Countless hours are poured over analytics to make minor improvements in overall run scoring and run prevention.

Money Ball showed that walks were an undervalued asset and circulated the realization that plate discipline is a skill. New research on pitch framing shows that not only is pitch framing a skill that can prevent runs, but it may prevent more runs than one could have previously imagined.

This article will look at bunting for a hit and try to identify if it is a skill, like plate discipline and pitch framing, that can efficiently and effectively increase offensive production, and answer the general question of, should players bunt more?

Is Bunting for a Hit a Skill?

Before we answer the ultimate question of whether or not players should bunt more, we need to first identify whether or not bunting for a hit is a skill to begin with.

This is where data becomes an issue, but we should be able to make do.

Before 2002 there are no records on FanGraphs of bunt hits, so I looked at all qualified hitter seasons from 2002 to 2014 in which a player bunted three or more times in a season—since most players go a whole season without a bunt, three bunts or more in a season puts a player in the top fifty percentile for bunt attempts in a season.

From there I looked at the year-to-year correlation of a player’s bunt hit percentage—bunt hits divided by bunts (i.e. a player’s batting average on bunts)—for the entire population. Mind you, we only have record of the amount of times a player bunts, not the amount of times a player attempted to bunt for a hit. So in all reality, a player’s bunt hit percentage would be higher if we were able to tease out the amount of times that they laid down a sacrifice bunt from their total bunts. However, from the data we are still able to find a .33r year-to-year correlation on bunt hit percentage for our population of hitters.

Takeaway: bunting for a hit is a skill.

Should Players Bunt More?

Now that we’ve answered the question of whether or not bunting for a hit is skill, we can circle back to our original question of whether or not players should bunt more.

Because we want to have a large enough sample of attempted bunts for bunt hit percentage (BH%) to stabilize, we will look at all qualified hitter totals (i.e. multiple season totals), not individual seasons, from 2002 to 2012.

To answer our question we need to look at the expected value gained for a player when they have an at bat where they don’t attempt to bunt—a regular at bat—and subtract it from the expected value gained in at bats where they attempt to bunt for a hit—a bunt hit attempt.

To come up with the expected value of a regular at bat we have to look at the linear weight value added per plate appearance of a player’s at bats from 2002 to 2012, or their entire career value if their whole career falls within that period. We then multiply that linear weight value per plate appearance by probability that they achieve one of those outcomes.

Here’s the formula for Expected Value of a regular at bat (xRA):

=((((1B-Bunt Hits)*0.474)+(2B*0.764)+(3B*1.063)+(HR*1.409)+(HBP*0.385)+(IBB*0.102)+(UBB*0.33))/(PA-Bunt Attempts))*((1B-Bunt Hits)+2B+3B+HR+BB+HBP)/(PA-Bunt Attempts)

This formula looks much more complicated than it actually is, but you’ll be able to click into the cells in the live excel document below and visually see how the values are computed. All of the decimals that are part of the formula are linear weight values, which you can find here.

We need to go through the same process to figure out what the expected value added is for a player on a bunt hit attempt—the average value added with a bunt times the probability of a successful bunt hit (BH%).

I was unable to find the linear weight value of a bunt hit, but we do have a sufficient substitute. A bunt hit essentially adds the same value as a base hit with no runners on base—.266 runs per inning. A single with no runners on base is a good proxy for the happening of a bunt hit. Like a base hit with no runners on base, a bunt hit offers no opportunity for a runner on base to score or advance past the next base in front of them. Short of looking at box score data to find the average amount of runners that scored per inning after a successful bunt hit, which will need to be done for a more conclusive answer to our question, we will use the average linear weight value of a single with no runners on for each of the out states as our linear weight value (i.e. I averaged the linear weight value of a single with no runners on base and no outs, a single with no runners on base and one out, and a single with no runners on base and two outs to get the average linear weight value; this is not the exact way to get the linear weights value of a single with no runners on base, because there are undoubtedly a different amount of singles with no runners on base that occurred for each out state, but this should be close).

