Can Matt Cain Sustain His Low HR/FB Rate?

Any time a general theory that applies to most people is advanced, people naturally begin to look for the outliers, and they often use the examples at the ends of the spectrum to cast validity on the theory. Or, they just dismiss the theory as not being applicable to that specific case, which may or may not be true. We see this quite a bit with metrics like xFIP and Matt Cain, who has become the poster child for the part of our readership who thinks that stat isn’t worth all that much. For years, Cain’s ERA has been better than his xFIP would suggest, largely because he has sustained one of the lowest HR/FB rates in all of baseball.

The low HR/FB rate was brought up again yesterday in a reasoned post over at PaapFly. As is often stated by the Cain-is-better-than-xFIP-says crowd, the author noted that Cain has thrown 1,100 innings in the big leagues now, and that should be a large enough sample to conclude that this is a legitimate skill that he can carry forward.

Just for fun, I decided to look back at the data that has been collected over the last nine years. We’re starting to get large enough samples now where we can find other pitchers who have had similar stretches of home run prevention for 1,000+ innings, and still have observed performance in seasons after their run of keeping the ball in the park.

Below are 10 pitchers who, from 2002 to 2007, had the lowest HR/FB rates in baseball, who have thrown a similar number of innings to Cain, and have thrown at least 100 total innings in the last three seasons. The first section is their 2002-2007 IP and HR/FB rate, with the second section being their 2008-2010 IP and HR/FB rate.

Pedro Martinez: 981 IP, 8.0% HR/FB – 154 IP, 14.2% HR/FB
Roy Oswalt: 1,272 IP, 8.3% HR/FB – 602 IP, 10.4% HR/RB
John Lackey: 1,162 IP, 8.5% HR/FB – 555 IP, 10.5% HR/FB
CC Sabathia: 1,226 IP, 8.5% HR/FB – 721 IP, 8.2% HR/FB
Brad Penny: 1,041 IP, 8.7% HR/FB – 324 IP, 10.5% HR/FB
Jarrod Washburn: 1,121 IP, 8.7% HR/FB – 330 IP, 9.3% HR/FB
Barry Zito: 1,320 IP, 8.8% HR/FB – 571 IP, 7.9% HR/FB
Miguel Batista: 1,051 IP, 8.8% HR/FB – 269 IP, 11.7% HR/FB
Dontrelle Willis: 1,022 IP, 8.9% HR/FB – 123 IP, 11.5% HR/FB
Kevin Millwood: 1,160 IP, 9.1% HR/FB – 558 IP, 10.6% HR/FB

Group: 11,351 IP, 8.6% HR/FB – 4,202 IP, 9.9% HR/FB

The league average HR/FB rate is usually around 10.6%. As a group, the ten best big time home run suppressors from 2002 to 2007 were only marginally better than average at that same skill from 2008 to 2010. Sabathia and Zito bucked the trend and actually lowered their HR/FB rates over the last three seasons, so it’s certainly possible that Cain could continue to post low HR/FB rates going forward. After all, he does pitch in a pretty good pitcher’s park and his career HR/FB rate is better than any of the pitchers in this sample, so maybe there is something to David Pinto’s theory about how his fastball moves.

You could have made a similar argument about almost everyone on the above list, though, and as a group, they didn’t demonstrate that there was really much of a sustainable skillset there. Just for fun, I also looked at the guys who had the highest HR/FB rates from 2002 to 2007 and had thrown similar numbers of innings in both samples. Their rate dropped from 12.2% in the first period to 11.4% in the second period – still higher than average, and higher than the low HR/FB group, but only by one percentage point, much smaller than the gap between their observed rates from 2002 to 2007.

Is there some skill to allowing long fly outs? Maybe. But if you can identify which pitchers are likely to keep their home run rates low while giving up a lot of fly balls before they actually do it, then you could make a lot of money in player forecasting. History suggests that we can’t simply look at guys with 1,000+ innings of home run prevention and assume they’ll keep chugging along. It just doesn’t work that way.



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Dave is the Managing Editor of FanGraphs.


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Dan
Guest
Dan

Good info. One issue I have with your group of pitchers is that they were probably all in their collective primes and then entered a typical decline phase after that. Miguel Batista? Dontrelle Willis? Millwood? Washburn…Martinez…even Oswalt to some degree. These guys are not the same pitchers they were in the 5 year stretch. So how much does their HR/FB rate have to do with their declines and not their law of averages regressing to the norm?

Zach Kolodin
Guest
Zach Kolodin

Word.

Kyle H
Member
Kyle H

Completely Agree. To that extent include Penny, Zito, and Lackey to pitchers that haven’t been the same as they were from 2002-2007. Not to mention that you are taking data from a much lower sample size after dismissing the larger sample size for not being big enough. Great article though

Kirkwood
Member
Kirkwood

Something else worth note: of the pitchers Dave listed in the article, I would only characterize 3 of them as flyball pitchers: Barry Zito, Jarrod Washburn, and Matt Cain. All of them have sustained low HR/FB rates without regression. The rest were groundballers or roughly even. Maybe their rising HR/FB rates were due to them getting slightly lucky earlier due to a smaller sample (less flyballs than Zito, Wash, and Cain).

AJS
Guest
AJS

Great point.

Nathaniel Dawson
Member
Nathaniel Dawson

Dan, within the parameters of the study, it’s going to be hard to find good comparables to Cain that aren’t in their decline years. Cain started pitching regularly in the big leagues at the age of 21. You’re just not going to find a whole lot of other pitchers that have pitched regularly for five seasons and aren’t in their 30’s.

I’d also wonder how much of a factor home park plays in this. If a pitcher plays in a park that suppresses HR/FB, that would tend to keep his rate lower in the future. If he switched parks, he wouldn’t get that same benefit. We do see what appears to be some regression to the mean with these pitchers, but they are still lower than league rates, so it suggests some degree of ability there. The park they’re in might have a hand in keeping their rates below league norms.

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