BABIP haters, unite!
by kcdc1 on Aug 6, 2008 4:08 PM EDT
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PrintOkay, I'd posted this in the comments section of another post a few days after it had been originally posted, but immediately realized that no one will read it there, so I'm putting it up here again.
A lot of people like to look at a player's BABIP, and if it's above league average, they say that player has been lucky--if it's below league average, they say that player has been unlucky. Others refine their statements somewhat by comparing that player's BABIP to an expected BABIP that is generated predominately from the player's line drive percentage. I am wholly unconvinced of the legitimacy of either method and have become increasingly frustrated with these seemingly unfounded proclamations. Do you feel likewise? Read on!
My premise: Ignoring wind and sun-aided hits, whether a ball in play is recorded as a hit is a matter of it's velocity and its trajectory, wherein a ball's trajectory is essentially it's vertical angle relative to the ground, and its lateral angle relative to the left and right field lines.
I'm willing to grant that the latter is predominately a matter of luck--a low line-drive straight up the middle is a single, but if it's 10 degrees to the right or left, it will caught by a middle-infielder. xBABIP, you ignore this matter entirely and for good reason. You're quite shaky on the rest though.
A ball's vertical trajectory determines whether it is called a ground ball, a line drive, or a fly ball, and a batter's ability to affect his batted ball's vertical trajectory over the course of a season is absolutely NOT a matter of luck. Alex Gordon generates twice as many fly balls as Joey Gathright. If they're both still playing in 5 years, Alex Gordon will still be generating twice as many fly balls as Joey Gathright. I know most xBABIP calculations take things like LD%, GB%, and FB% into account, but these 3-way classifications are not sufficiently specific, nor are they especially accurate. One fly ball is not the same as the next, and it's not even always clear whether a batted ball is a fly or a line drive. Likewise, a one-hopper through the infield has a lot more in common with a line drive than it does a ground ball pounded straight down into the dirt.
xBABIP calculations that rely on LD/FB/GB percentages are assuming that the majority of one player's flies, grounders, and line drives are leaving the bat at roughly the same vertical angles as the next. This assumption is certainly flawed, but at least the calculations are taking the vertical angles of batted balls into account when determining their probability of generating hits. The much more serious flaw in these calculations is that they don't even take the velocity of batted balls into account. Certain players consistently hit balls harder than others. Likewise, certain players consistently hit balls more weakly than others. And the velocity of a ball of the bat has a HUGE impact on whether it generates a hit. A weakly hit fly ball is called a pop-up and is a nearly automatic out; a hard hit fly ball lands behind an outfielder for a double. Even a ground ball that's hit straight down will generate a hit if it's hit hard enough to produce a large bounce. Likewise, a line drive is an easy out for an infielder if it's hit softly.
Any calculation that purports to generate a player's expected batting average on balls in play without taking into account the speed of that player's batted balls is neglecting one of the most important factors in that expectancy. Because of this, we end up calling players who consistently make hard contact lucky, and players like TPJ who consistently fail to get the barrel of the bat on the ball extremely unlucky. Does this make sense?
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You are ignoring the fact that line drives are significantly about velocity, not just trajectory
This is just my opinion. I could easily be wrong.
no....
They’re not. Softly hit balls that are hit just above parallel to the ground are called “soft line drives” and are scored as line drives. For example, “Mark Grudzielanek softly lined out to second.”
The vast majority of batted balls classified as "line drives" are hard hit
That’s what makes them line drives. A ball with the same trajectory that is hit softer becomes a groundball.
This is just my opinion. I could easily be wrong.
by Scott McKinney on Aug 6, 2008 4:31 PM EDT up reply actions
Can someone pull up the
BABIP for line drives? Isn’t it something hideously high like .750?
A mind without purpose will walk in dark places.
by NHZ on Aug 6, 2008 4:36 PM EDT up reply actions
Thank you
therefore let’s dispense with this “not all line drives are hard hit so LD%/BABIP means nothing” idea. That just doesn’t hold up.
