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When Eric Seidman and I unveiled SIERA, a little Googling showed that there were three big debates that broke out on the internet. Firstly, sabermetricians debated its validity and value. Secondly, readers debated whether they wanted to see how the sausage was made or the just see the end result, which was how the statistic would be used. Thirdly, baseball fans with a sabermetric bent once again debated the validity of Defense Independent Pitching Statistics (DIPS) Theory.

My introduction to true baseball analysis came during my first year of graduate school in 2003, when my roommate interrupted my studying and asked me to read a chapter of this neat book called Moneyball. He explained that it was an inside look at how the A’s were able to field such good teams without having much money. He also said there was one chapter that really shocked him, the one that explained the theory of how pitchers do not control their Batting Average on Balls in Play (BABIP), but only walks, strikeouts, and home runs.

"There’s no way that’s true," I told him, horrified that he knew so little about baseball and thinking that he would understand if he had played the game. He said he believed the book's hypothesis, and I asked him the question that we all ask when we first hear of DIPS Theory:

"Do you think if I went took a major-league mound that hitters would hit me no better than Pedro Martinez?"

He asked me why the math showed that the other three statistics were the only things that mattered, up to my neck in Econometrics homework at the time, told him, "The skill is probably weaker and correlated with other things, and he's probably just running a regression. I'd bet this BABIP thing is correlated with walks, strikeouts, and home runs, and so they are picking up the effect in a regression making it look useless in isolation." Up until this point, I had probably answered with the same arguments most baseball fans use, but I don't know if most non-baseball fans would use that argument. That dismissive insight partly led to the birth of SIERA more than six years later, as Eric and I have picked up on and utilized some of the small correlation between strikeout rate and BABIP (that J.C. Bradbury found in 2005), and allowed SIERA to be a sort of hybrid DIPS statistic that picks up on some control that pitchers have over BABIP, too.

I didn't think about sabermetrics again for a couple more years after the conversation with my roommate, when I eventually picked up Moneyball and read it cover to cover. At that point, I was a convert and haven't turned back. I’m now a sabermetric preacher in day-to-day life, willing to talk baseball with anyone. If you tell intelligent people about sabermetrics, they seem to believe most things. When you tell people about on-base percentage being more important than batting average, most people say that should have been obvious to them. When you talk about RBI being team-dependent, people believe you. When you talk about fielding percentage undervaluing players with good range, they believe you, too, and also have no problem throwing win-loss record out the window (at least until they need it to describe someone as a 20-game winner.)

However, the second you tell anybody about DIPS Theory and that pitchers can't control their hit rate on balls in play, they say you're nuts and "if you played baseball, you would understand." I did play. I was a bad high school pitcher who had a ridiculously high hit rate on balls in play. I would never be able to hold major-league hitters to a league-average .300 BABIP, but this is where decision theory and DIPS need to be friends.

The simple reason I do not get to demonstrate this counterargument in real life is that I would not be allowed to pitch in a major-league game in the first place. To be able to pitch in the majors, one needs to at least be able to strike hitters out now and then. No one in the majors who pitched at least 100 innings last year struck out fewer than 9.4 percent of the hitters they faced, as Jeremy Sowers did, which is still a feat most people could never dream of achieving. On the other hand, no one struck out more than Tim Lincecum's 28.8 percent. The reality is that everybody who can get hitters to whiff enough to hold a roster spot on a major-league team has similar skills at preventing hits on balls in play. Even Sowers got hitters to whiff on 13 percent of their swings. Pitchers are not all the same at preventing hits on balls in play, but the discrepancies are so small that there is not much meaningful statistical difference between major-league pitchers as far as hit prevention upon contact.

