## Clustering Pitchers With PITCHf/x

At any point, feel free to scroll down to the bottom to see some of the tables of pitcher clusters.

## Clustering Pitches

Clustering individual pitches using data from PITCHf/x is a fairly simple task. All you need to do is pick out the important attributes that you believe define a pitch (velocity, movement, etc.) and use a clustering algorithm, such as K-Means clustering.

With K-Means clustering, you decide what K (the number of clusters) should be. For my analysis, I chose K to be 500 (rather arbitrarily). Different pitch clusters can represent the same type of pitch (i.e. fastball) but with varying attributes. For example, clusters 50 and 100 might both correspond to fastballs, but cluster 50 might be a typical Chris Young fastball whereas cluster 100 might be a typical Aroldis Chapman fastball.

One important point to remember is that you, the analyst, must decide what the clusters represent. By looking at attributes of the pitches in a given cluster, you might identity the cluster as “lefty changeups” or “submariner fastballs” (which is actually a category you will discover).

## The Problem of Clustering Pitchers

We can identify every pitch that a pitcher throws as belonging to a cluster from 1 to 500. Therefore, we know the distribution of pitch clusters for a given pitcher. The difficult problem, however, is how do we compare two pitchers using this information? Let’s say we have two pitchers:

• Pitcher A’s pitches are 50% from cluster 1 and 50% from cluster 200.
• Pitcher B’s pitches are 33% from cluster 1, 33% from cluster 300, and 33% from cluster 139.

The question remains, are Pitcher A and Pitcher B similar pitchers?

The problem of clustering pitchers is a more complicated one than clustering pitches because we now have a collection of pitches instead of just individual pitches to compare. In order to cluster pitchers, I use a model that is typically used for topic modeling called Latent Dirichlet Allocation (LDA).

## An Aside on LDA

In LDA for topic modeling, our data is a collection of documents.

Let’s imagine that our collection of documents is articles from the New York Times. There are global topics that govern how these articles are generated. For example, if you think of a newspaper, the topics might be sports, finance, health, politics, etc. Additionally, each article can be a mixture of these topics. We might imagine there is an article in the sports section titled, “Yankees payroll exceeds \$300 million”, which our algorithm may discover is 50% about sports and 50% about finance.

Similar to what is mentioned above, the analyst must figure out what the topics actually are. You do not tell the algorithm that there is a sports topic. You discover that the topic is sports by observing that the most probable words are “baseball”, “Jeter”, “LeBron”, “touchdown”, etc. The algorithm will tell you that a particular document is 50% about topic 1 and 50% about topic 20, but you must ultimately infer what topics 1 and topics 20 are.

I am harping on this point mainly just to mention that there is no magic to these clustering algorithms. An algorithm can cluster data, but it cannot tell you what these clusters mean.

## Relevance of LDA to Pitchers

Anyway, how can this model be used to analyze pitchers? We just need to use our imagination. Instead of a collection of documents, we now have a collection of pitcher seasons. Whereas each document is made up of a collection of words, each pitcher season is made up of a collection of pitches. We have already discretized each pitch using K-Means clustering in order to create our own “dictionary” of pitches. In our baseball model, we imagine that each pitcher is a mixture of repertoires, whereas in topic modeling, each document was a mixture of topics. We can then cluster pitchers together by figuring out who has the most similar repertoires.

## Nitty Gritty Details

If you are not interested in getting into the nitty gritty details, feel free to skip ahead to the next section to just see the cluster groupings.

• Data used is from 2007-2014.
• The dictionary of pitches (500 clusters) was created by running K-Means using all of the pitches from 2014. The choice of 2014 is arbitrary, but I used just one year’s worth of data because I thought it might be a sufficient amount and it was much quicker to run K-Means.
• The PITCHf/x attributes that were used to cluster pitches were start_speed, pfx_x/pfx_z (horizontal/vertical movement), px/pz (horizontal/vertical location), vx0/vz0 (components of velocity).
• For each pitcher from 2007-2014, each pitch was assigned to its closest cluster (determined by distance to the cluster center). I filtered out pitcher seasons in which the pitcher threw fewer than 500 pitches.
• I then ran LDA on pitcher seasons, choosing the number of repertoires (topics) to be 5.
• I used the method from this paper to get a vector representation of each pitcher season. I could have used the inferred repertoire proportions as my vector representations, but for various reasons, this did not produce as nice of clusters.
• Finally, I ran K-Means (K=100) on these vectors to get clusters of pitchers.
• Whereas in topic modeling, it is often interesting to interpret what the global topics actually are, I am not really interested in what the global “repertoires” are for the model. I am really using LDA as a dimensionality reduction technique to produce smaller vectors (5 vs. 500) that can be clustered together.

