Adam Jones is Back

Adam Jones is a remarkably consistent player.  From 2011 to 2015, he hit at least 25 homers, scored at least 68 runs, drove in at least 82 runs, never had an OPS under .780 or wOBA under .334, and played fewer than 150 games only once.  Over that time frame, Jones also put up three 4+ WAR seasons and established himself as one of the best outfielders in the American League. He may not be elite and he does some maddening things (flailing at sliders in the dirt is never a good look), but he's very good.  Certainly good enough to be a multiple time All-Star and the face of the Orioles franchise for the better part of a decade.

This consistency, however, belies what has been a slow but steady decline in nearly every stat category.  Since 2012, his games played, plate appearances, batting average, on base percentage, slugging percentage, wOBA, WRC+, runs, and stolen bases have ALL declined in each season. Save for one more home run in 2013 than in 2012 and a .314 BABIP in 2013 as opposed to a .313 mark in 2012, the pattern exists for dingers and BABIP as well.  In 2016, this decline has culminated in what would easily be Jones’ worst season since at least 2009. 

So far this season, Adam Jones has been worth less than a replacement player, and he is at career lows in nearly every rate stat.  For two months, he was bad.  Not just bad for Jones, but bad for anyone.  A quick look at the differences in his batting average by location between this season and last tells us all we really need to know.

fsdsdfsdf

Jones has struggled mightily with pitches on the corners.  He was able to turn on pitches inside last season to great effect and had decent plate coverage on the outside corner, but both of those abilities seem to have left him in 2016.  One of the biggest criticisms that Jones has faced in his career is an inability to lay off pitches down in the zone, particularly down and away, and he’s done nothing to dispel that so far this season.  Unfortunately for Jones, even pitches in the strike zone have proven more challenging this season than last.  While he has done well up and low and away in the zone, Jones has struggled with pretty much every other pitch location in the strike zone.  That's a good way to hit .240 with 9 homers.  

The reality that Jones is a 30 year old center fielder with over 1200 games under his belt and declining statistics across the board. His body may also be beginning to break down, as Jones has struggled with injuries the last two seasons, missing 25 games in 2015 and 4 so far in 2016 (though he played through a rib injury early in the season that clearly affected his game).  While his BABIP is over nearly 50 points lower than his career, he did put up a below career average .286 BABIP last year as well.  He has certainly gotten somewhat unlucky with his batted balls this season, but he also has a career low line drive rate and a career high ground ball rate.

Over the last two weeks, however, Jones has looked much like his old self.  Manager Buck Showalter (somewhat inexplicably at the time) moved Jones into the leadoff spot on May 27, and since then, Jones has hit a much more Jones-like .306/.327/.612 with 4 home runs, 12 RBI, 11 runs scored and 3 doubles.  While the .327 OBP isn't an elite mark for a leadoff hitter, not to mention the fact that changing lineup spots and immediately improving seems a little bit like a spurious correlation, it is clear that Jones has begun to rediscover his old stroke.

A closer look under the hood supports this, and not just in the last twelve or so games.  Jones currently sports the highest walk rate of his career AND the lowest BABIP.  He is hitting the ball harder than in 2014 and 2015, has the lowest pop-up rate of his career, and is 2 percentage points below his career HR/FB rate.  Even more interesting is the fact that he is swinging at fewer pitches out the strike zone than in any year since 2009 and is basically in line with his career swing and contact rates overall.  Essentially, he has been the same player he’s always been and, in some ways, even better.  While he has struggled to put up the kind of numbers we are used to seeing from him, there is certainly reason to think that the past two weeks are the start of an overall positive trend.

In all, the 2016 season offers both hope and worry for Jones and his future production.  It would not be a stretch to call his season unlucky by any means, and he has begun to put up the kind of stats we would expect to see.  It is, however, difficult to ignore the fact that his offensive game is in decline overall.  Combine that with increasing injury concerns and age, and it is more than possible that we have seen Jones’ best work.  That said, I think he can stave off Father Time for a while longer.  He's hitting the ball with more authority and looking much better at the plate than he did early in the season, not to mention the fact that his surface stats are finally starting to catch up with his peripherals.  With Manny Machado likely facing a suspension for charging the mound against Yordano Ventura Tuesday night, the O's are going to need the current Adam Jones to keep being the old Adam Jones for a while.