One reason that people often suspect there should be a difference is that ground balls in play are more likely to be hits than fly balls in play. Although about 24 percent of ground balls are hits, just 14 percent of fly balls and pop-ups are hits (and 16 percent of non-home run outfield fly balls, specifically). Since pitchers are certainly prone to either be of the ground-ball or fly-ball variety—GB/FB ratio has as much persistence as walk and strikeout rates—people expect that there should be some difference between pitchers in this regard. The reason that this is such a small difference in aggregate is that the batted-ball type that really falls for hits more than the others is line drives, which drop about 73 percent of the time. Thus, the most important question in asking whether pitchers control their hit rates on balls in play is whether they control their line-drive rate on balls in play.

The answer to that question is no. Although we are perfectly aware that game charters are biased in evaluating what constitutes a line drive—Colin Wyers showed that very well a few months ago—when you look at a pitcher's line drive rate, net of his team’s pitching staff's line drive rate, the intra-class correlation Eric and I found was 0.007. In other words, pitchers who give up a lot of line drives on balls in play one year are no more or less likely to allow a lot of line drives on balls in play the next year. Line drives are not a pitcher skill, but they are the primary determinant in BABIP. That is why researchers have continually found that pitchers do not have significant control over BABIP.

That is not to say Lincecum will surrender the same number of line drives in his next start as Sowers. Lincecum will strike out more hitters he faces, and so he will allow fewer balls in play overall. But Lincecum's line-drive rate on the balls hitters put in play last year was 19.1 percent and Sowers' was 17.1 percent. And we know from the intra-class correlation that both will probably have line-drive rates around the league average of 19 percent this year.

This is the reason that tRA did so poorly at predicting ERA the following year compared to FIP, despite having all of the same information and batted ball rates mixed in. Since tRA asked the question, "What would the average pitcher's ERA be, given his strikeout, walk, home run, pop-up, ground-ball, non-HR outfield fly ball, and line-drive rate?" it was given an answer that highly correlated with line drives. There is a negative -0.23 correlated between line drive in a given season and ERA for pitchers who pitched at least 40 innings, but line-drive rate does not carry over to the following season. Thus, any DIPS statistic that relies on line-drive rate will unravel the following season if it tries to predict ERA. That is why when tRA was compared to FIP in predicting the following year's ERA, it did worse. It uses all the same information, and a bunch of extra information to confuse itself. Basically, tRA is FIP having a nightmare.

There simply isn't much of a difference between pitchers in their ability to control what percent of balls hit the bat and what percent hit the bat squarely on the center. That makes a lot of sense. The pitcher can control how often the batter misses, whether it's more likely to hit the top of the bat (fly ball) or bottom of the bat (ground ball) based on the trajectory of his pitches. I think that if I went out to the mound in a major-league game, hitters would be able to time my slow offerings right in the center of their bats. They might be a little better at putting their bats on Sidney Ponson's pitches than CC Sabathia's pitches, but when they do, they square it up more often based on whether they are Michael Young or Eric Bruntlett (or whether they happened to guess right), rather than who the pitcher was. Of course, if pitchers were predictable, then their line-drive rate would spike, but it does prove to be true statistically that their line-drive rate shows no persistence when they are trying to avoid tipping their pitches. The fact is if you can miss enough bats to get 10 percent of hitters to strike out, then the other 90 percent will get their bats on the ball right in the center of the bat as often against Sabathia (20.4 percent) as Ponson (19.3 percent).

This seems to be the missing puzzle piece in DIPS Theory, and I'm afraid that Eric and I buried it in our SIERA series, but it should be highlighted. The primary reason that pitchers do not control their BABIP is that among those sufficiently capable of missing bats, they all seem to have the similar lack of skill in keeping the ball from hitting directly on the barrel rather than just above or just below. Since they control the trajectory of their pitches, some show more of a tendency to make the hitter miss half an inch high rather than half an inch low when they do fail to hit it squarely (based on which part of the ball enters the strike zone first). Yet it's up to the hitter to guess right and center the ball on the sweet spot. The pitchers who can miss enough bats to keep their jobs simply do not differ in how often the ball hits the sweet spot.

