14.3 1.01 .00220 1127 4.9 1.59 .00435 3.3 1.6 1.4
Vic Wertz 120 7027 30.3 1.10 .00431 1042 5.7 1.71 .00547 3.3 2.4 2.3
Ted Kluszewski 120 6469 26.9 1.08 .00416 956 3.9 1.69 .00408 3.0 0.9 0.9
Troy Glaus 120 6355 10.4 1.01 .00164 1036 -0.1 1.55 -.00010 3.0 -3.1 -3.0
Mike Greenwell 120 5166 15.3 1.05 .00296 841 6.5 1.59 .00773 2.5 4.0 4.8
average










1.8



career RISP, < 2 out Avg Bunting

wRC+ PA WPA LI WPA
/ PA
PA WPA LI WPA
/ PA
WPA
/ Bunt
BtB Per 1000
George Hendrick 115 7834 13.8 1.07 .00176 1277 0.1 1.64 .00008 3.9 -3.8 -3.0
Aramis Ramirez 115 8986 27.3 1.03 .00304 1403 9.0 1.65 .00641 4.3 4.7 3.3
Craig Biggio 115 12504 24.9 0.99 .00199 1484 0.5 1.58 .00034 4.4 -3.9 -2.6
Billy Butler 115 5412 6.8 0.99 .00126 801 2.1 1.57 .00262 2.3 -0.2 -0.3
Ryne Sandberg 115 9282 22.9 1.01 .00247 1284 6.2 1.64 .00483 3.9 2.3 1.8
Kevin McReynolds 115 6039 15.9 1.06 .00263 915 -1.2 1.75 -.00131 3.0 -4.2 -4.6
Matt Stairs 115 6024 10.0 1.05 .00166 971 1.1 1.63 .00113 3.0 -1.9 -1.9
Amos Otis 115 8247 20.2 1.01 .00245 1300 7.2 1.58 .00554 3.8 3.4 2.6
Smoky Burgess 115 5013 13.8 1.17 .00275 739 7.0 1.97 .00947 2.7 4.3 5.8
Richie Ashburn 115 9737 18.6 0.99 .00191 841 -2.3 1.64 -.00273 2.6 -4.9 -5.8
Brett Butler 115 9545 17.7 0.99 .00185 1027 1.5 1.68 .00146 3.2 -1.7 -1.7
Johnny Callison 115 7437 17.3 1.00 .00233 1026 5.5 1.67 .00536 3.2 2.3 2.2
average










-0.3



career RISP, < 2 out Avg Bunting

wRC+ PA WPA LI WPA
/ PA
PA WPA LI WPA
/ PA
WPA
/ Bunt
BtB Per 1000
Vada Pinson 110 10402 20.8 1.00 .00200 1536 1.4 1.58 .00091 4.5 -3.1 -2.0
Ian Kinsler 110 6192 4.9 0.95 .00079 833 -0.1 1.43 -.00012 2.2 -2.3 -2.8
Matt Williams 110 7595 11.7 1.03 .00154 1146 -2.8 1.63 -.00244 3.5 -6.3 -5.5
Raul Ibanez 110 8278 15.3 1.01 .00185 1217 7.8 1.58 .00641 3.6 4.2 3.5
David Segui 110 5451 3.8 1.02 .00070 859 0.9 1.5 .00105 2.4 -1.5 -1.8
Torii Hunter 110 9692 1.9 1.02 .00020 1499 -4.5 1.57 -.00300 4.4 -8.9 -5.9
Jermaine Dye 110 7214 0.2 1.03 .00003 1172 -1.3 1.59 -.00111 3.5 -4.8 -4.1
Pat Kelly 110 5013 7.9 1.02 .00158 567 0.3 1.72 .00053 1.8 -1.5 -2.7
Jeromy Burnitz 110 6580 16.7 1.06 .00254 997 1.0 1.64 .00100 3.1 -2.1 -2.1
Adam Laroche 110 6329 9.6 1.04 .00152 952 -2.1 1.7 -.00221 3.0 -5.1 -5.4
average










-2.9

So now you can see why the wRC+ 115 for the divider between "should bunt" and "shouldn't bunt." Where do the various members of the 2016 Indians fall? That's for you to decide. STEAMER and I aren't speaking right now because it thinks more or less everybody on the team should bunt in these situations.

The Indians' position players had 866 plate appearances with RISP and less than 2 outs in 2015. They bunted in only(!) 38 of those chances, leaving 828 times they did not bunt. Since they had a wRC+ of 99 last year, there is a good argument to be made that the Tribe should bunt both more and less often.

Just follow my ONE SIMPLE TRICK!

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