Mining for Under (and Over) Performers: Strikeouts

A while back, I had a little pet project to try and simplify the process of sniffing out the over- and under-performers relative to strikeout rates. More specifically, recognizing the sometimes wild fluctuations between strikeout rates year to year, I wanted a better idea if a particular pitcher earned their increase (or decrease) in the category. Was there a process — similar to the one we use on ERA with batting average on balls in play and strand rates — that we could go through for strikeout rates?

Obviously, a high swinging strike rate suggests an inherent ability to strike batters out. Makes sense –- you don’t miss many bats, you’re not likely to wind up registering many strikeouts. So using the swinging strike rate to potentially identify the pretenders from the contenders has merit as the season wears on. But I wanted to tighten that up a bit — add variables that would perhaps control for another part of a pitcher’s skill set to help us identify who should reasonably be expected to strike out more, or fewer, batters. And of course, this is with fantasy baseball in mind –- so the idea was that we can all outsmart the next guy relative to the strikeout column.

I originally looked at strikeouts per nine innings pitched (K/9) as my dependent variable in the analysis, but as colleague Jeff Zimmerman astutely pointed out to me later, the strikeout percentage is a stronger measure to show actual ability. That is, “three straight strikeouts in an inning is better than three strikeouts and three hits in an inning even though they both end up as the same K/9.” An excellent observation. So back to the drawing board I went, with some improvements to the sample size, the overall data set, and yes, we’ll use strikeout percentage as our dependent variable.

First, the data set.

I’m looking at starting pitchers who tossed at least 100 innings in 2011. In the last study, I only used qualified starters and it served to limit the sample set considerably, so this opens things up accordingly. In order to make a strikeout rate comparison with the previous year, which will help us with the model, I need these pitchers to have thrown at least 100 innings in 2010 as well. So we lose some names in that cut (most notably, guys like Jeremy Hellickson, Jordan Zimmerman, Michael Pineda, Vance Worley, Brandon Beachy, Cory Luebke, and Alexi Ogando).

Next, I only wanted to compare pitchers that use their fastball enough to make the velocity on that pitch relevant, and thus kicked out R.A. Dickey and Tim Wakefield. Comparing their fastball velocity in this sample would only serve to skew the results since they’re obviously working on the knuckler for their outs, by and large. So we lose two more.

What we’re left with is a sample of 97 starting pitchers who have thrown at least 100 innings in the last two seasons.

Running correlations on this group, looking specifically at the variables we have in mind –- 2011 K% (represented in the graphs as K%), 2010 K%, Fastball velocity (FBv) and Swinging Strike rate (SwStr%) — all have statistically significant correlations with 2011 K% at 0.01 significance (incidentally, age was interestingly correlated at -.231, significant at the .05 level…but that’s for another study). The correlations are as follows:

	2010 K%	FBv	SwStr%
2011 K%	0.754	0.543	0.825

Graphically, looking at the relationships of K% (2011) with 2010 K%, FBv, and SwStr%, you can see that all relationships in the model are statistically significant, and the associated R-squared in each case help define how much of the variance can be explained by that given relationship. In other words, how much of a pitcher’s 2011’s strikeout percentage can be explained by his 2010 K%? How much of that percentage is explained by his fastball velocity? Forgive me for the odd formatting, but if you click on the graph, you should have full functionality to hover over each case to get the name of the pitcher and the two sets of associated data, which is rather fun:

The R-squared for the 2010 K% is 0.568 with a p-value of <0.0001

The R-squared for the FBv is 0.295 with a p-value of <0.0001

The R-squared for the SwStr% is 0.681 with a p-value of <0.0001

Plugging all three into a linear regression model that uses 2011 K% as our dependent variable — in an effort to come up with an expected K% in 2011 — we find a model represented by this:

xK% = -.278 + (.003)*FBv + (1.428)*SwStr% + (.321)*K% 2010

The model summary and fit:

R	R Square	Adjusted R Square	Std. Error of the Estimate
0.877	.769	.761	.020905

So the model explains a good degree more variance in strikeout rate than any singular variable, which of course what this is all about. Applying this to our 97 starting pitchers from the sample set, there are 50 starting pitchers that are within 1.5% (either above or below) of their actual 2011 K%, there are 35 above 1.5% (outperforming their expected strikeout rate), and there are 12 under 1.5% (under-performing their expected strikeout rate). The full results with analysis of a couple cases thereafter:

Name	x2011K%	2010 K%	2011 K%	FBv	SwStr%	Difference
Zack Greinke	0.214	0.197	0.281	92.5	0.106	6.7%
Cliff Lee	0.200	0.220	0.259	91.5	0.093	5.9%
Tommy Hanson	0.204	0.205	0.263	91.2	0.100	5.9%
C.J. Wilson	0.178	0.200	0.225	91	0.083	4.7%
David Price	0.196	0.218	0.238	94.8	0.084	4.2%
Madison Bumgarner	0.187	0.182	0.226	91.7	0.092	3.9%
Doug Fister	0.129	0.129	0.167	90	0.067	3.8%
Paul Maholm	0.104	0.121	0.141	87.4	0.057	3.7%
Jake Arrieta	0.142	0.116	0.178	92.4	0.074	3.6%
Ian Kennedy	0.185	0.207	0.220	90.3	0.088	3.5%
Ubaldo Jimenez	0.186	0.239	0.219	93.5	0.075	3.3%
Clayton Kershaw	0.241	0.250	0.272	93.4	0.111	3.1%
Yovani Gallardo	0.209	0.249	0.239	92.7	0.090	3.0%
Anibal Sanchez	0.213	0.187	0.243	91.7	0.109	3.0%
Justin Verlander	0.229	0.237	0.258	95	0.102	2.9%
Gio Gonzalez	0.200	0.201	0.228	92.5	0.095	2.8%
Felix Hernandez	0.202	0.232	0.230	93.3	0.088	2.8%
Jonathon Niese	0.173	0.192	0.200	90.6	0.082	2.7%
Javier Vazquez	0.176	0.174	0.203	90.4	0.089	2.7%
Jeff Niemann	0.157	0.175	0.184	91.1	0.074	2.7%
Livan Hernandez	0.106	0.127	0.132	83.9	0.064	2.6%
Wandy Rodriguez	0.180	0.217	0.205	89.1	0.085	2.5%
Ted Lilly	0.174	0.212	0.198	87.4	0.085	2.4%
Mike Leake	0.147	0.148	0.171	89.1	0.077	2.4%
Bruce Chen	0.126	0.158	0.148	85.8	0.067	2.2%
Nick Blackburn	0.091	0.098	0.113	89.7	0.048	2.2%
Colby Lewis	0.181	0.232	0.201	89	0.082	2.0%
Mark Buehrle	0.107	0.110	0.127	85.6	0.065	2.0%
Brett Myers	0.153	0.192	0.173	88.4	0.073	2.0%
Jon Lester	0.208	0.261	0.228	92.8	0.087	2.0%
Jonathan Sanchez	0.211	0.252	0.230	89.9	0.097	1.9%
Ryan Dempster	0.199	0.227	0.217	90.3	0.093	1.8%
Trevor Cahill	0.146	0.151	0.163	89.1	0.076	1.7%
James Shields	0.215	0.209	0.231	91	0.107	1.6%
Bronson Arroyo	0.110	0.138	0.126	87	0.058	1.6%
Matt Garza	0.220	0.176	0.235	93.7	0.112	1.5%
Scott Baker	0.208	0.204	0.223	91	0.103	1.5%
Randy Wolf	0.133	0.152	0.148	88.4	0.068	1.5%
Tim Hudson	0.165	0.151	0.179	90.5	0.086	1.4%
Gavin Floyd	0.176	0.189	0.190	91.2	0.084	1.4%
Roy Halladay	0.223	0.221	0.236	92	0.108	1.3%
Josh Beckett	0.216	0.201	0.228	93.1	0.105	1.2%
Jered Weaver	0.202	0.258	0.214	89.1	0.091	1.2%
Rick Porcello	0.121	0.120	0.133	90.2	0.063	1.2%
Justin Masterson	0.162	0.171	0.174	92.7	0.075	1.2%
Jeremy Guthrie	0.133	0.137	0.145	92.5	0.063	1.2%
Jason Vargas	0.141	0.143	0.153	87.4	0.078	1.2%
Chris Volstad	0.152	0.135	0.163	91.3	0.079	1.1%
Chad Billingsley	0.172	0.209	0.183	91.5	0.076	1.1%
Ervin Santana	0.177	0.177	0.188	92.8	0.084	1.1%
Derek Lowe	0.155	0.165	0.165	88	0.081	1.0%
Tim Lincecum	0.235	0.258	0.244	92.3	0.107	0.9%
Aaron Harang	0.165	0.166	0.173	89.8	0.084	0.8%
A.J. Burnett	0.199	0.175	0.207	92.7	0.100	0.8%
Matt Cain	0.189	0.198	0.197	91.2	0.091	0.8%
Jake Peavy	0.186	0.207	0.193	90.6	0.088	0.7%
CC Sabathia	0.228	0.203	0.234	93.8	0.112	0.6%
Brett Cecil	0.159	0.161	0.164	88.5	0.084	0.5%
Mike Pelfrey	0.119	0.131	0.123	92.1	0.055	0.4%
Carlos Zambrano	0.155	0.209	0.159	90.2	0.067	0.4%
Joe Saunders	0.121	0.130	0.124	89.6	0.062	0.3%
Brandon Morrow	0.259	0.283	0.261	93.9	0.115	0.2%
Chris Carpenter	0.190	0.185	0.192	92.5	0.092	0.2%
Wade Davis	0.131	0.157	0.132	91.4	0.059	0.1%
Freddy Garcia	0.152	0.133	0.153	87.2	0.088	0.1%
Travis Wood	0.155	0.205	0.155	89.9	0.068	0.0%
Jeff Karstens	0.138	0.138	0.138	88.8	0.074	0.0%
Jake Westbrook	0.130	0.149	0.129	90	0.063	-0.1%
Mat Latos	0.233	0.253	0.232	92.8	0.106	-0.1%
Dan Haren	0.203	0.217	0.201	90	0.099	-0.2%
Bud Norris	0.224	0.231	0.221	92.6	0.105	-0.3%
Shaun Marcum	0.196	0.206	0.192	86.9	0.103	-0.4%
John Danks	0.189	0.185	0.185	91.6	0.093	-0.4%
John Lannan	0.135	0.110	0.131	89.8	0.076	-0.4%
Ricky Romero	0.199	0.197	0.194	92.1	0.096	-0.5%
Jhoulys Chacin	0.186	0.230	0.181	91	0.082	-0.5%
Joel Pineiro	0.104	0.145	0.099	87.6	0.051	-0.5%
John Lackey	0.151	0.168	0.145	91.6	0.070	-0.6%
Max Scherzer	0.215	0.230	0.209	93.1	0.098	-0.6%
Jair Jurrjens	0.150	0.172	0.144	89.1	0.074	-0.6%
Johnny Cueto	0.172	0.177	0.165	93.4	0.079	-0.7%
Cole Hamels	0.238	0.247	0.230	91.7	0.113	-0.8%
Homer Bailey	0.200	0.215	0.189	92.2	0.093	-1.1%
Jeff Francis	0.125	0.153	0.113	84.7	0.070	-1.2%
Jaime Garcia	0.202	0.190	0.189	89.8	0.105	-1.3%
Hiroki Kuroda	0.208	0.196	0.192	92	0.103	-1.6%
Kevin Correia	0.133	0.179	0.117	90.8	0.057	-1.6%
Chris Narveson	0.197	0.196	0.180	87.8	0.104	-1.7%
Randy Wells	0.158	0.171	0.141	88.1	0.082	-1.7%
Luke Hochevar	0.171	0.168	0.153	92.7	0.082	-1.8%
Carl Pavano	0.132	0.129	0.107	89	0.071	-2.5%
Ricky Nolasco	0.192	0.221	0.166	90.5	0.089	-2.6%
Fausto Carmona	0.158	0.141	0.131	92.5	0.079	-2.7%
Roy Oswalt	0.185	0.231	0.157	91.4	0.080	-2.8%
Jason Hammel	0.151	0.183	0.123	92.9	0.064	-2.8%
Edwin Jackson	0.203	0.201	0.173	94.5	0.093	-3.0%
Francisco Liriano	0.240	0.249	0.189	91.8	0.114	-5.1%