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wendtm
2/25
"The primary reason that pitchers do not control their BABIP is that among those sufficiently capable of missing bats, they all seem to have the similar lack of skill in keeping the ball from hitting directly on the barrel rather than just above or just below." That's very close to "A is A," isn't it, Matt? Better to say, "What BABIP consistency tells us is that . . . " I think we can illustrate things in this way. Picture a normal distribution representing a set of swings off a pitcher, with the x-axis measuring where the bat is placed relative to the ball. For you or me, the distribution would be very tight, and the maximum would (sadly for us) coincide with X=0. For Lincecum or Sabathia, the distribution would be quite broad and the y-intercept would be much lower and quite possibly not the maximum, with more of the curve to the left or right depending on whether the pitcher in question favors GBs or FBs. Now picture not just the y-intercept, but the small critical strip surrounding it, representing all pitches contacted by the bat. What DIPS tells us is that the percentage of the entire strip that is contained in the center, smaller strip representing line drives, is always about the same, and that in turn is telling us that the curve in this region is always roughly the same shape, i.e. much flatter than for you or me. I cna't embed graphs, but you could perhaps do this.
ssimon
2/25
Tremendous explanation, thanks.
cloakedarbiter
2/25
I suspect more Hit F/X data would generate more statistical evidence to support the DIPS Theory as well as back up Swartz' explanation here. Great article, and a good reminder of the first time I read Voros McCracken's article half a decade ago.
NLBB15
2/25
Great article and it's always nice to hear more about the concrete impact of Moneyball. Would there be use in a DIPS system like tRA that does not include line drives? Is this what David Gassko created with his Luck Independent Pitching Statistic? Any thoughts on that metric? I also don't know the code for tRA* but I suspect they would regress LD heavily toward league average. I know they do other regressions to but perhaps this is a way to improve a DIPS metric.
swartzm
2/26
I want to learn more about LIPS. I don't have a good enough sense of it, and I haven't seen it talked about much. Reading through some of Gassko's stuff though, it looks very well done and he seems to be coming to a lot of good conclusions. I still like what we're doing with SIERA and the interactions and the quadratic terms, but LIPS seems to be understanding the line drive issue well. I think it you want do a projection, do a projection. Looking at next year ERA is a way to show you are representing the relationship between ERA and skills.
TheRedsMan
2/25
Bravo, Matt. I've never seen the issue so clearly articulated.
FeVeR22
2/25
Great work, Matt. Sometimes it's really useful to "dub-down" some of these sabermetric intricacies and your explanation is superb. Really good stuff.
irablum
2/25
Great article! I wonder if, in the course of creating PECOTA's for minor league prospects, the issue of the variance of K rates for minor league pitchers was presented. Just looking at the Rangers system, we go from Miguel de Los Santos' 52% strikeout rate (70 K's in 32 innings) to Andrew Laughter's 8.8% strikeout rate (18 K's in 44 innings). Other teams may have more extreme examples. does this change the methodology associated with SIERA?
swartzm
2/26
Not really going to change the methodology, but I sure wouldn't expect it to be gospel outside of MLB pitchers. I imagine it's pretty good at minors and worse at low minors, but I'm not sure.
2/25
Very well done, this is a memorable article.
jackalltogether
2/25
Why is home run rate considered a pitcher skill when line drive rate isn't? Statistically, I know it's because pitchers are consistent from year to year, but logically how is it that they have some ability to avoid hard contact on fly balls but none on liners? I half want to say it's simply a function of K rate and FB% with no independent value of its own, but I assume somebody can tell me why that's wrong...
swartzm
2/26
HR rate isn't really a pitcher skill, but flyball skill is, so that's why HR has something like a .15 correlation year-to-year. FB has a .70 correlation I think, but HR/FB is like .07 I think. Liners aren't just hard contact. They are hard contact centered. That's the key.