This is the formula for expected value gained on a bunt attempt (xBA):

Bunt Hit Average (bunt hits/total bunts)*.266 (our estimated linear weight value for a bunt hit)

Now that we’re able to come up with the expected value added for a player in a regular at bat (xRA) and the expected value added for a player on a bunt hit attempt (xBA), we can subtract the two values from each other—xRA minus xBA—to see which players have lost the most value per plate appearance by not bunting.

This chart shows the players with a minimum of ten bunt attempts that have lost the most value by not bunting (i.e. which players have the biggest difference between their expected value gained from a regular at bat and a hit attempt):

Player Team Bunts Bunt Hits RA% BH% xRA xBA Net Value Carlos Santana Indians 14 11 0.365 0.786 0.07 0.21 -0.135 Eric Hinske - - - 11 8 0.331 0.727 0.06 0.19 -0.129 Ryan Zimmerman Nationals 20 15 0.350 0.750 0.07 0.20 -0.127 Luke Scott - - - 11 8 0.338 0.727 0.07 0.19 -0.123 Ken Griffey Jr. - - - 13 9 0.347 0.692 0.07 0.18 -0.112 Jason Kipnis Indians 29 18 0.333 0.621 0.06 0.16 -0.104 Josh Reddick - - - 10 6 0.303 0.600 0.06 0.16 -0.103 Colby Rasmus - - - 39 22 0.309 0.564 0.06 0.15 -0.091 Carlos Pena - - - 50 30 0.343 0.600 0.07 0.16 -0.090 Yasiel Puig Dodgers 11 7 0.383 0.636 0.08 0.17 -0.085 Joe Mauer Twins 32 20 0.399 0.625 0.08 0.16 -0.083 Rocco Baldelli - - - 28 15 0.319 0.536 0.06 0.14 -0.081 Adrian Gonzalez - - - 17 10 0.363 0.588 0.08 0.15 -0.079 Willy Taveras - - - 281 130 0.296 0.463 0.04 0.12 -0.078 Adam Jones - - - 40 21 0.318 0.525 0.06 0.14 -0.076 Rich Aurilia - - - 44 22 0.318 0.500 0.06 0.13 -0.074 Ryan Braun Brewers 17 10 0.367 0.588 0.08 0.15 -0.072 Chris Heisey Reds 43 20 0.291 0.465 0.05 0.12 -0.071 Carlos Gonzalez - - - 51 28 0.347 0.549 0.07 0.14 -0.070 Michael Brantley Indians 26 13 0.340 0.500 0.06 0.13 -0.070 Tony Womack - - - 109 47 0.300 0.431 0.05 0.11 -0.067 Leonys Martin Rangers 68 30 0.302 0.441 0.05 0.12 -0.067 Carlos Beltran - - - 43 23 0.358 0.535 0.08 0.14 -0.066 Shin-Soo Choo - - - 22 12 0.382 0.545 0.08 0.14 -0.066 Danny Espinosa Nationals 64 28 0.293 0.438 0.05 0.12 -0.066 Gregor Blanco - - - 103 47 0.335 0.456 0.05 0.12 -0.065 Miguel Tejada - - - 28 14 0.339 0.500 0.07 0.13 -0.065 Russell Branyan - - - 10 5 0.330 0.500 0.07 0.13 -0.065 Michael Saunders Mariners 61 26 0.295 0.426 0.05 0.11 -0.063 Gerald Laird - - - 113 47 0.296 0.416 0.05 0.11 -0.063 Corey Patterson - - - 246 102 0.280 0.415 0.05 0.11 -0.062 Lastings Milledge - - - 31 14 0.324 0.452 0.06 0.12 -0.062 Roger Cedeno - - - 26 11 0.310 