A mind without purpose will walk in dark places.
by NHZ on Aug 6, 2008 6:21 PM EDT up reply actions
I didn't say it means nothing
Of course players who hit more line drives have higher BABIP’s. But not all line drives are created equally. A lot of balls are borderline between flies and liners. They’re hit a little higher than most line drives, and while they do often fall for hits, they don’t fall for hits quite as often as the line drives that are scorched just over the heads of infielders. Likewise, low line drives are far more likely to generate hits if they are hit fast enough that an infielder doesn’t have time to react.
Yes, but..
the fact that 72% of line drives end up as base hits should tell you that they’re a different case than grounders or fly outs, which hitters get on base with at a far lower clip.
A mind without purpose will walk in dark places.
by NHZ on Aug 6, 2008 6:44 PM EDT up reply actions
of course
But they average out sufficiently to the point that there is very little value in differentiating among all these different categories of line drives. It would amount to a lot of effort gathering data and analyzing it for possibly a slightly more accurate estimation of BA than just using line drives. I mean there are an infinite location/velocity combinations you could distinguish among, but the point is the current model gets you close enough to the point that there doesn’t seem to be much benefit to trying to complicate things further.
"But not all line drives are created equally."
You’ve got a logical problem with your entire argument, and it’s this:
Not all groundballs are created equally, either; nor are all fly balls. The outliers come out in the wash.
Sarcasm™. It's the new gravy.
You're also ignoring the fact that we have an immense amount of data with regard to how often GB's, FB's and LD's become hits
Does that mean that BABIP and xBABIP are perfect? Of course not. But they get us close to our goal. Eventually we will have pitch f/x type data on batted balls as we now do with pitched balls, and we’ll be able to describe each batted ball much better and get better xBABIP-type data. Until then, these current metrics do a good job. Like all stats and metrics, they don’t tell us everything, but they tell us something important.
This is just my opinion. I could easily be wrong.
I'm not saying that BABIP and xBABIP are meaningless
I’m saying that they don’t often don’t mean what people take them for. For example, TPJ has a low BABIP because he doesn’t square the ball up often, not because “God hates TPJ” as other posters have concluded. And yes, I think the system would be much much improved if we had pitch f/x for batted balls and you could incorporate velocity data. That would be fantastic and would make xBABIP a lot closer to what you should expect a player’s BABIP to be.
clearly, the practicing Wicca Brian Bannister put a spell on TPJ, thus god hates him. trust me, its science…
Have you heard of financial astrology?
There are people out there who actually use planetary alignment to predict stock prices. So Bannister may in fact be a spellcaster – it’s the new science!
and that's exactly the point
The data takes one GB to be just as likely to generate a hit as the next. But does anyone actually think that a Joey Gathright ground ball is the same as a Manny Ramirez ground ball?
It has limitations
But I think you are exaggerating the limitations. I’m going to go along with the vast majority of baseball researchers and sabermetricians who agree that there is great value in BABIP and xBABIP analysis on this one.
This is just my opinion. I could easily be wrong.
by Scott McKinney on Aug 6, 2008 4:33 PM EDT up reply actions
yes, over time
they are the same ground ball.
a explanation of this maybe:
LD are created from solid contact, and solid contact allows a batter to transfer more energy to the ball
GBs are not solid contact and are more a function of how fast the pitch was thrown and the type and shape of the bat.
there is definitely an in between, but, in aggregate, it hasn’t been shown to have significant effect.
John Dewan is way ahead of you
Dewan is the guy who started the two companies, STATS, Inc. and Baseball Info Solutions, who compile the batted ball data. If you have questions about how BIS or STATS classifies batted balls, I am sure someone there can help you out.
MLB is also working on a Hit F/X system, which may provide even greater detail and further refinement to the use of batted ball data to analyze hitters.