Ogremace
2/26
Part of the point of the SIERA posts was that HR rate does not actually have a very high year to year correlation and is not as much of a pitcher skill as was originally assumed by DIPS metrics.
ncimon
2/25
You're right. The SIERA articles would have greatly benefited from surfacing this idea. This is a great article because it explains something obscure with great clarity. Thanks.
YankeesSuck0213
2/26
This is incredibly clear and well done.
thegeneral13
2/26
Great job, Matt. I'm trying to reconcile how missing bats entirely is a skill while missing the very center of the bat is not. This seems to imply that when hitters either guess correctly or properly identify a pitch mid-flight they make square contact at a similar rate regardless of the pitcher, but when they guess incorrectly or mis-identify the pitch mid-flight they generally miss altogether. There have to be discrete scenarios like this - if it was a normal distribution of swings around an estimated point of contact a higher swing and miss rate would also lead to a lower rate of square contact (fatter tails so less probabiity in the middle). This is kind of what the first responder was getting at, I think. So it seems like what pitchers really control is whether the hitter guesses correctly or properly identifies the pitch mid-flight - if yes, square contact rate is the same for every pitcher; if no, a swing and miss is likely regardless of the pitcher. One final thing on this last point - I wonder if mediocre pitchers have higher foul ball rates against them. I recall whenever I moved up to tougher competition (former pitcher myself) guys got a lot better at fouling off good pitches when they were fooled. It occurs to me that one of the key abilities of a hitter might be to take scenario B above where they are fooled and turn it into a foul ball instead of a swing and miss. And a key skill for pitchers is to have good enough stuff that if the hitter doesn't correctly identify the pitch there's no chance to make contact. Just something to think about. Has anyone ever looked at foul ball rates? I'm really just throwing this out there because I think it's interesting. Maybe I'm missing some other way these things could both be true (missing bats is a skill, missing the very center of the bat is not). Again, nice job on the article. It made me really think about how the game works, which is fun.
swartzm
2/26
Thanks. I really liked the description in the first post. I guess the question is really what the distribution is near the center of the bat. I'm guessing it's not that steep for pitchers and that's probably what's going on. I think your characterization of correctly identifying pitches is great too. I think that's what's going on. Thanks again.
greensox
2/26
I still don't see a lick of "proof" that pitchers are incapable of controlling anything but strike outs. I see theory, opinion, but no proof. That every ML pitcher strikes out 9.4% of the hitters proves anything proves about as much as every ML pitcher also gives up at least x% homers, y% doubles, t% popouts, etc.
thegeneral13
2/26
I think the point Matt was trying to make there is that major league pitchers are all of sufficient skill to make it to the majors in the first place, and within that elite class of pitchers there is no difference in line drive rates. If you compared different classes (major leaguers vs. little leaguers, each facing ML hitters) you would see a difference, but not when you're comparing the best in the world to the 25th best in the world. I think he was just using K rate as an example to show that even the worst pitcher in the majors is still very good and capable of making major league hitters swing and miss a lot - especially compared to the zero % of the time they'd swing and miss off of most of us.
Ogremace
2/26
Then go read an article actually explaining the reasoning behind DIPS. The correlation year to year on hits and hit types is very small. The correlation year to year on Ks and BBs are much higher. What other kind of proof do you need?
swartzm
2/26
Greensox: Year-to-year correlation-- that's how to prove it. It's pretty standard. If the pitcher who did it before is more likely to do it again than the pitcher who didn't do it before, it's probably a skill. For K%, it's about .75. For BB%, it's about .65. For GB%, it's about .75. For LD%, it's about .01.
greensox
2/26