0.423 0.05 0.11 -0.061 Angel Pagan - - - 70 32 0.330 0.457 0.06 0.12 -0.060 Craig Gentry - - - 48 21 0.336 0.438 0.06 0.12 -0.060 Justin Upton - - - 26 13 0.353 0.500 0.07 0.13 -0.060 Ben Zobrist Devil Rays 52 25 0.351 0.481 0.07 0.13 -0.060 Ichiro Suzuki - - - 155 72 0.354 0.465 0.06 0.12 -0.058 DeWayne Wise - - - 29 11 0.259 0.379 0.04 0.10 -0.058 Alexi Casilla - - - 130 50 0.289 0.385 0.04 0.10 -0.058 Neil Walker Pirates 26 12 0.338 0.462 0.06 0.12 -0.058 Alex Gordon Royals 15 7 0.344 0.467 0.07 0.12 -0.057 Jean Segura - - - 42 17 0.304 0.405 0.05 0.11 -0.057 Bobby Crosby - - - 32 13 0.302 0.406 0.05 0.11 -0.057 Jacoby Ellsbury - - - 52 24 0.343 0.462 0.07 0.12 -0.056 Charlie Blackmon Rockies 25 11 0.323 0.440 0.06 0.12 -0.056 Jim Edmonds - - - 17 9 0.377 0.529 0.08 0.14 -0.056 Manny Machado Orioles 26 11 0.308 0.423 0.06 0.11 -0.055 Mark Teahen - - - 28 12 0.325 0.429 0.06 0.11 -0.054 Carlos Gomez - - - 196 81 0.304 0.413 0.05 0.11 -0.054 Chase Utley Phillies 16 8 0.369 0.500 0.08 0.13 -0.054 Scott Spiezio - - - 16 7 0.333 0.438 0.06 0.12 -0.054 Peter Bourjos - - - 92 36 0.292 0.391 0.05 0.10 -0.054 Trevor Plouffe Twins 17 7 0.307 0.412 0.06 0.11 -0.053 Kenny Lofton - - - 102 46 0.354 0.451 0.07 0.12 -0.053 Corey Hart - - - 49 22 0.327 0.449 0.07 0.12 -0.053 Bobby Abreu - - - 16 8 0.389 0.500 0.08 0.13 -0.052 Tomas Perez - - - 48 17 0.276 0.354 0.04 0.09 -0.052 Kevin Frandsen - - - 39 15 0.308 0.385 0.05 0.10 -0.052 Jeff Francoeur - - - 10 4 0.305 0.400 0.05 0.11 -0.051 Alex Sanchez - - - 196 77 0.317 0.393 0.05 0.10 -0.051 Julio Lugo - - - 138 56 0.326 0.406 0.06 0.11 -0.051 Wilson Valdez - - - 71 24 0.273 0.338 0.04 0.09 -0.051 Ryan Church - - - 21 9 0.334 0.429 0.06 0.11 -0.050 Eric Young - - - 75 28 0.313 0.373 0.05 0.10 -0.049 Cameron Maybin - - - 51 19 0.305 0.373 0.05 0.10 -0.048 Neifi Perez - - - 145 48 0.270 0.331 0.04 0.09 -0.048 Torii Hunter - - - 48 21 0.337 0.438 0.07 0.12 -0.048 Michael Bourn - - - 190 74 0.328 0.389 0.06 0.10 -0.048 Alejandro De Aza - - - 68 27 0.325 0.397 0.06 0.10 -0.047 Travis Snider - - - 13 5 0.309 0.385 0.05 0.10 -0.047 Jordan Schafer - - - 98 34 0.302 0.347 0.04 0.09 -0.047 Roger Bernadina - - - 69 25 0.302 0.362 0.05 0.10 -0.046 Paul Bako - - - 27 9 0.292 0.333 0.04 0.09 -0.046 Starling Marte Pirates 45 19 0.337 0.422 0.07 0.11 -0.045 Emilio Bonifacio - - - 171 61 0.312 0.357 0.05 0.09 -0.045 Erick Aybar Angels 272 100 0.309 0.368 0.05 0.10 -0.045 Greg Dobbs - - - 11 4 0.305 0.364 0.05 0.10 -0.045 Ben Francisco - - - 23 9 0.320 0.391 0.06 0.10 -0.044 Nick Punto - - - 156 55 0.316 0.353 0.05 0.09 -0.044 