How Horizontal Trajectory Affects BABIP

Batting average on balls in play has long been used as a yardstick for batters. Players whose BABIP is higher than the league average and their historic stats are said to be fortunate and likely to see a major drop in production when their luck runs out.

It occurred me that BABIP could be used as a defensive metric when Ben Lindbergh used it, calculated in groups by horizontal hit trajectory, to illustrate the improvement in the quality of Major League fielders over many decades. Considering an emphasis on defense (it’s said that it’s cheaper to prevent a run than to score one), the rise of the infield shift, and more athletic and able players, it’s certainly conceivable that defenses today are better than they were in the 1970s.

However, that’s not particularly useful for the task of roster construction today. Sure, Manny Machado may be a better third baseman than most of the professionals 50 years ago, but how much better than his active peers is he? And how much is he worth to the Orioles in run prevention and ultimately revenue generation (i.e., as the General Manager, how much I should pay him)?

To begin to answer these questions in a new way - good but volatile defensive metrics exist, remember - I sought to categorize balls in play by horizontal angle as Ben and partner Rob Arthur did, but also to build in the ability to separate fielding teams from one another. This would allow me to strip out BABIP against the Orioles (or any team) by horizontal angle - absolving Manny of balls hit to areas of the field that he isn’t responsible for, and better indicating which hits came in his domain - and compare it to other teams or the league generally. As such, we are able to see which teams are best at fielding in which locations around the diamond, and better ascribe that to specific players.

The wonderful and regularly-updated PITCHf/x database produced by Baseball Heat Maps was my primary source of information, as it included coordinates for the location of every ball in play since PITCHf/x was introduced in 2007. One thing to note - the coordinates are for where the ball was played by a fielder, not where the ball first hit the ground. A liner that hits the grass and reaches the outfield wall before being picked up in the warning track is shown as having a y-coordinate of the warning track, not of the grass. That’s a big issue if you’re measuring hit distances, but not so much if you’re measuring hit angle; rarely do balls take sharp turns in the field of play before being played.

The other critical source that needs recognition is this Hardball Times article by Peter Jensen from 2009. The PITCHf/x data contains no information to put hit coordinates into context, and the coordinates make little sense to someone used to the normal four-quadrant coordinate plane. Home runs typically have a y-coordinate of less than 20, while bunts have a y-coordinate of around 200. Peter Jensen answered a ton of questions by publishing his normalized estimates of home plate coordinates in each stadium, which allowed me to manipulate the data to orient the field of play in a way that made sense with a regular plane - and to calculate the horizontal angle of each ball in play.
If you visit that Hardball Times link, you’ll notice that the home plate coordinates were from 2008, before the Marlins, Yankees, and Mets began playing in their new stadiums. To avoid complicating things even further, I elected to use the 2008 context for all of the PITCHf/x information. The home plate in most stadiums is just about at the y-coordinate 200.0, so I can’t imagine the MLB Gameday scorekeepers would deviate much from that in three parks.

With all of this information (plus hits and outs) properly paired and grouped, we can see how well the Orioles do in the field against balls batted to different parts of the park:

They are, in this case, very much average. In fact, most teams are:

There just isn’t much variation in BABIP by hit angle across Major League Baseball, likely because certain balls in play are going to be hits nearlyall of the time. It doesn’t matter who’s on your team; a grounder to the shortstop is always going to be an out (or an error, which isn’t counted here), and a liner to shallow left center is always going to be out of reach of everyone running towards it.

Since Manny Machado’s call-up, we might expect to see the Orioles leading Major League Baseball in BABIP allowed by hit angle. He is, after all, one of the best fielding third basemen in baseball, with stellar range and a cannon for an arm - all while playing shortstop throughout the minors. Surely a player of Manny’s caliber in the field would hurt the ability of a batter to reach base safely if he were to put the ball into play, right?