I have a related question...how would you ever prove an individual statistic is valid anyway? There's nothing really to compare it to. Some formula for measuring team quality is easy to prove: match it up against actual wins. But otherwise, it seems impossible. On this, again, I just can't see how every whiff is meaningful and indicative of pitcher quality; but the difference between a dink ball hit in front of the plate and a homer run is pitcher independent.
swartzm
2/26
Again, there are agreed upon standards that meet logical rules. Being able to predict next-year ERA well is good because it highlights skills that pitchers control well. There's different standards that are agreed upon for different types of statistics. Repeatability and team-level correlations are important.
greensox
2/28
Matt You've been generous with your time to a skeptic like me, and I appeciate it. I am comfortable with most of the theories of measuring hitting presented hereon, but I just can't get my head around the strikeout thing. I'm not a natural (or trained) statistician like you guys. I really enjoy reading your stuff - great work, really.
sunpar
2/26
Correct me if I'm wrong: There is pitcher skill in LD rates so far as a GB cannot ever be a LD, and thus GB pitchers will generally allow fewer line drives. This is generally why GB rate doesn't increase BABIP (since FBs offer much lower BABIP than any other batted ball) .
sunpar
2/26
At least, that would be my explanation for why Derek Lowe and Brandon Webb have had such low LD rates.
swartzm
2/26
There apparently isn't any correlation between LD% for high GB% and low FB%. It seems that 1% extra GB skill correlates with 1% less FB+PU skill. Lowe and Webb (and mostly Lowe) happen to have low line drive rates thus far and Pineiro seems to be on the high line drive side of things, but I think that if I had to pick an over/under LD% for all three of them in 2010, I'd go with 19%.
sunpar
2/26
sunpar
2/26
Hey Matt, I hate you bother you with more questions, so feel free to ignore if you've already gone over this: I went on fangraphs (just easier to export and work with than BP's stats, I'm sorry!), and pulled batted-ball info for all the pitchers with at least 700 IP from 2002-2009 (128 pitchers in total). When I look at this sample, I find a correlation of -0.35 between GB rate and LD rate, and a correlation of +0.16 between FB rate and LD rate; seemingly indicating that over a large sample size, pitchers who force more GBs will allow fewer LDs, though the effect is not strong. (Correlation of -0.98 for GB rate and FB rate) There is the very real possibility that I'm making some fundamental mistake in my reasoning here.
swartzm
2/26
I'm getting -0.32 same-year correlation between GB% and LD% and -0.05 for LD% and (FB+PU)% with BP data. The problem is that GB%+LD%+(FB+PU)%=100%, so the non-line drives need to go into either the GB category or into the FB or PU category. Looking at correlations with following year line drive rates, we see that the correlation almost disappears, just .08 for (FB+PU)-first-year vs. LD-second-year, and -.14. GB-first-year and LD-second-year. So maybe there is some element of more non-line drives being turned into grounders. I'm not sure if that's park effects or something, or something I'm not thinking of but it doesn't seem to be much evidence of a big deal. I like what you did there, and I thought about that once too, but it's really the issue that the non-line-drives need to go somewhere. I'm not sure why Fangraphs' stats said +0.16, but that could be an indication that non-line-drives could be turning into GB more than FB.
Mooser
2/26
I am still a little confused. If GB% of BIP and FB% of BIP is somewhat controllable,then isn't the alternative hit type a Line Drive, and thus controlled by default. Or does a pitcher that has high control over high GB% rates, have little control over what is left (LD% and FB%). Thus, does a pitcher have control over how high his FB% is and how low his GB% is, or just control over his high FB%.
swartzm
2/26
@Mooser: They control what percent of non-line drives are GB and FB. The way I think about it is that when a pitcher is lucky on "linedrivelessness" they get extra GB and FB instead and unlucky with lots of line drives being hit, they get that subtracted from GB and FB. Assuming the luck is even up or down, that's why we used (GB-FB-PU)/PA as our estimator. If a few extra line drives hurt both evenly so neither GB/PA or (FB+PU)/PA would be great stats to use. Let me know if this helps.