David Ortiz - - - 12 6 0.384 0.500 0.09 0.13 -0.044 Akinori Iwamura - - - 28 11 0.342 0.393 0.06 0.10 -0.044 Jemile Weeks - - - 22 8 0.316 0.364 0.05 0.10 -0.044 Andrelton Simmons Braves 23 8 0.294 0.348 0.05 0.09 -0.043 Alfonso Soriano - - - 12 5 0.321 0.417 0.07 0.11 -0.043 Jose Reyes - - - 190 77 0.337 0.405 0.06 0.11 -0.043 Drew Stubbs - - - 62 23 0.311 0.371 0.05 0.10 -0.043 Kurt Suzuki - - - 36 13 0.312 0.361 0.05 0.10 -0.042 Dee Gordon Dodgers 104 36 0.309 0.346 0.05 0.09 -0.042 Brandon Crawford Giants 20 7 0.309 0.350 0.05 0.09 -0.042 Alexi Amarista - - - 35 11 0.274 0.314 0.04 0.08 -0.042 Luis Valbuena - - - 25 9 0.311 0.360 0.05 0.09 -0.042 Mark Reynolds - - - 10 4 0.324 0.400 0.06 0.11 -0.042 Luis Matos - - - 50 18 0.311 0.360 0.05 0.09 -0.042 Rajai Davis - - - 61 22 0.314 0.361 0.05 0.09 -0.041 Tony Gwynn - - - 95 31 0.304 0.326 0.05 0.09 -0.041 Reed Johnson - - - 117 45 0.332 0.385 0.06 0.10 -0.041 Everth Cabrera Padres 110 37 0.311 0.336 0.05 0.09 -0.040 Bobby Kielty - - - 10 4 0.350 0.400 0.07 0.11 -0.040 Kyle Seager Mariners 13 5 0.327 0.385 0.06 0.10 -0.040 Alex Gonzalez - - - 74 25 0.289 0.338 0.05 0.09 -0.040 David Ross - - - 65 24 0.313 0.369 0.06 0.10 -0.039 Cliff Pennington - - - 59 20 0.310 0.339 0.05 0.09 -0.039 Will Venable Padres 69 25 0.313 0.362 0.06 0.10 -0.039 Matt Carpenter Cardinals 14 6 0.377 0.429 0.07 0.11 -0.039 Robert Fick - - - 14 5 0.322 0.357 0.06 0.09 -0.038 Brian McCann - - - 25 10 0.342 0.400 0.07 0.11 -0.038 Melvin Mora - - - 117 47 0.350 0.402 0.07 0.11 -0.037 Gerardo Parra - - - 66 23 0.322 0.348 0.06 0.09 -0.036 Humberto Quintero - - - 25 7 0.265 0.280 0.04 0.07 -0.035 Miguel Montero Diamondbacks 16 6 0.342 0.375 0.06 0.10 -0.035 Rafael Furcal - - - 267 98 0.339 0.367 0.06 0.10 -0.035 Endy Chavez - - - 212 67 0.301 0.316 0.05 0.08 -0.035 Mike Cameron - - - 45 17 0.333 0.378 0.07 0.10 -0.034 Juan Pierre - - - 632 215 0.332 0.340 0.06 0.09 -0.034 Eduardo Nunez - - - 22 7 0.301 0.318 0.05 0.08 -0.034 Michael Tucker - - - 62 22 0.332 0.355 0.06 0.09 -0.033 Willy Aybar - - - 14 5 0.339 0.357 0.06 0.09 -0.033 Ben Revere - - - 79 25 0.321 0.316 0.05 0.08 -0.032 Grady Sizemore - - - 41 16 0.351 0.390 0.07 0.10 -0.032 Coco Crisp - - - 219 75 0.326 0.342 0.06 0.09 -0.032 Hanley Ramirez - - - 31 13 0.372 0.419 0.08 0.11 -0.031 Bret Boone - - - 11 4 0.333 0.364 0.06 0.10 -0.031 Jay Bruce Reds 17 6 0.323 0.353 0.06 0.09 -0.030 Jacque Jones - - - 52 18 0.324 0.346 0.06 0.09 -0.029 Dave Roberts - - - 224 75 0.341 0.335 0.06 0.09 -0.029 Paul Janish - - - 27 7 0.282 0.259 0.04 0.07 -0.029 Lance Berkman - - - 11 5 