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.

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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

 

 

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