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Laying It Down, Part 1

I've been thinking a lot about bunting recently.  Considering all of the information we have about offensive statistics, it's surprising to me that we don't have more data on bunting readily available - Fangraphs has data for bunt hits and sacrifice bunts, but it's much more difficult to find information for bunts that aren't hit into play.  Certainly, one of the most important aspects of being a good bunter is being able to consistently get the ball in play, so I believe that it's just as important to look at foul and missed bunts as it is to look at fair bunts.  Combining the Fangraphs bunting splits leaderboard with PITCHf/x data can give us a more detailed look at who the league's better and worse bunters are. 

There are three main things that I was able to quantify with the data I was working with - the frequency with which a batter attempts to lay down a bunt, the frequency of bunts put into play, and the overall quality of the bunt.  Before I get more in depth on the metrics I've been working with, I think it would be best to show some generic bunting benchmarks for the 2010 season.


Attempt% Fair% Foul% Missed%
League Average .019 .505 .416 .080


Hit% Out% Sac% Double Play%
League Average .188 .286 .517 .009

For the first table: attempt% is the number of bunt attempts (fair bunts, foul bunts, missed bunts) divided by the total number of swings; fair% is the number of fair bunts divided by bunt attempts; foul% is the number of foul bunts divided by bunt attempts; missed% is the number of missed bunts divided by bunt attempts.  For the second table: hit% is the number of bunt hits divided by fair bunts; out% is the number of bunt outs (in which a sacrifice is not involved) divided by fair bunts; sac% is the number of sacrifice bunts divided by fair bunts; double play% is the number of bunt double plays (there are very few of these) divided by fair bunts.  As you can see by the attempt percentage, it's not that common that a hitter decides to try to lay down a bunt - on average, only three or four pitches in a game result in a bunt attempt.  For the 2010 season, which is the data set that I'll be working with for this post, I have a total of 5,921 bunt attempts.


Who attempts to bunt the most?

The first metric I'd like to dig deeper into is attempt percentage - basically, which players attempt to bunt the most?  The distribution below shows the attempt percentages for 573 hitters who took at least 75 swings last year.  


Of the qualified hitters, there were 138 without a single bunt attempt, and a few more with an attempt percentage between 0% and 1%.  Approximately 90% of the qualified players had rates under 10%, and the vast majority of the players over the number were pitchers, with Tim Lincecum (.252), Aaron Cook (.247), Livan Hernandez (.234), Zach Duke (.223), and Dave Bush (.218) leading the charge.  Raising the minimum number of swings to 200 (and thus eliminating pitchers) gives us these leaders:


Rank Name Team Attempt%
1 Carlos Gomez Brewers .107
2 Julio Borbon Rangers .105
3 Peter Bourjos Angels .102
4 Juan Pierre White Sox .098
5 Erick Aybar Angels .096
6 Nyjer Morgan Nationals .095
7 Emilio Bonifacio Marlins .093
8 Luis Castillo Mets .083
9 Gregor Blanco Braves/Royals .081
10 Everth Cabrera Padres .078

On a counting level, Juan Pierre was the clear champion of bunt attempts with 111; he was the only player with more than 100.  Next closest were Nyjer Morgan and Erick Aybar with 95 and 94, respectively. 

One more thing on attempt percentages.  Common baseball sense would tell us that the guys who are less offensively adept would be the players resorting to bunting most often.  Based on these data, that would appear to be the case.  Keeping the 200 swing minimum, here is attempt percentage plotted against linear weight runs per 100 pitches (the numbers aren't exact, so don't consider 0 to be the exact 2010 league average).



For the most part, the frequent bunters are to the "below-average" side of the chart.  If you were wondering, the outlier with a bunt percentage of just over 6% and and an 0.69 runs per 100 is the Tigers' Will Rhymes.


Who gets it in fair territory?

As I showed in the benchmarks, the league average rate for fair bunts was just over 50%.  For the purpose of looking at season leaders and trailers, I've set a minimum of 20 bunt attempts, which leaves 70 bunters from last year.  The tables below show the fair bunt rates for the 10 leaders and trailers.


Rank Name Team Attempts Fair%
1 Scott Podsednik Royals/Dodgers 33 .818
2 Ramon Santiago Tigers 27 .778
3 Will Rhymes Tigers 20 .750
4 Daric Barton Athletics 20 .750
5 Clayton Kershaw Dodgers 27 .741
6 Ryan Dempster Cubs 26 .731
7 Barry Zito Giants 22 .727
8 Alexi Casilla Twins 20 .700
9 Tony Gwynn Padres 30 .700
10 Elvis Andrus Rangers 56 .679

1x Roy Halladay Phillies 22 .136
2x B.J. Upton Rays 23 .174
3x Rajai Davis Athletics 36 .222
4x Alcides Escobar Brewers 37 .270
5x Chris Coghlan Marlins 29 .276
6x Mike Leake Reds 22 .318
7x Jon Garland Padres 22 .364
8x Anibal Sanchez Marlins 22 .364
9x Nick Punto Twins 27 .370
10x Orlando Hudson Twins 27 .370

Since this metric only judges whether the bunt was in play or not, its best use is probably to determine which players would be good in sacrifice situations.  There are plenty of pitchers sprinkled throughout the list (including three in the top 10 and four in the bottom 10), and the pitcher's role is almost exclusively to sacrifice.  


Who does the most damage?


But what about what happens once the ball is in play?  There are a number of possible ways to quantify this; Fangraphs has bunt average, which is a good way to show how productive non-sacrifice bunts were.  In this post, I will use hits over all fair bunts as opposed to non-sacrifices (detailed in the glossary).  Below are the 10 leaders for hit%, out%, and sac%, with 10 fair bunts as the minimum. 