Good points both by kcdc1 and NYRoyal
People do tend to try to find the holy grail of statistics, where they can find one be all end all stat that tells them everything they want to know about the situation. Of course in most cases it just isn’t feasible – like BABIP, which does a nice job of estimating how lucky a hitter has been but at the same time leaves out most of the effects of velocity (due to the lack of available data as mentioned above).
Hence, I would use BABIP to get a ballpark figure of how lucky a player is but I tend to ignore the difference between a say .320 and .325 BABIP and just assume both players are somewhat lucky.
And just like anything else in statistics, historical data and common logic may give you more insight into the situation. For example, statistics will tell you that if a coin lands on heads 20 times in a row, there is still an equal probability that it will land on tails on flip 21. However, a logical person will start to assume that something may be amiss with the coin itself (such as weighting) to where it will more likely land on heads.
So – if Aviles has a .371 BABIP this year, .350 next year, and .360 the year after that you may want to start assuming he isn’t extraordinarily lucky and has something figured out.
Something like a velocity measurement included in BABIP may help regulate a situation like the one I made up above.
ok
in theory, yes, each time the bat hits the ball, a unique outcome ensues. these outcomes can be categorized in various fashions which ultimately leads to the fact that two unlike events are treated statistically like the same thing. And..in specific cases you are completely right. The thing about statistics is, as the aggregate grows, those differences go away. Including things like pitch f/x data (which is becoming increasingly available), hit location (which is already available on retrosheet), and such would significantly improve the study. Definition of linedrives is also ugly, baseball info solution avgs around 20% LD% where as retrosheet has 7-10%.
But your underlying point, that BABIP isn’t a meaningful measure of luck is a fairly radical take on sound statistical methods. there are too many good studies that say you are wrong that a line drive means something different between 2 players. TPJ and alex gordon have the same line drives, they simply hit them at different rates. if you have data to support your claim, i would love to see it, but statisticians thus far would say your claim is without merit.
You know you're probably right except
they’d have trouble measuring the velocity of a TPJ LD – I’m not sure he’s ever hit one.
by jsolo on Aug 6, 2008 4:50 PM EDT up reply actions 1 recs
Tis the holy grail of sabermetrics
“The Quest for the Tony Pena Line Drive”
Relive Royals History at royalsretro.blogspot.com
I think he hits the ball a ton harder than Gator
at least TPJ has hit the ball to the fence, I’m not sure Gator has yet to do that.
The concept of progress acts as a protective mechanism to shield us from the terrors of the future. - Collected sayings of Muad'Dib
really?
Do you really think one player’s ground balls, line dries, and fly balls are the same as every other player’s ground balls, line drives, and fly balls? I know you used TPJ and Gordon as your examples, but I’d like to take Gathright and Gordon because the difference between how hard they strike the ball is even more pronounced. I unfortunately can’t find data on individual players for H/GB, H/LD, and H/FB because it seems most major stats sites are committed to the idea that you can plug in MLB averages for any player without harming your results. All I found after some quick searching was HR/F (home runs per fly ball). I know BABIP fans will protest that a HR is not technically a ball in play, but the way I look at it, a HR is a hard hit fly ball.
Gordon last 2 years:
HR/F: 8.4%
TPJ last 2 years:
HR/F: 2.1%
Gathright last 2 years:
HR/F: 0.0%
Pujols last 2 years:
HR/F: 17.7%
There are arguments to be made about extrapolating this data to H/FB and even better arguments to be made about extrapolating these trends to H/GB and H/LD, but I think it’s enormously clear from these numbers that some players hit fly balls harder than others. I don’t have time to try to come up with numbers for H/GB LD or FB, but I’d like to see the data and I’d be very surprised if a Pujols GB is just as likely to record a hit as a TPJ GB. You know, because Pujols seems to hit the ball so much harder.
Do you really think one player’s ground balls, line dries, and fly balls are the same as every other player’s ground balls, line drives, and fly balls?
