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. 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 |

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 - -0.03Bunt 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 should be 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.

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.