## Some Observations

The actual clusters along with some relevant FanGraphs statistics are provided below. Each table is sortable. For brevity, I have only included clusters in which there are 10 or fewer pitchers. Only the first cluster shown (cluster 3) has more than 10 pitchers, which I simply included to demonstrate that a cluster could be quite big.

• As is probably expected, clusters are almost always entirely righties or lefties even though this is not an input to the model.
• Guys with similar numbers of batters faced cluster together. This is by design, as the way I determined the repertoire proportions accounts for the number of times a particular pitch is thrown.
• Sometimes weird clusters can form, such as Cluster 37, which contains both Chapman and Wakefield. Cluster 37 is mostly cohesive with hard-throwing left-handers and I believe Wakefield ends up here simply because he did not fit well into any cluster.
• This is not to say that the algorithm cannot find clusters of knuckleballers. Cluster 14 is all R.A. Dickey from years 2011-2014.
• There are also other clusters that contain exclusively one (or almost one) pitcher. Cluster 8 is 5 Kershaw years and one Hamels year. Cluster 68 is 5 Verlander years. I believe these clusters form partially because their stuff is so good. There are other pitchers who fall into almost exclusively one cluster but who are joined by many other pitchers. Another factor is that they might be able to repeat their mechanics so well that they remain in the same cluster because they are always throwing the same pitch types.
• Clusters of individual pitchers also happens if a pitcher has an incredibly unique style. Justin Masterson has his own cluster because he is such an extreme ground-ball pitcher. Josh Collmenter does as well due to the extreme rise he generates on his “fastball”.
• Cluster 29 contains just Kershaw’s 2014 season and J.A. Happ’s 2009 season. If you do a Ctrl-F for J.A. Happ, he finds himself in some pretty flattering clusters. This is especially interesting because from 2007-2014, he does not have particularly good seasons, but he has been quite good the last two years. This is not to suggest that these clusters can uncover hidden gems, but it’s not fully out of the realm of possibility.
• Most clusters produce quite similar ground-ball percentages. One of the factors that goes into clustering pitches (and therefore pitchers) is horizontal and vertical movement, which play a huge factor in a pitcher’s ability to produce ground-balls.
• Submarine pitchers always end up together. Check out Clusters 9, 60, and 92.

Overall, I think this is pretty interesting stuff. I was honestly surprised that the clusters turned out to be as cohesive as they were. Additionally, besides being a descriptive tool, I have to wonder whether this information can be used for predictive purposes. For example, we often talk about regression to the mean when discussing a player’s performance, whether it be a pitcher of a batter. It is possible that the appropriate mean for many pitchers is the cluster mean that they happen to fall into.