0.406 0.455 0.09 0.12 -0.029 B.J. Upton - - - 55 18 0.323 0.327 0.06 0.09 -0.029 John McDonald - - - 94 24 0.268 0.255 0.04 0.07 -0.028 Eli Marrero - - - 15 5 0.317 0.333 0.06 0.09 -0.028 Ian Desmond Nationals 58 19 0.314 0.328 0.06 0.09 -0.028 Jeff Mathis - - - 76 18 0.250 0.237 0.03 0.06 -0.028 Andy Dirks Tigers 21 7 0.330 0.333 0.06 0.09 -0.027 Alcides Escobar - - - 111 31 0.295 0.279 0.05 0.07 -0.027 Roberto Alomar - - - 64 20 0.326 0.313 0.06 0.08 -0.027 Jose Cruz - - - 38 13 0.339 0.342 0.06 0.09 -0.027 Howie Kendrick Angels 60 20 0.330 0.333 0.06 0.09 -0.027 Cesar Izturis - - - 148 39 0.290 0.264 0.04 0.07 -0.027 Nyjer Morgan - - - 161 51 0.336 0.317 0.06 0.08 -0.026 Miguel Olivo - - - 43 12 0.273 0.279 0.05 0.07 -0.026 Chris Getz - - - 82 22 0.304 0.268 0.04 0.07 -0.026 Sean Rodriguez - - - 53 15 0.293 0.283 0.05 0.07 -0.026 Jimmy Rollins Phillies 114 37 0.326 0.325 0.06 0.09 -0.025 Brian Dozier Twins 26 8 0.316 0.308 0.06 0.08 -0.025 Craig Counsell - - - 96 29 0.334 0.302 0.05 0.08 -0.025 Luis Castillo - - - 217 72 0.364 0.332 0.06 0.09 -0.025 Barry Larkin Reds 13 4 0.326 0.308 0.06 0.08 -0.025 Jeremy Reed - - - 46 13 0.308 0.283 0.05 0.07 -0.025 Andres Torres - - - 87 26 0.315 0.299 0.05 0.08 -0.024 Ronny Cedeno - - - 97 25 0.284 0.258 0.04 0.07 -0.024 Jerry Hairston - - - 128 38 0.322 0.297 0.05 0.08 -0.024 Orlando Palmeiro - - - 43 13 0.337 0.302 0.06 0.08 -0.024 Zack Cozart Reds 35 9 0.278 0.257 0.04 0.07 -0.024 Mark Kotsay - - - 51 16 0.332 0.314 0.06 0.08 -0.023 Adeiny Hechavarria - - - 36 9 0.284 0.250 0.04 0.07 -0.023 Alfredo Amezaga - - - 82 22 0.304 0.268 0.05 0.07 -0.023 Darin Erstad - - - 49 14 0.314 0.286 0.05 0.08 -0.023 Joey Gathright - - - 145 40 0.325 0.276 0.05 0.07 -0.023 Scott Podsednik - - - 194 60 0.336 0.309 0.06 0.08 -0.023 Willie Bloomquist - - - 50 14 0.317 0.280 0.05 0.07 -0.023 Marlon Anderson - - - 55 16 0.313 0.291 0.05 0.08 -0.023 Robert Andino - - - 40 10 0.291 0.250 0.04 0.07 -0.022 Austin Jackson - - - 48 15 0.334 0.313 0.06 0.08 -0.022 Dan Uggla - - - 24 8 0.336 0.333 0.07 0.09 -0.022 Ryan Langerhans - - - 30 9 0.331 0.300 0.06 0.08 -0.022 Ryan Raburn - - - 30 9 0.309 0.300 0.06 0.08 -0.022 Damion Easley - - - 17 5 0.314 0.294 0.06 0.08 -0.022 Milton Bradley - - - 52 19 0.372 0.365 0.07 0.10 -0.021 Adam Kennedy - - - 84 25 0.330 0.298 0.06 0.08 -0.021 Edgar Renteria - - - 100 32 0.344 0.320 0.06 0.08 -0.021 Eric Young - - - 62 19 0.340 0.306 0.06 0.08 -0.021 Brendan Ryan - - - 70 17 0.292 0.243 0.04 0.06 -0.021 Alex Presley - - - 22 6 0.297 0.273 0.05 0.07 -0.021 Jay Payton - - - 36 11 0.323 0.306 0.06 