nosybrian
2/26
@Matt: Thanks for a strong and particular instructive article.
krissbeth
2/27
So, has it been proven that hitters control line drive percentage? Or is it uncontrolled by anyone?
swartzm
2/27
They definitely do, but less than people think.
gregorybfoley
2/27
This is great stuff and I completely agree that line drive rate, and therefore BABIP, are mostly out of the control of the pitcher, but one thing bugs me whenever I think about DIPS. There are some pitchers (exceptions?) who seem to be able to sustain below average BABIPs over the course of long careers. Here's a thoroughly unscientific list of a few from the not-too-distant past: Catfish Hunter .251 Jim Palmer .255 Charlie Hough .258 Sid Fernandez .259 Warren Spahn .265 Tom Seaver .267 Barry Zito .275 Nolan Ryan: .275 Mariano Rivera .276 Phil Niekro .277 Bob Gibson .278 Trevor Hoffman .278 Ferguson Jenkins .278 Carlos Zambrano .280 Tim Wakefield .281 Dan Quisenberry .282 Orel Hershiser .284 Dennis Eckersley .284 Tom Glavine .286 Johan Santana .287 Oil Can Boyd .288 Bret Saberhagen .288 Tim Hudson .289 Greg Maddux .289 Bert Blyleven .289 ...and a few who I expected might make my arbitrary .290 cut but didn't: Roger Clemens .294 Jake Peavy .294 Randy Johnson .302 I don't know what this list means, but if a player is above average over the course of 10 or 15 or 20 years, then it can't be a fluke, right? Knuckleballers are well represented but so are curveballers, power pitchers, control artists and change-up specialists. I'd be interested to know what Matt thinks.
TheRealNeal
2/27
Well, one thing is that you've got to adjust for defense... of course that gets difficult because the way most defensive stats, and certainly most team-oriented defensive stats are calculated is based upon the rate of turning batted balls into outs. There's a school of thought, and Matt pretty much admits to being in this school with his comments on Lowe's line drive rate, that luck is luck and the reason those guys are on your list is that they were lucky, and because they were lucky they were practically better pitchers than their rates would suggest. I'm not comfortable with that logic either, I think it's a bit lazy. I've seen pitchers pitch to their park and defense, and pitch differently in other parks, so I know that they can, for instance, control to some degree the rate at which balls do get put into play. The question is how much are you really concerned with Lowes of the world? Is it enough to use this formula that generally predicts ERA quite well, and just mentally lower Lowes' ERA? Probably for most purposes.
gregorybfoley
2/28
I agree that you have to adjust for defense, but over the course of these pitchers' long careers, wouldn't the changing defensive allignments behind them and the affects of luck even out? I find it hard to believe that some of these guys had BABIPs .040 points below league average over the course of their careers due only to luck and consistently good defense, but maybe they did. The DIPs formulas work as predictive tools for the general population of MLB pitchers so they're definitely useful and sound, but maybe something interesting is going on with a few elite pitchers. It might also be interesting to note that some of the worst pitchers to have enjoyed long careers recently (or suffered through them as the case may be) appear at the other end of the list: Glendon Rusch .334 Esteban Loaiza .316 Brian Moehler .315 Jason Jennings .314 Julian Tavarez .314 Carlos Silva .313 Mark Hendrickson .313 Sidney Ponson .312 Livan Hernandez .311 Nate Robertson .311 Of course I'm cherry picking by omitting good pitchers like: Shane Reynolds .323 Charles Nagy .316 Aaron Harang .316 Andy Pettitte .315 John Lackey .311 ...but these last few are the best of the pitchers who had long recent careers with BABIPs above .310 and they aren't as good as the ones listed in the previous post who had BABIPs below .290. There seems to be a trend of elite pitchers beating the league average and less than elite pitchers being beat by it. I may be seeing a non-existent trend in what is just a normal distribution around the average BABIP or the difference might be explained by team defensive efficiency. I don't know what the explanation is or if there is even anything to explain.
swartzm
3/01