Cluster 3

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2009 Chris Carpenter Cardinals 750 6.73 1.78 0.33 55.0 28.0 4.6 5.5
2010 Hiroki Kuroda Dodgers 810 7.29 2.20 0.69 51.1 32.1 8.0 4.3
2010 Gavin Floyd White Sox 798 7.25 2.79 0.67 49.9 32.1 7.6 4.1
2008 Hiroki Kuroda Dodgers 776 5.69 2.06 0.64 51.3 28.6 7.6 3.6
2012 Doug Fister Tigers 673 7.63 2.06 0.84 51.0 26.7 11.6 3.4
2011 Josh Beckett Red Sox 767 8.16 2.42 0.98 40.1 42.2 9.6 3.3
2011 Michael Pineda Mariners 696 9.11 2.89 0.95 36.3 44.8 9.0 3.2
2012 A.J. Burnett Pirates 851 8.01 2.76 0.80 56.9 24.3 12.7 3.0
2013 Rick Porcello Tigers 736 7.22 2.14 0.92 55.3 23.7 14.1 2.9
2008 Carlos Zambrano Cubs 796 6.20 3.43 0.86 47.2 34.9 9.0 2.8
2013 Andrew Cashner Padres 707 6.58 2.42 0.62 52.5 28.7 8.1 2.7
2012 Jeff Samardzija Cubs 723 9.27 2.89 1.03 44.6 33.1 12.8 2.7
2010 Scott Baker Twins 725 7.82 2.27 1.22 35.6 43.5 10.2 2.6
2014 Kyle Gibson Twins 757 5.37 2.86 0.60 54.4 26.6 7.8 2.3
2012 Tim Hudson Braves 749 5.13 2.41 0.60 55.5 25.2 8.3 2.1
2014 Henderson Alvarez Marlins 772 5.34 1.59 0.67 53.8 24.3 9.5 2.1
2008 Todd Wellemeyer Cardinals 807 6.29 2.91 1.17 39.3 39.8 10.6 2.0
2010 Rick Porcello Tigers 700 4.65 2.10 1.00 50.3 32.1 9.9 1.7
2011 Luke Hochevar Royals 835 5.82 2.82 1.05 49.8 32.2 11.5 1.7
2008 Jason Marquis Cubs 738 4.90 3.77 0.81 47.6 32.5 8.3 1.7
2014 Charlie Morton Pirates 666 7.21 3.26 0.51 55.7 22.8 8.8 1.6
2012 Luis Mendoza Royals 709 5.64 3.20 0.81 52.1 27.1 10.6 1.5
2009 Aaron Cook Rockies 675 4.44 2.68 1.08 56.5 24.7 14.2 1.4
2014 Doug Fister Nationals 662 5.38 1.32 0.99 48.9 34.2 10.1 1.4
2010 Mitch Talbot Indians 696 4.97 3.90 0.73 47.8 35.3 7.0 1.2
2008 Armando Galarraga Tigers 746 6.35 3.07 1.41 43.5 39.7 13.0 1.2
2008 Carlos Silva Mariners 689 4.05 1.88 1.17 44.0 33.3 10.4 1.2
2009 Ross Ohlendorf Pirates 725 5.55 2.70 1.27 40.6 42.1 11.1 1.2
2008 Vicente Padilla Rangers 757 6.68 3.42 1.37 42.7 38.1 12.5 1.1
2012 Luke Hochevar Royals 800 6.99 2.96 1.31 43.3 35.0 13.5 1.1
2012 Derek Lowe – – – 640 3.47 3.22 0.63 59.2 21.0 9.1 1.0
2013 Edinson Volquez – – – 777 7.50 4.07 1.00 47.6 29.6 11.9 0.9
2011 Chris Volstad Marlins 719 6.36 2.66 1.25 52.3 27.7 15.5 0.7
2010 Jeremy Bonderman Tigers 754 5.89 3.16 1.32 44.7 39.2 11.4 0.7
2010 Brad Bergesen Orioles 746 4.29 2.70 1.38 48.7 36.6 11.9 0.6
2014 Hector Noesi – – – 733 6.42 2.92 1.46 38.0 40.6 12.7 0.3
2009 Armando Galarraga Tigers 642 5.95 4.20 1.50 39.9 38.6 13.3 0.2
2008 Kyle Kendrick Phillies 722 3.93 3.30 1.33 44.3 28.7 14.0 0.1
2014 Roberto Hernandez – – – 722 5.74 3.99 1.04 49.7 29.9 12.2 0.0
2013 Lucas Harrell Astros 707 5.21 5.15 1.17 51.5 27.4 14.3 -0.8

Cluster 5

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2010 Cliff Lee – – – 843 7.84 0.76 0.68 41.9 40.4 6.3 7.0
2011 Cliff Lee Phillies 920 9.21 1.62 0.70 46.3 32.4 9.0 6.8
2009 Jon Lester Red Sox 843 9.96 2.83 0.89 47.7 34.5 10.6 5.3
2014 Jose Quintana White Sox 830 8.00 2.34 0.45 44.7 33.2 5.1 5.1
2013 Derek Holland Rangers 894 7.99 2.70 0.85 40.8 36.4 8.8 4.3
2012 Matt Moore Rays 759 8.88 4.11 0.91 37.4 42.9 8.6 2.7
2013 Wade Miley Diamondbacks 847 6.53 2.93 0.93 52.0 27.2 12.5 1.8