0.08 -0.021 Chris Denorfia - - - 30 9 0.330 0.300 0.06 0.08 -0.020 Melky Cabrera - - - 87 27 0.336 0.310 0.06 0.08 -0.020 Troy Tulowitzki Rockies 26 10 0.372 0.385 0.08 0.10 -0.020 Derek Jeter Yankees 136 47 0.367 0.346 0.07 0.09 -0.020 Tony Graffanino - - - 36 11 0.335 0.306 0.06 0.08 -0.020 Chris Young - - - 44 13 0.312 0.295 0.06 0.08 -0.020 Royce Clayton - - - 106 28 0.308 0.264 0.05 0.07 -0.020 Ivan Rodriguez - - - 23 7 0.324 0.304 0.06 0.08 -0.020 Chris Coghlan - - - 26 8 0.339 0.308 0.06 0.08 -0.020 Nori Aoki - - - 70 22 0.350 0.314 0.06 0.08 -0.020 Desmond Jennings Rays 54 16 0.324 0.296 0.06 0.08 -0.020 Jose Macias - - - 29 7 0.281 0.241 0.04 0.06 -0.019 Kelly Johnson - - - 52 16 0.331 0.308 0.06 0.08 -0.019 Jack Wilson - - - 193 51 0.306 0.264 0.05 0.07 -0.019 Denard Span - - - 119 37 0.349 0.311 0.06 0.08 -0.019 Jason Bartlett - - - 59 17 0.334 0.288 0.06 0.08 -0.019 Elvis Andrus Rangers 174 48 0.330 0.276 0.05 0.07 -0.019 Omar Infante - - - 116 32 0.313 0.276 0.05 0.07 -0.018 Aaron Miles - - - 87 23 0.317 0.264 0.05 0.07 -0.018 Alex Cora - - - 73 19 0.313 0.260 0.05 0.07 -0.018 Alexei Ramirez White Sox 72 20 0.312 0.278 0.05 0.07 -0.018 Brett Gardner Yankees 109 33 0.343 0.303 0.06 0.08 -0.018 Jed Lowrie - - - 17 5 0.329 0.294 0.06 0.08 -0.018 Josh Barfield - - - 12 3 0.294 0.250 0.05 0.07 -0.018 Gabe Kapler - - - 25 7 0.318 0.280 0.06 0.07 -0.018 Josh Harrison Pirates 32 9 0.310 0.281 0.06 0.07 -0.017 Jeff DaVanon - - - 51 16 0.354 0.314 0.07 0.08 -0.017 Asdrubal Cabrera - - - 104 30 0.327 0.288 0.06 0.08 -0.017 Ryan Theriot - - - 94 26 0.338 0.277 0.06 0.07 -0.017 Carl Crawford - - - 97 29 0.331 0.299 0.06 0.08 -0.016 Brad Wilkerson - - - 53 17 0.350 0.321 0.07 0.08 -0.016 Sam Fuld - - - 36 9 0.314 0.250 0.05 0.07 -0.016 Chris Burke - - - 47 12 0.312 0.255 0.05 0.07 -0.016 Willie Harris - - - 101 27 0.328 0.267 0.05 0.07 -0.016 Clint Barmes - - - 91 22 0.292 0.242 0.05 0.06 -0.015 Dustin Ackley Mariners 12 3 0.308 0.250 0.05 0.07 -0.015 Chase Headley - - - 10 3 0.347 0.300 0.06 0.08 -0.015 Jeff Cirillo - - - 48 12 0.316 0.250 0.05 0.07 -0.014 Steve Finley - - - 37 11 0.330 0.297 0.06 0.08 -0.014 Craig Biggio Astros 64 18 0.321 0.281 0.06 0.07 -0.014 Luis Rivas - - - 50 12 0.294 0.240 0.05 0.06 -0.014 Ramon Santiago - - - 189 45 0.309 0.238 0.05 0.06 -0.014 Mark Ellis - - - 97 26 0.325 0.268 0.06 0.07 -0.014 Ian Kinsler - - - 78 24 0.342 0.308 0.07 0.08 -0.014 Jose Hernandez - - - 11 3 0.321 0.273 0.06 0.07 -0.014 Dioner Navarro - - - 44 11 0.311 0.250 0.05 0.07 -0.014 Koyie Hill - - - 27 5 0.265 0.185 0.04 0.05 -0.013 Jonathan