No, but for most players, on average they are similar. The different nature of different players GB and FB is a limitation of BABIP and xBABIP, but it is not a fatal or even a major flaw.
This is just my opinion. I could easily be wrong.
by Scott McKinney on Aug 6, 2008 6:11 PM EDT up reply actions
exactly
For most players, they are similar. I’m actually fine with the stat, and I think it provides useful information. What actually drives me crazy is when people take a player like TPJ, watch him make only glancing contact with the ball time and again, and then conclude from his low BABIP that he is merely unlucky. Let’s use the stat, but let’s remember what it can tell us and what its limitations are. A player’s deviation from a league average BABIP or an expected BABIP may indicate that player has been lucky/unlucky, it may reflect on the quality of contact he is making, and a lot of times, both factors will be in play.
The stat is generally considered useful for "major league quality" hitters
I don’t think TPJ is really that.
We always did feel the same, We just saw it from a different point of view, Tangled up in blue.
-Bob Dylan
by Royal Kingdom on Aug 6, 2008 6:25 PM EDT up reply actions
don't you need to consider
That some players (Gordon, Pujols) are intentionally hitting fly balls much more often. Meanwhile TPJ, Gathright, etc are not. Therefore, there is a good chance that their fly balls are mistakes, not attempts to hit fly balls/home runs. Naturally, these wouldn’t go for home runs very often.
Sure
Fine, but the point is that a Pujols FB is not at all the same as a Gathright FB, and you’re ignoring something very important if you expect Pujol’s batting average of fly balls to be the same as Gathright’s batting average on fly balls.
Yeah
But the IP in BABIP implies that it doesn’t include home runs. The fact is that getting a hit on a fly ball is almost entirely location, regardless of how hard you hit it. If a fly ball doesn’t leave the park, the harder it is hit it seems like the more time it will be in the air and the more likely it will be caught. I dont know the probabilities, but I wouldn’t think it’s much more likely to get a flyball hit over the outfielders than in front of them. It all comes back to line drives.
by PopeSoria on Aug 6, 2008 7:03 PM EDT up reply actions 1 recs
There's no reason to "hate" BABIP
Like almost all stats, it tells you something and is a useful piece of the puzzle in describing and evaluating a player’s performance. No one thinks it tells you everything you need to know about a player. It’s just one useful tool among many.
This is just my opinion. I could easily be wrong.
by Scott McKinney on Aug 6, 2008 4:46 PM EDT reply actions 1 recs
i would highly recommend
http://www.baseballprospectus.com/article.php?articleid=878
its Voros McCracken who pretty much created this stuff, if you read this whole thing and still disagree, well, I don’t know what to tell you. just if you really want to prove your point, it would help you alot to move from your above narrative to some evidence. your complaints aren’t anything new, in fact, they are all in that article as common ones (which shows you are on the right track in an always health dose of skepticism). i think it will give you a better understanding of how your concerns are really the result of limited and selective observation than meaningful trends.
One thing to consider
Of course it absolutely should take solid empirical evidence to convince someone that the prevailing metrics are flawed.
However, for the sake of argument, I want to mention the Efficient Market Hypothesis which is still heavily accepted as the law that governs the financial markets. Nevertheless, anyone who’s been in the trading business will tell you that the people who make money in the markets year after year are not just getting lucky as EHB postulates.
In recent years, neural nets have been used to show statistically significant prediction capabilities further diminishing the absolute certainty that you can only make money in the markets over the long term by being lucky.
BABIP may be something like that. Once more advanced data is incorporated into the equation, we may see a clearer picture of why certain players consistently beat the BABIP predictions. Not that anyone is arguing this point, but I can pretty much guarantee that it isn’t luck.
Yeah, I've read that
And it’s about pitching, not hitting. The premise behind FIP and the like is that the pitchers have limited to no ability control the trajectory of balls leaving bats, and thus cannot control whether batted balls fall for hits. The evidence bears this out.