Cluster 6

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2007 CC Sabathia Indians 975 7.80 1.38 0.75 45.0 36.6 7.8 6.4
2014 Jake McGee Rays 274 11.36 2.02 0.25 38.0 42.9 2.9 2.6
2014 Tyler Matzek Rockies 503 6.96 3.37 0.69 49.7 30.3 8.3 1.7
2013 J.A. Happ Blue Jays 415 7.48 4.37 0.97 36.5 46.0 7.6 1.1
2010 J.A. Happ – – – 374 7.21 4.84 0.82 39.0 43.4 7.4 1.0
2009 Sean West Marlins 467 6.10 3.83 0.96 40.2 40.8 8.0 1.0
2009 Andrew Miller Marlins 366 6.64 4.84 0.79 48.0 30.0 9.3 0.7
2012 Drew Pomeranz Rockies 434 7.73 4.28 1.30 43.9 35.9 13.6 0.7
2013 Jake McGee Rays 260 10.77 3.16 1.15 42.5 38.8 12.9 0.6
2008 Jo-Jo Reyes Braves 512 6.21 4.14 1.43 48.5 31.8 15.5 0.2

Cluster 8

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2013 Clayton Kershaw Dodgers 908 8.85 1.98 0.42 46.0 31.3 5.8 7.1
2011 Clayton Kershaw Dodgers 912 9.57 2.08 0.58 43.2 38.6 6.7 7.1
2012 Clayton Kershaw Dodgers 901 9.05 2.49 0.63 46.9 34.0 8.1 5.9
2010 Clayton Kershaw Dodgers 848 9.34 3.57 0.57 40.1 42.1 5.8 4.7
2009 Clayton Kershaw Dodgers 701 9.74 4.79 0.37 39.4 41.6 4.1 4.4
2010 Cole Hamels Phillies 856 9.10 2.63 1.12 45.4 37.9 12.3 3.5

Cluster 9

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2009 Peter Moylan Braves 309 7.52 4.32 0.00 62.4 19.5 0.0 1.4
2014 Joe Smith Angels 285 8.20 1.81 0.48 59.1 25.9 8.0 1.0
2011 Joe Smith Indians 267 6.04 2.82 0.13 56.6 23.5 2.2 1.0
2009 Brad Ziegler Athletics 313 6.63 3.44 0.25 62.3 19.7 4.4 1.0
2013 Brad Ziegler Diamondbacks 297 5.42 2.71 0.37 70.4 10.8 12.5 0.6
2012 Brad Ziegler Diamondbacks 263 5.50 2.75 0.26 75.5 7.7 13.3 0.6
2012 Joe Smith Indians 278 7.12 3.36 0.54 58.0 24.9 8.3 0.6
2008 Cla Meredith Padres 302 6.27 3.07 0.77 66.8 17.3 15.8 0.3
2010 Peter Moylan Braves 271 7.35 5.23 0.71 67.8 21.3 13.5 -0.3

Cluster 14

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2012 R.A. Dickey Mets 927 8.86 2.08 0.92 46.1 34.1 11.3 5.0
2011 R.A. Dickey Mets 876 5.78 2.33 0.78 50.8 32.9 8.3 2.5
2014 R.A. Dickey Blue Jays 914 7.22 3.09 1.09 42.0 37.6 10.7 1.7
2013 R.A. Dickey Blue Jays 943 7.09 2.84 1.40 40.3 40.5 12.7 1.7

Cluster 16

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2013 Max Scherzer Tigers 836 10.08 2.35 0.76 36.3 44.6 7.6 6.1
2014 Max Scherzer Tigers 904 10.29 2.57 0.74 36.7 41.6 7.5 5.2
2011 Daniel Hudson Diamondbacks 921 6.85 2.03 0.69 41.7 39.1 6.4 4.6
2012 Max Scherzer Tigers 787 11.08 2.88 1.10 36.5 41.5 11.6 4.4
2014 Jeff Samardzija – – – 879 8.28 1.76 0.82 50.2 30.5 10.6 4.1
2014 Lance Lynn Cardinals 866 8.00 3.18 0.57 44.3 36.0 6.1 3.4