Herrera - - - 63 15 0.319 0.238 0.05 0.06 -0.013 Mark Loretta - - - 53 16 0.361 0.302 0.07 0.08 -0.013 Miguel Cairo - - - 77 18 0.303 0.234 0.05 0.06 -0.012 Jose Valentin - - - 41 11 0.308 0.268 0.06 0.07 -0.012 Johnny Damon - - - 68 21 0.355 0.309 0.07 0.08 -0.012 Timo Perez - - - 78 19 0.308 0.244 0.05 0.06 -0.012 Jason Varitek Red Sox 24 7 0.342 0.292 0.07 0.08 -0.012 Jeremy Hermida - - - 18 5 0.334 0.278 0.06 0.07 -0.011 Ty Wigginton - - - 11 3 0.322 0.273 0.06 0.07 -0.011 Dexter Fowler - - - 92 28 0.364 0.304 0.07 0.08 -0.010 Sean Burroughs - - - 16 4 0.334 0.250 0.06 0.07 -0.010 Chone Figgins - - - 204 54 0.348 0.265 0.06 0.07 -0.010 Juan Encarnacion - - - 34 9 0.320 0.265 0.06 0.07 -0.009 Aaron Rowand - - - 59 16 0.327 0.271 0.06 0.07 -0.009 Jason Michaels - - - 15 4 0.335 0.267 0.06 0.07 -0.009 Omar Vizquel - - - 167 39 0.327 0.234 0.05 0.06 -0.008 Einar Diaz - - - 28 5 0.275 0.179 0.04 0.05 -0.008 Cristian Guzman - - - 113 25 0.308 0.221 0.05 0.06 -0.006 Lew Ford - - - 19 5 0.344 0.263 0.06 0.07 -0.006 Franklin Gutierrez - - - 50 11 0.304 0.220 0.05 0.06 -0.006 Reggie Willits Angels 64 15 0.354 0.234 0.06 0.06 -0.006 Rob Mackowiak - - - 16 4 0.332 0.250 0.06 0.07 -0.006 David Eckstein - - - 174 42 0.341 0.241 0.06 0.06 -0.006 Adam LaRoche - - - 18 5 0.339 0.278 0.07 0.07 -0.005 Bill Hall - - - 38 9 0.307 0.237 0.06 0.06 -0.005 Gaby Sanchez - - - 12 3 0.331 0.250 0.06 0.07 -0.005 Daniel Murphy Mets 12 3 0.332 0.250 0.06 0.07 -0.005 Alberto Gonzalez - - - 12 2 0.274 0.167 0.04 0.04 -0.005 Jody Gerut - - - 16 4 0.325 0.250 0.06 0.07 -0.005 Ryan Freel - - - 70 18 0.354 0.257 0.06 0.07 -0.005 DJ LeMahieu - - - 24 5 0.312 0.208 0.05 0.05 -0.004 Chad Moeller - - - 21 4 0.288 0.190 0.05 0.05 -0.004 Ruben Tejada Mets 34 7 0.326 0.206 0.05 0.05 -0.004 J.D. Drew - - - 22 7 0.382 0.318 0.08 0.08 -0.003 Curtis Granderson - - - 66 18 0.337 0.273 0.07 0.07 -0.003 Orlando Cabrera - - - 77 17 0.320 0.221 0.05 0.06 -0.003 John Baker - - - 14 3 0.329 0.214 0.05 0.06 -0.003 Ramon Vazquez - - - 69 15 0.329 0.217 0.05 0.06 -0.003 Kaz Matsui - - - 101 22 0.319 0.218 0.05 0.06 -0.003 Aaron Boone - - - 40 9 0.316 0.225 0.06 0.06 -0.002 Jayson Nix - - - 23 4 0.280 0.174 0.04 0.05 -0.002 Shane Victorino - - - 103 26 0.339 0.252 0.06 0.07 -0.002 Mark McLemore - - - 17 4 0.352 0.235 0.06 0.06 -0.002 Adam Everett - - - 139 25 0.292 0.180 0.05 0.05 -0.002 Cory Sullivan - - - 69 15 0.325 0.217 0.06 0.06 -0.001 Alex Gonzalez - - - 25 5 0.296 0.200 0.05 0.05 -0.001 Ray Durham - - - 41 11 0.355 0.268 0.07 0.07 -0.001 Nate McLouth - - - 73 17 0.331 0.233 0.06 0.06 -0.001