You’ll note that at no point in the article you cited does it attempt to draw any conclusions whatsoever about BABIP for hitters—and that’s because hitters actually DO have some control over whether the ball goes as it leaves the bat. Individual players have individual batted ball profiles. Pitchers, on the other hand, face an aggregate MLB average hitter over the course of a season.
That last paragraph really doesn't make much sense
and of coruse there are limits to McCracken’s work! That should be fairly obvious by now, seeing as all the work since then has expanded on his own stuff and proven a lot of his conclusions to be oversimplified. Such is the nature of an evolving field of study.
A mind without purpose will walk in dark places.
by NHZ on Aug 6, 2008 6:23 PM EDT up reply actions
yeah, there was a typo
“hitters actually do have some control over WHERE* the ball goes as it leaves the bat.”
I’m saying that while pitchers typically cannot affect their BABIP, hitters very much are able to do so. The existence of xBABIP calculated from LD% shows that hitters are able to control their BABIP at least by increasing LD%. I would contend that hitters are also able to raise their BABIP by hitting the ball harder.
Point,
but that hardly seems like a reason to hate BABIP. It seems like another variable in the equation needs consideration, which is hardly news. When you use one stat to evaluate a player, then you simply won’t have enough of the picture.
A mind without purpose will walk in dark places.
by NHZ on Aug 6, 2008 6:37 PM EDT up reply actions
you're right, it is about pitchers and that is an important fact
pitcher’s are not thought to have control where as hitters do have repeatable hit profiles. and i understand your argument is different players xBABIP equation is essentially different, i.e. gathright produces more hits from ground balls than other players, which is a supported notion (i have read a research paper on this but my googling has failed to find the link). but outside of speed guys i have never seem supported math that there is much repeatable variance in an mlb average equation.
for the case of fly balls, i could argue that guys who hit lots of home runs dont get any more hits in play because deep fly balls are easy to catch. fly balls become hits by hitting gaps, not by depth/velocity. since babip factors out home runs, being able to hit a fly ball far doesn’t help
ground balls again, need to be in gaps, and strength of hit gains nothing (errors get factored out too)
hitters do have repeatable abilities to pull or go opposite field by swing type, but i dont feel many can “aim” it to gaps reliably. obviously this is debatable, and i think a key point your trying to make, but, empirically, i never hear players say “i was going for the gap”, i hear “i was trying to go opposite way”.
i want to reiterate, the underlying concept of hit location needs to be considered in the future, but i think you really overestimate how much it changes xBABIP aside from extreme cases (like gathright to be fair, but even gathright doesn’t get many more IF hits if you factor out bunts which i didn’t do in my post but easily could have).
fair enough
My focus wasn’t on hit location—I conceded from the very beginning that a matter of 10 degrees is probably luck. My point was mostly that BABIP ignores how hard players hit the ball, and solid contact is very much a repeatable skill. You can argue that solid contact leads to line drives, and so this contact factor is reflected in xBABIP calculations. That’s true enough, but solid contact also leads to hard hit ground balls fly balls which, it seems to me, are more likely to fall in for hits. (Although it also seems that weakly hit fly balls tend to bloop in more than moderately hit fly balls) We don’t have the data to confirm or deny this, but it could pretty easily be calculated by anyone who knew where to find batted ball data and was willing to spend an hour with Excel.
It sounds like we can agree that different types of players do generate different types of batted balls, and these batted ball profiles are not entirely captured by GB%, LD%, and FB%. I think we might even be able to agree that these different player profiles may generate somewhat different results, on average, even for batted balls that are called by the same name. We know some players hit more FB’s than GB’s—would it be so crazy to imagine some players hit more low FB’s than high FB’s? That said, I do understand that the majority of MLB players are more or less similar and a Grud FB probably isn’t all that statistically different than a DDJ FB.
But the fact is that xBABIP doesn’t take into consideration how hard players hit the ball. In order for a player’s expected BABIP to accurately caclulabe from leage average hit rates on GBs, LDs, and FBs, one of the following statements must be true:
(1) Players don’t vary in how hard they hit the ball, or
(2) Velocity off the bat does not affect BABIP.