Cluster 18

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2008 Brandon Webb Diamondbacks 944 7.27 2.58 0.52 64.4 20.4 9.6 5.5
2013 Justin Masterson Indians 803 9.09 3.54 0.61 58.0 24.2 10.7 3.5
2012 Justin Masterson Indians 906 6.94 3.84 0.79 55.7 25.0 11.4 2.3
2011 Derek Lowe Braves 830 6.59 3.37 0.67 59.0 22.5 10.2 2.1

Cluster 20

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2010 John Danks White Sox 878 6.85 2.96 0.76 45.4 38.9 7.4 4.4
2010 Brian Matusz Orioles 760 7.33 3.23 0.97 36.2 45.0 7.9 3.0
2009 John Danks White Sox 839 6.69 3.28 1.26 44.2 40.9 11.5 2.7
2013 Felix Doubront Red Sox 705 7.71 3.94 0.72 45.6 34.4 7.8 2.2
2014 J.A. Happ Blue Jays 673 7.58 2.91 1.25 40.6 39.5 11.5 1.0

Cluster 24

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2008 CC Sabathia – – – 1023 8.93 2.10 0.68 46.6 31.7 8.8 7.3
2011 CC Sabathia Yankees 985 8.72 2.31 0.64 46.6 30.3 8.4 6.4
2010 David Price Rays 861 8.11 3.41 0.65 43.7 39.6 6.5 4.2

Cluster 29

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2014 Clayton Kershaw Dodgers 749 10.85 1.41 0.41 51.8 29.2 6.6 7.6
2009 J.A. Happ Phillies 685 6.45 3.04 1.08 38.4 42.9 9.5 1.7

Cluster 35

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2014 Chris Young Mariners 688 5.89 3.27 1.42 22.3 58.7 8.8 0.1
2014 Marco Estrada Brewers 624 7.59 2.63 1.73 32.7 49.5 13.2 -0.1

Cluster 36

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2011 Justin Masterson Indians 908 6.58 2.71 0.46 55.1 26.7 6.3 4.2
2010 Justin Masterson Indians 802 7.00 3.65 0.70 59.9 24.9 10.0 2.3

Cluster 37

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2012 Aroldis Chapman Reds 276 15.32 2.89 0.50 37.3 42.9 7.4 3.3
2009 Matt Thornton White Sox 291 10.82 2.49 0.62 46.4 36.3 7.7 2.3
2008 Matt Thornton White Sox 268 10.29 2.54 0.67 53.0 27.4 10.9 1.7
2012 Drew Smyly Tigers 416 8.52 2.99 1.09 39.9 41.3 10.3 1.7
2008 Clayton Kershaw Dodgers 470 8.36 4.35 0.92 48.0 31.3 11.6 1.5
2008 Tim Wakefield Red Sox 754 5.82 2.98 1.24 35.5 48.9 9.1 1.1
2011 Tim Wakefield Red Sox 677 5.41 2.73 1.45 38.4 45.8 10.5 0.2

Cluster 38

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2013 Cliff Lee Phillies 876 8.97 1.29 0.89 44.3 33.3 10.9 5.5
2008 Johan Santana Mets 964 7.91 2.42 0.88 41.2 36.4 9.4 5.3
2010 Jon Lester Red Sox 861 9.74 3.59 0.61 53.6 29.6 8.9 4.8
2012 CC Sabathia Yankees 833 8.87 1.98 0.99 48.2 30.7 12.5 4.7
2008 Jon Lester Red Sox 874 6.50 2.82 0.60 47.5 31.6 7.0 4.1
2013 Hyun-Jin Ryu Dodgers 783 7.22 2.30 0.70 50.6 30.5 8.7 3.6
2014 Wei-Yin Chen Orioles 772 6.59 1.70 1.11 41.0 37.5 10.5 2.4
2010 Jonathan Sanchez Giants 812 9.54 4.47 0.98 41.5 43.7 9.8 2.3
2014 Wade Miley Diamondbacks 866 8.18 3.35 1.03 51.1 28.0 13.9 1.6