The chart below is a live version of the table above. It allows you to click into the cells and visually see how the calculations were made. The chart above is more user friendly, but this gets into the finer details.

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Bunts: Bunt attempts

Bunt Hits: Hits on bunts

RA%: Chance that a positive offensive event occurs, outside of bunt hit

BA%: Chance that a player gets a hit on a bunt

xRA: Expected value added from a regular at bat

xBA: Expected value added from a bunt attempt

Net Value: xRA minus xBA

Implications

This research doesn’t mean to suggest that all players who have a higher expected value added on a bunt attempt than they do in a regular at bat should bunt every time. Carlos Santana gets a hit in 78% of the at bats where he bunts, but he has only attempted 14 bunts in his career, so we don’t have a large enough sample of bunt attempts to know what his actual average on bunt attempts would be; this goes for most if not all of the players on this list. There is most likely an inverse correlation between BA% and bunt attempts (i.e. the more you try and bunt for a hit, the less likely you will get a hit as the infield plays further up on the grass).

This research means to suggest that players have not reached the equilibrium for bunt attempts (i.e. they haven’t maximized their value). Players should increase the percentage of the time they bunt until their xRA and xBA are the same; at this point their value will be maximized. The more a player with a negative net value tries to bunt for a hit, the more expected value he will add. This will happen until his expected value added from a bunt falls beneath what he is able to achieve through a regular at bat; this happens when the defense starts to defend him more optimally, they align for the bunt hit, and his BH% falls. Once this occurs he will force the defense to play more honestly—the infielders will have to play farther in on the grass—and increase his expected value added in a regular at bat as more balls get past the infield from shallow play.

What’s interesting is that there are two different types of players on this list. The first type of player is the type that you would traditionally think of as player who would try and bunt for a hit: the speedster with very little power. The second type of player is the player who, as a result of the recent, extensive use of defensive shifts, has a high BA%—batting average on bunt hits—because the defense is not in a position to cover a bunt efficiently: Carlos Santana, Carlos Pena, Colby Rasmus, etc.

The voice for the question about why players don’t try to beat the shift with bunts down the third base line has grown louder, but there still hasn’t been a good answer as to why it hasn’t been done more; the evidence seems to suggest that it is valuable and should be done more. I’m not able to confirm that the 11 hits that Carlos Santana had on bunt hits came when the defense was in a shift, but I think it would be somewhat unreasonable to believe that he was able to beat out a throw to first on a bunt hit attempt when the defense was in a traditional alignment more than a few times.

The image above is a spray chart of Carlos Santana’s ground ball distribution as a left-handed hitter; the white dots are hits and the red dots are outs. This chart suggests that it would be advantageous for teams to shift against Santana when he bats left-handed. I would argue that because of Santana’s success—his high BH%—at bunting for a hit, he should do this more, which will generate more value by itself, and increase the value generated in regular at bats as he forces the defense to change their defensive shift against him from the increase in bunt attempts. However, once he reaches the equilibrium, any further changes may ultimately be a zero sum game.

There are no silver bullets to get more runners on base, but there will always be more efficient, undervalued ways to achieve that goal. This research has proven that bunting for a hit is underutilized, and once more work is done to tease out sac bunts from a player’s bunt hit attempts and calculate an accurate BH%, along with the generation of linear weight values for a bunt hit, we will have a more definitive answer for what a bunt hit is worth.

Credit for the Carlos Santana photo goes to Keith Allison