Either of those will likely be unpopular positions. You can take your pick, but I can’t wait until we get Hit f/x and can generate some much more meaningful xBABIP numbers.
like i mentioned earlier
retorsheet data has hit location, and by extension how hard a ball is hit to a certain degree. but it is not the easiest stuff to access on a large scale. when i get some time at work, ill run some regressions for xBABIP equations normalized by some subjective “player profile” i.e. power hitters, speed guys, etc. that may be able to give us some data insight on this issue.
in the meantime, any ideas on an easy way to parse major league hitters into these profiles. like what 4 or 5 categories would encapsulate most batters?
parsing
Oh, I don’t know that I’d formulate a hypothesis that says I expect group A type of hitters to deviate from xBABIP (calculated from LD%) by this x and group B type of hitters to deviate from from xBABIP by y amount. I’d be more inclined to simply break down hitters’ H/GB, H/FB, and H/LD and see if there do seem to be repeatable differences between players that show up over significantly large sample sizes. From there, you might see trends that show speed guys have better H/GB rates because they beat out more throws to first, but then there are guys like Gathright that are all speed and rarely hit the ball hard enough to knock it through the infield on the ground and their H/GB might actually be lower than average. I don’t know what trends you will see, but I do think it’s likely that there will be players that consistently outperform or consistently underperform MLB average H/GB, H/LD, and/or H/FB rates.
anybody know someone who compiles H/"hit type" data
i know hit type profiles, but not individual records of hits by type, this is an idea worth researching
continued
The numbers there might be interesting, but I do think we’ll have batted ball velocity and direction data available soon enough, and from there, I think people can and will come up with much more comprehensive hitter profiles than simply showing the rates at which they hit ground balls, line drives, and fly balls.
Instead of saying player A is a line-drive gap-to-gap hitter while player B has plus power and gets good natural loft from his swing, we’ll be able to say player A hits a X number of balls at elevation angles between parallel with the horizon and 20 degrees above which is 30% higher than MLB average and this range of batted balls falls for hits more than 80% of the time, and player B hits more than double the MLB average number of balls at elevation angles between 25% and 50% with velocity V or greater, and these result in extra base hits more than 60% of the time.
just curious
hit f/x data will be wonderful when it goes live, but that easily could be 10 years away (they just started development this year and its a much larger project than pitch f/x which took several years and is just starting to yield data for long term trends), but do you really think xBABIP is not valid enough data in the meantime to most hitters (i.e. not extreme cases, ichiro actually being the biggest one by far).
That's the key
While not perfect, xBABIP is both useful and meaningful.
This is just my opinion. I could easily be wrong.
by Scott McKinney on Aug 7, 2008 2:42 PM EDT up reply actions
not sure
I really don’t know how reliable a predictor xBABIP is for BABIP. That’s another thing that can be analyzed pretty easily and I’m sure people have done so.
xBABIP is derived from average hit rates for MLB ground balls, fly balls, and line drives, and I’m sure most players ground balls, fly balls, and line drives are similar to MLB averages for those groups. So yes, for most players, xBABIP is probably pretty good. But I do think that there will be outliers that actually do create different batted ball profiles that aren’t sufficiently captured in the current 3-part categorization, and xBABIPs derived from that 3-part system will not reflect that player’s true skill for generating hits on balls in play.
When we’re comparing BABIP to xBABIP to assess a player’s luck, what we’re really trying to do is to compare that player’s BABIP to his true skill BABIP. For a lot of players, xBABIP calculated from MLB average rates will be quite close to a true skill expectancy, but if a player deviates from their xBABIP in a statistically signficant manner, I think we should remember that there may be factors other than luck involved that do not show up in xBABIP calculations.
I really don’t know how reliable a predictor xBABIP is for BABIP. That’s another thing that can be analyzed pretty easily and I’m sure people have done so.