Cluster 44

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2011 Cole Hamels Phillies 850 8.08 1.83 0.79 52.3 32.6 9.9 4.9
2008 Cole Hamels Phillies 914 7.76 2.10 1.11 39.5 38.7 11.2 4.8
2008 John Danks White Sox 804 7.34 2.63 0.69 42.8 35.4 7.4 4.8
2009 Cole Hamels Phillies 814 7.81 2.00 1.12 40.4 38.7 10.7 3.9
2014 Danny Duffy Royals 606 6.81 3.19 0.72 35.8 46.0 6.1 1.9
2011 J.A. Happ Astros 698 7.71 4.78 1.21 33.0 44.2 10.2 0.6

Cluster 46

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2010 Roy Halladay Phillies 993 7.86 1.08 0.86 51.2 29.7 11.3 6.1
2013 Lance Lynn Cardinals 856 8.84 3.39 0.62 43.1 34.4 7.4 3.7
2008 Mike Pelfrey Mets 851 4.93 2.87 0.54 49.6 29.6 6.3 3.1
2009 A.J. Burnett Yankees 896 8.48 4.22 1.09 42.8 39.2 10.8 3.0
2010 Roberto Hernandez Indians 880 5.31 3.08 0.73 55.6 30.8 8.3 2.6
2009 Derek Lowe Braves 855 5.13 2.91 0.74 56.3 25.8 9.4 2.5
2010 Derek Lowe Braves 824 6.32 2.83 0.84 58.8 22.6 13.1 2.2

Cluster 49

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2014 Aroldis Chapman Reds 202 17.67 4.00 0.17 43.5 34.8 4.2 2.8
2014 James Paxton Mariners 303 7.18 3.53 0.36 54.8 22.6 6.4 1.2
2013 Rex Brothers Rockies 281 10.16 4.81 0.67 48.8 32.5 9.3 0.9
2012 Antonio Bastardo Phillies 224 14.02 4.50 1.21 27.7 50.0 12.5 0.8
2012 Tim Collins Royals 295 12.01 4.39 1.03 40.9 42.8 11.8 0.7
2012 Christian Friedrich Rockies 377 7.87 3.19 1.49 42.2 34.6 15.4 0.7
2013 Justin Wilson Pirates 295 7.21 3.42 0.49 53.0 30.0 6.7 0.6
2011 Aroldis Chapman Reds 207 12.78 7.38 0.36 52.7 30.8 7.1 0.5
2014 Justin Wilson Pirates 256 9.15 4.50 0.60 51.3 34.4 7.3 0.2
2011 Mike Dunn Marlins 267 9.71 4.43 1.29 38.5 46.0 12.2 -0.2

Cluster 51

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2009 Cliff Lee – – – 969 7.03 1.67 0.66 41.3 36.5 6.5 6.3
2009 CC Sabathia Yankees 938 7.71 2.62 0.70 42.9 37.3 7.4 5.9
2010 CC Sabathia Yankees 970 7.46 2.80 0.76 50.7 34.1 8.6 5.1

Cluster 54

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2014 Hisashi Iwakuma Mariners 709 7.74 1.06 1.01 50.2 28.7 13.2 3.1
2009 Justin Masterson – – – 568 8.28 4.18 0.84 53.6 31.4 10.4 1.5
2014 Justin Masterson – – – 592 8.11 4.83 0.84 58.2 21.6 14.6 0.4

Cluster 58

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2014 David Price – – – 1009 9.82 1.38 0.91 41.2 38.1 9.7 6.0
2014 Jon Lester – – – 885 9.01 1.97 0.66 42.4 37.0 7.2 5.6
2012 Gio Gonzalez Nationals 822 9.35 3.43 0.41 48.2 30.0 5.8 5.0
2011 David Price Rays 918 8.75 2.53 0.88 44.3 36.9 9.7 4.4
2013 Gio Gonzalez Nationals 819 8.83 3.50 0.78 43.9 33.3 9.7 3.2
2011 Gio Gonzalez Athletics 864 8.78 4.05 0.76 47.5 34.1 8.9 3.1
2010 Gio Gonzalez Athletics 851 7.67 4.13 0.67 49.3 35.3 7.4 3.1