Zack Greinke topping this list as the resident over-performer is probably misleading for a couple of very obvious reasons. One, his 2010 K% of 19.7% was about seven percentage points behind 2009, and the fact that his league change to the National League should also help his strikeout figures. His career rate of 21.1% would have been a more reasonable number to use instead of 2010 K%, but hey, we can’t cherry pick. 28.1% might be a little lofty looking forward for Greinke, but I also think the model probably underrates him at 21.4%.

Cliff Lee is an interesting one. His 25.9% K rate in 2011 is far and away the highest of his career. His career K% is 19.3%, so using the 2010 K% figure of 22% isn’t necessarily under-representing his skill set. But Lee also moved leagues and the last time he was with the Phillies, he posted the second highest K rate of his career. I’d bet that he won’t repeat 25.9%, but he should best 20% in the National League.

Also interesting is the pretty dramatic drop in K% for Travis Wood between 2010 and 2011. Objectively, because he doesn’t have a terribly long track record, you might expect him to land somewhere comfortably in between his 20.5 K% from 2010 and his 15.5% rate from 2011, but in this sample, he was one of only two pitchers to perfectly match his expected K% — so according to the model, he fully earned his 15.5 K% over the course of 2011.

The model predicts that several pitchers ought to have performed significantly better than they did in 2011 as well. In particular, it sees pitchers such as Jaime Garcia, Hiroki Kuroda, and Luke Hochevar having the skills to post stronger strikeout rates than they did in either of the last two seasons. And while it suggests Francisco Liriano is capable of more relative to strikeout rates, I suspect the rest of the Twins faithful feels the same way — and you’ll want to weigh his fantasy value less on whether he underperformed in strikeouts than whether he is healthy and manages to find the strike zone again going forward.

There are many interesting nuggets in the overall sample, but I encourage you to take each as merely another information point as you make plans for 2012. For pitchers stacked towards the middle, there’s probably not much of a story, other than the fact that you can take their 2011 rate with a little more confidence. For those on the poles, consider the back story (Greinke, for instance) but you might also consider them to be good candidates to regress or improve going forward.