It's true that pitchers who can't strike hitters out well enough to stay at the big league level will not manage league average BABIPs, which was a large point I was trying to make in the article. It's also true that groundball pitchers will tend to have BABIPs around .310, as will pitchers who struggle to strike hitters out. There's also such thing as bad defenses that play a role here.
swartzm
3/01
TheRealNeal-- It's really not laziness to say that it's luck, because you can statistically calculate the standard deviation of luck of a binomial variable as sqrt((p)*(1-p)/n). So a pitcher who allows 500 balls in play in a .300 BABIP league will have a variance of sqrt((.300)*(1-.300)/500), which is .020 points. So in any given year, 1/3 of pitchers will have BABIPs below .280 or above .320 even without having special BABIP skills.
swartzm
2/28
Sorry it took so long for me to reply-- I was out of town this weekend... Certainly there ARE pitchers that have some control over BABIP-- both myself and others have discovered that groundball pitchers have higher BABIPs because groundballs have higher BABIP than line drives. Also, knuckleballers as you mention are BABIP-prevention wizards at the MLB level. Further, strikeout pitchers have lower BABIPs. The thing is that all of these things make the skill level range +/- .010 with respect to league average (except knuckleballers). The pitchers you have above are a mixture of pitchers who pitched in front of good defenses, strikeout wizards, perhaps some luck, but BY FAR the most common string you see there is pitchers who played in low BABIP eras overall. Most of the recorded 1950s had BABIPs around .275. The 1960s was around .269-.281. The 1970s ranged from .272 to .287. The 1980s were mostly in the .280s varying around, and same with the early 1990s. It wasn't until the "modern era" of 1993-now that BABIPs have averaged around .300. So looking through the list, most of the pitchers there did play during the era when those were normal. But a lot of those guys are knuckleballers or guys that played in front of great defenses (Maddux, Glavine). You also see great strikeout pitchers like Johan Santana on there.
gregorybfoley
2/28
One final note: Most of the pitchers on the first list of recent elite pitchers with career BABIPs below .290 demonstrated a multiple-season-long period of even lower BABIPs that coincided with their statistical and physiological peak seasons. Most of them also had a few, non-peak seasons at the tails of their career where they posted merely average BABIPs. To me, this implies that when these elite pitchers were at their physical best, they could do something sustainable to suppress hits on balls in play. Compare Greg Maddux's peak years' BABIPs (1991-1998) to his non-peak years' BABIPs (1986-1990 and 1999-2008). http://www.fangraphs.com/statss.aspx?playerid=104&position=P#advanced His peak years' BABIPs are outstanding while his non-peak years' BABIPs are merely slightly better than average. The other pitchers on the list show the same pattern of seemingly suppressing BABIP for a sustained period during their peak years.
swartzm
3/01
Although Maddux is an example of a pitcher who played in front of great defense, you do make a very smart observation here. I don't have the link but google for an article by Tom Tippett who I believe now works for the Red Sox who showed that there is a tendency for pitchers to be particularly good at suppressing BABIP in their peak years. My belief is that this is not the same as saying they suppress their line drive rate more in their peak years. In fact, my general belief on this topic might be categorized well by saying "Matt thinks that pitchers do control their BABIP but they just don't control their line drive rate which is the primary determinant of their BABIP so it's tough to tell BABIP skill apart."
gregorybfoley
3/01
Thanks for the thoughtful responses.
rwillou
3/08
"I don't have the link but google for an article by Tom Tippett who I believe now works for the Red Sox who showed that there is a tendency for pitchers to be particularly good at suppressing BABIP in their peak years." I think this may be the Tippett article you are talking about: http://www.diamond-mind.com/articles/ipavg2.htm
brucegilsen
3/08
Excellent article Matt! Your point about Sowers reminded me that Bill James many, many, many years ago noted that major league baseball talent is not normally distributed - anyone in or near the majors is way off on the right tail. It's something very basic yet I come back to it time and time again over the years.