Cluster 60

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2011 Brad Ziegler – – – 239 6.79 2.93 0.00 68.6 13.4 0.0 1.0
2007 Cla Meredith Padres 342 6.67 1.92 0.68 72.0 13.6 17.1 1.0
2008 Brad Ziegler Athletics 229 4.53 3.32 0.30 64.7 18.8 6.3 0.5
2013 Joe Smith Indians 259 7.71 3.29 0.71 49.1 30.1 9.6 0.5
2008 Chad Bradford – – – 241 2.58 2.28 0.46 66.5 16.0 9.4 0.4
2012 Cody Eppley Yankees 194 6.26 3.33 0.59 60.3 19.1 11.1 0.3
2008 Joe Smith Mets 271 7.39 4.41 0.57 62.6 17.9 12.5 0.3
2009 Cla Meredith – – – 283 5.10 3.44 0.55 62.9 21.1 8.9 0.2
2010 Brad Ziegler Athletics 257 6.08 4.15 0.59 54.4 26.9 8.2 0.1
2014 Brad Ziegler Diamondbacks 281 7.25 3.22 0.67 63.8 18.9 13.5 0.1

Cluster 68

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2009 Justin Verlander Tigers 982 10.09 2.36 0.75 36.0 42.8 7.4 7.7
2012 Justin Verlander Tigers 956 9.03 2.27 0.72 42.3 35.6 8.3 6.8
2011 Justin Verlander Tigers 969 8.96 2.04 0.86 40.2 42.1 8.8 6.4
2010 Justin Verlander Tigers 925 8.79 2.85 0.56 41.0 40.3 5.6 6.3
2013 Justin Verlander Tigers 925 8.95 3.09 0.78 38.4 38.9 7.8 4.9

Cluster 69

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2008 Manny Parra Brewers 741 7.97 4.07 0.98 51.6 26.6 13.5 2.3
2014 Drew Smyly – – – 618 7.82 2.47 1.06 36.6 43.4 9.5 2.2
2012 J.A. Happ – – – 627 8.96 3.48 1.18 44.0 38.9 11.9 1.9

Cluster 70

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2014 Gerrit Cole Pirates 571 9.00 2.61 0.72 49.2 31.8 9.4 2.3
2009 Luke Hochevar Royals 631 6.67 2.90 1.45 46.6 35.8 13.8 1.0
2012 Joe Kelly Cardinals 457 6.31 3.03 0.84 51.7 27.5 11.0 0.9
2008 Sidney Ponson – – – 612 3.85 3.18 0.93 54.5 26.2 10.9 0.9
2013 Joe Kelly Cardinals 532 5.73 3.19 0.73 51.1 28.2 8.9 0.7
2009 Roberto Hernandez Indians 596 5.67 5.03 1.15 55.2 27.0 13.7 0.0

Cluster 71

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2008 Chris Young Padres 434 8.18 4.22 1.14 21.7 53.4 8.7 1.4
2012 Chris Young Mets 493 6.26 2.82 1.25 22.3 58.2 7.7 1.2
2013 Josh Collmenter Diamondbacks 384 8.32 3.23 0.78 32.7 46.8 6.9 1.0
2012 Josh Collmenter Diamondbacks 375 7.97 2.19 1.30 37.4 43.1 11.5 0.8
2009 Chris Young Padres 336 5.92 4.74 1.42 30.2 51.7 10.0 0.0

Cluster 72

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2014 Madison Bumgarner Giants 873 9.07 1.78 0.87 44.4 35.8 10.0 4.0
2013 Jon Lester Red Sox 903 7.47 2.83 0.80 45.0 35.4 8.3 3.5

Cluster 77

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2011 Josh Collmenter Diamondbacks 621 5.83 1.63 0.99 33.3 47.0 7.7 2.3
2014 Josh Collmenter Diamondbacks 719 5.77 1.96 0.90 38.8 39.9 8.3 1.9

Cluster 78

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2007 Rich Hill Cubs 812 8.45 2.91 1.25 36.0 42.9 11.7 3.1
2014 Tyler Skaggs Angels 464 6.85 2.39 0.72 50.1 30.9 8.7 1.5
2011 Danny Duffy Royals 474 7.43 4.36 1.28 37.5 40.3 11.5 0.5
2010 Manny Parra Brewers 560 9.52 4.65 1.33 47.2 34.5 14.8 0.3