Rank Name Team Fair Bunts Hit%
1 Adam Jones Orioles 12 .583
2 Gregor Blanco Braves/Royals 21 .571
3 Cameron Maybin Marlins 11 .545
4 Jose Reyes Mets 17 .529
5 Ichiro Suzuki Mariners 14 .500
6 Angel Pagan Mets 25 .480
7 Ben Zobrist Rays 15 .467
8 Kevin Fransden Angels 11 .455
9 Sean Rodriguez Rays 16 .438
10 Cesar Izturis Orioles 17 .412


Rank Name Team Fair Bunts Out%
1 Emilio Bonifacio Marlins 14 .786
2 Koyie Hill Cubs 10 .600
3 Rafael Furcal Dodgers 11 .545
4 Reggie Willits Angels 13 .538
5 Trevor Crowe Indians 13 .538
6 Roger Bernadina Nationals 17 .529
7 Juan Pierre White Sox 55 .527
8 Michael Saunders Mariners 14 .500
9 Drew Stubbs Reds 14 .500
10 Orlando Hudson Twins 10 .500


Rank  Name Team Fair Bunts Sac%
1 Darnell McDonald Red Sox 13 .923
2 Brett Myers Astros 12 .917
3 Clayton Kershaw Dodgers 20 .900
4 Barry Zito Giants 16 .875
5 Roy Oswalt Astros/Phillies 13 .846
6 Ryan Dempster Cubs 19 .842
7 Bud Norris Astros 12 .833
8 Chris Carpenter Cardinals 12 .833
9 Chris Volstad Marlins 11 .818
t-10 Mat Latos Padres 11 .818
t-10 Wandy Rodriguez Astros 11 .818
t-10 Livan Hernandez Nationals 11 .818


The sacrifice column is interesting because it is composed entirely of pitchers except for the leader, Red Sox outfielder Darnell McDonald. 

The last thing I'd like to look at in this post is a way to tie in all of the facets of bunt attempts into one metric.  Using run values is typically the best way to do this.  For bunts in play, I'm using the following weights (with "0" representing a neutral outcome):

Bunt Double Play - -0.78
Bunt Out - -0.28
Sac Bunt - -0.03
Bunt Single - +0.50
Bunt Double - +0.83 (there was only one bunt double last year, courtesy of Cliff Pennington)

In the overall value, I'll also include failed bunt attempts.  The run values for these pitches are dependent on the count and are similar to the ones shown here.  

There will be two sets of leaders and trailers for this metric, which for now I'll refer to as bunting runs - there's bunting runs as a counting stat, and there's bunting runs per 100 attempts.  I don't really like using 100 because it doesn't really have much meaning when it comes to bunting, but it's a nice, round number and is commonly used for rate stats.  According to my numbers, the league average bunt runs/100 in 2010 was -3.46 (-2.95 for bunts not in play and -0.51 for bunts in play), which would mean that overall, attempting to bunt leads to a below-average outcome.  

The top table shows bunting runs, and the bottom table shows bunting runs / 100 (minimum 20 attempts for both).  Both lists include pretty much the same players, but I've included both metrics anyway.

Rank Name Team Bunt Runs
1 Gregor Blanco Braves/Royals 3.27
2 Angel Pagan Mets 2.76
3 Ben Zobrist Rays 2.26
4 Adam Jones Orioles 2.00
5 Elvis Andrus Rangers 1.99
6 Jose Reyes Mets 1.93
7 Cesar Izturis Orioles 1.59
8 Julio Borbon Rangers 1.56
9 Ichiro Suzuki Mariners 1.55
10 Cliff Pennington Athletics 1.19

1x Juan Pierre White Sox -6.27
2x Nyjer Morgan Nationals -3.92
3x Chone Figgins Mariners -3.79
4x Joe Blanton Phillies -3.48
5x Denard Span Twins -3.06
6x Emilio Bonifacio Marlins -3.04
7x Livan Hernandez Nationals -2.89
8x Mike Pelfrey Mets -2.81
9x Derek Lowe Braves -2.56
10x Orlando Hudson Twins -2.47

Rank Name Team Bunt Runs / 100
1 Gregor Blanco Braves/Royals 9.35
2 Adam Jones Orioles 8.35
3 Ben Zobrist Rays 7.52
4 Angel Pagan Mets 5.75
5 Ichiro Suzuki Mariners 5.73
6 Jose Reyes Mets 4.71
7 Cesar Izturis Orioles 4.55
8 Cameron Maybin Marlins 3.66
9 Elvis Andrus Rangers 3.56
10 Alexi Casilla Twins 3.39

1x Joe Blanton Phillies -15.82
2x Mike Pelfrey Mets -12.79
3x Zach Duke Pirates -11.54
4x Derek Lowe Braves -11.13
5x Livan Hernandez Nationals -9.97
6x Trevor Crowe Indians -9.87
7x Hiroki Kuroda Dodgers -9.40
8x Orlando Hudson Twins -9.13
9x Roy Halladay Phillies -8.99
10x Emilio Bonifacio Marlins -8.94

I think it's fair to say that Gregor Blanco was the majors' best bunter in 2010.  As notable is Juan Pierre's number of bunting runs, which shows that lots of mediocre bunting might not be a good idea.  

Hopefully, this post provided some answers about bunting; in addition, it certainly raises some more questions.  Next week, I will expand to data from 2008 and 2009 in order to look at larger sample sizes and year-to-year correlations.   


Fair bunt data are from Fangraphs.  Foul and missed bunt data were generated from Joe Lefkowitz's PITCHf/x tool.