Yes, they have. And that’s why baseball researchers/sabermetricians recognize that it is a valuable, meaningful statistical tool.
This is just my opinion. I could easily be wrong.
by Scott McKinney on Aug 7, 2008 2:58 PM EDT up reply actions
ease
I guess I ruffled some feathers with the title of this post. I’ve already said this mutliple times—I don’t actually hate BABIP. I simply believe it has some limitations, and I’ve seen a whole lot of analysis using BABIP that I believe has not appropriately recognized these limitations. Yes, BABIP is a useful tool. It’s just often misinterpreted.
ill be posting some more detailed data at some point
that properly factors things like infield hits, infield flies, and home runs, and does try to see how certain “edge” players effect certain factors. there is no way for me really to break down analysis to single players, they just can’t be reliable at that scale. but i have some ideas to really test gb success rates on speedsters, flyballs with power hitters, and even bringing k rate back in (Studeman made a point in an article that player which swing really hard, they will strike out more and get more hits on balls in play).
don’t get me wrong, you have a completely legitimate point, Jack Cust and Ichiro (athletics nations has a great post on this) prove what you are saying, just ask the PECOTA guys who have struggled to understand either. but ya, i still am confident that BABIP-xBABIP is a relevant measure of scalable luck for the middle 90% of ML players and even most beyond that. I’ll get back to you with data soon.
I think one of the people who misinterpret it is you
It appears that you undervalue it greatly
This is just my opinion. I could easily be wrong.
by Scott McKinney on Aug 7, 2008 3:58 PM EDT up reply actions
To me BABIP is a great stat… here’s how I use it with my high school hitters…
Player A, you are hitting .310. You’ve had 100 ABs. You’ve struck out 22 times. You have had 78 ABs in which you have not struck out. That means that you are 31 for 78. You are hitting .397 when you put the ball in play. What does that tell us? When you hit the ball you can rake!! Let’s focus on good contact and hitting line drives. When you get behind in the count lets shorten up our stroke and use our hands to hit the ball where it is pitched.
However, in all honesty, you have to hit it to have an average. Sometimes we look to deep into stats. Agree? Disagree? Like how I approach this stat? Let me know…
Don't forget to send your broken maples to the US Forest Service.
Great stuff guys
Although I’m a little disappointed that there has been an extended debate on RR without name-calling, hurt feelings, and “scissoring” accusations. :(
OMG Banny. FWIW I am only crdtng u w/3 runs allwd bc of DDJ OMFG
As a side issue,
I am fascinated by defender positioning, and how this may or may not impact BABIP, and if that can be factored into xBABIP.
For example, what are the impacts of “super shifts” like we see employed on David Ortiz and Jim Thome?
Also, could a diverse hit distribution increase one’s BABIP? It would seem to me if a hitter can hit equally well to all fields, including both foul lines, that should possibly increase his BABIP by forcing the defense to account for more ground.
What got me thinking about this was Aviles. Without looking at his actual hit data, it just seems, from naked observation, that he has been uncanny at sending hits in virtually all zones of the playing field, and maybe this could explain his higher than normal BABIP, or at least explain 25 points of it, or some such amount. Mike has been especially adept at hitting balls down both foul lines.
I remember Hal McRae saying (probably in jest) that he liked to aim for the foul lines, because if he succeeded, it was a hit nearly every time, and if he missed, it was only a strike and he would have another chance. Aviles seems to hit somewhat like McRae to me; that’s what brought Mac’s joking comment to mind.
"Aviles seems to hit somewhat like McRae to me"
I hadn’t made the connection, but… yeah. Yeah, he does.
Sarcasm™. It's the new gravy.
"Aiming for foul lines" = "my timing sucks"????
OMG Banny. FWIW I am only crdtng u w/3 runs allwd bc of DDJ OMFG
by Matt Klaassen on Aug 8, 2008 2:54 PM EDT up reply actions
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