Cluster 79

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2012 David Price Rays 836 8.74 2.52 0.68 53.1 27.0 10.5 5.0
2011 C.J. Wilson Rangers 915 8.30 2.98 0.64 49.3 31.9 8.2 4.9
2010 C.J. Wilson Rangers 850 7.50 4.10 0.44 49.2 33.5 5.3 4.1
2013 C.J. Wilson Angels 913 7.97 3.60 0.64 44.4 33.4 7.2 3.2
2012 Madison Bumgarner Giants 849 8.25 2.12 0.99 47.9 33.3 11.7 3.1
2011 Derek Holland Rangers 843 7.36 3.05 1.00 46.4 33.6 11.0 3.0
2012 Wandy Rodriguez – – – 875 6.08 2.45 0.92 48.0 31.6 10.1 2.5
2014 Jason Vargas Royals 790 6.16 1.97 0.91 38.3 38.7 8.2 2.2
2012 C.J. Wilson Angels 865 7.70 4.05 0.85 50.3 29.9 10.8 2.2

Cluster 85

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2012 Cliff Lee Phillies 847 8.83 1.19 1.11 45.0 36.9 11.8 5.0
2014 Cole Hamels Phillies 829 8.71 2.59 0.62 46.4 31.1 8.2 4.3
2009 Wandy Rodriguez Astros 849 8.45 2.76 0.92 44.9 37.1 9.9 4.1
2012 Wade Miley Diamondbacks 807 6.66 1.71 0.65 43.3 33.7 6.9 4.1
2013 Jose Quintana White Sox 832 7.38 2.52 1.03 42.5 37.4 10.2 3.5
2009 Andy Pettitte Yankees 834 6.84 3.51 0.92 42.9 37.8 8.9 3.4
2012 Wei-Yin Chen Orioles 818 7.19 2.66 1.35 37.1 42.1 11.7 2.3

Cluster 86

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2009 Josh Beckett Red Sox 883 8.43 2.33 1.06 47.2 31.7 12.8 4.2
2010 Max Scherzer Tigers 800 8.46 3.22 0.92 40.3 40.0 9.6 3.7
2014 Nathan Eovaldi Marlins 854 6.40 1.94 0.63 44.8 32.9 6.6 2.9
2012 Lucas Harrell Astros 827 6.51 3.62 0.60 57.2 22.5 9.7 2.8
2013 Jeff Samardzija Cubs 914 9.01 3.29 1.05 48.2 31.4 13.3 2.7
2011 Max Scherzer Tigers 833 8.03 2.58 1.34 40.3 39.5 12.6 2.2
2009 Mike Pelfrey Mets 824 5.22 3.22 0.88 51.3 30.0 9.5 1.7
2011 Roberto Hernandez Indians 833 5.20 2.86 1.05 54.8 26.6 13.0 0.9

Cluster 92

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2014 Steve Cishek Marlins 275 11.57 2.89 0.41 42.7 31.1 5.9 2.0
2007 Sean Green Mariners 304 7.01 4.50 0.26 60.9 18.8 5.1 0.7
2008 Sean Green Mariners 358 7.06 4.10 0.34 63.3 19.5 6.1 0.7
2011 Shawn Camp Blue Jays 292 4.34 2.98 0.41 53.5 25.7 5.2 0.3
2010 Shawn Camp Blue Jays 298 5.72 2.24 1.00 52.0 31.4 11.1 0.2

Cluster 95

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2008 Cliff Lee Indians 891 6.85 1.37 0.48 45.9 35.1 5.1 6.7
2012 Cole Hamels Phillies 867 9.03 2.17 1.00 43.4 35.1 11.9 4.6
2013 Cole Hamels Phillies 905 8.26 2.05 0.86 42.7 36.7 9.1 4.5
2008 Scott Kazmir Rays 641 9.81 4.14 1.36 30.8 48.9 12.0 2.0

Cluster 97

year Name Team TBF K9 BB9 HR9 GB_pct FB_pct HR_FB WAR
2011 Jered Weaver Angels 926 7.56 2.14 0.76 32.5 48.6 6.3 5.7
2009 Jered Weaver Angels 882 7.42 2.82 1.11 30.9 50.4 8.3 3.9
2014 Chris Tillman Orioles 871 6.51 2.86 0.91 40.6 39.3 8.3 2.3
2009 Joe Blanton Phillies 837 7.51 2.72 1.38 40.6 39.5 12.9 2.2
2013 Chris Tillman Orioles 845 7.81 2.97 1.44 38.6 39.8 14.2 1.9