Actually, Manny’s call up hasn’t materially affected the Orioles’ ability to prevent hits once the ball is in play.

To get a better sense of the Orioles’ ability to prevent hits when a ball in play might be expected to be played by their third baseman, I pared down the data to only include a hit distance inside or just out of the infield - ones that are played by the third baseman or by the outfielder rushing in to back up the third baseman once a ball gets through his area - and balls hit between -55 degrees (which is just foul) and -22.5 degrees (which is halfway between the foul line and second base). This seems like a good set of balls in play to assign as the responsibility of the third baseman. I included balls past -45 degrees because MLB’s data tracks where the ball is played, not where it lands, so a live ball that gets picked up in foul territory would be played at a less than -45 degree angle.

I calculated the league average BABIP using this set of data that could reasonably considered specific to third basemen, as well as the BABIP of each of the thirty teams in order to determine the standard deviation from that average. The data follows a roughly normal distribution, although Toronto is apparently so bad at third base as to be a significant outlier that made me think I had very heavy skewed data.

League average BABIP on balls to third base and in the infield is 0.117 - very low! Which is to be expected, given that those are balls played in the infield and therefore did not get past the third baseman. The Orioles, since Manny’s callup, have allowed a batting average of 0.122 on similar balls, or about 0.41 standard deviations above average. This is well within the range of possibilities via random chance, meaning that there might not be any cause for alarm regarding infield defense.

Rather, what this means to me is that the dazzling, rangy plays that it seems like Manny makes on a regular basis might not have a material affect on outcomes. Even though Manny can field a bunt on the grass and fire to first from an impossible angle to garner a putput, he doesn’t do so with enough regularity to dramatically affect the Orioles’ ability to suppress batting average on balls hit to him - and really, nobody in Major League Baseball does.

But wait - that was only on balls fielded in the infield. Perhaps Manny is unusually good at stopping balls hit towards him from reaching the outfield, and makes more outs on those than normal. In fact, the Orioles fare slightly worse in this category, with a BABIP allowed of 0.335, a full standard deviation above the league average of 0.322. Here, again, I have to consider the possibility that despite his incredible range, Manny doesn’t have supernatural talent at preventing an unusually high number of balls in play hit towards him from becoming hits.

There is one final way to slice the data that might shed a more optimistic light on Machado’s fielding. Since his callup on August 9, 2012, Manny has missed large chunks of time for two separate knee injuries. By limiting the sample further to include not only games since Manny’s callup but also only games in which Manny was an active player, we see exactly how his presence on the field has affected the Orioles’ defense. At the same time, this limits the sample size significantly, the information is far more prone to drastic swings, and our conclusions should be couched to recognize that our understanding of Manny’s defensive ability is still developing.

The first thing I notice is that the Orioles are about as adept at preventing balls in play from becoming hits with and without Machado on the field - and they’re about league average at it. Minor fluctuations visible in this chart are more than likely the result of small samples rather than Manny being better or worse than fielding balls in play than average.

Remember, this is not to say that Machado is somehow overrated or that we should reevaluate our understanding of defensive ability. Not one team was noticeably well above average in preventing hits on balls in play - but some were noticeably worse, particularly at first base. Manny’s greatness comes from many things, and although he can snag a hard grounder up the third base line arguably better than most players, those web gems are few and far between for a reason: they’re incredibly difficult plays to make. Turning them once in a while is awesome, and proves that the ability is there, but doesn’t materially affect how often a player who hits them gets on base against the Orioles. Some balls in play are just impossible to turn into outs.

* * *

In addition to slicing this data up by pitching team, it’s possible to examine in-play occurrences in each park. The results of doing so are expected to be pretty similar to splitting the data up by pitching team - after all, nobody plays at Camden Yards without the Orioles, so roughly half of the balls in play in Camden Yards occur with Adam Jones, Manny Machado, and J.J. Hardy on the field.

Where this is interesting is on the margins. All Major League infields are identical in shape and size, but outfield corners can be in any variety of shapes and any distance from home plate.

For instance, the right field corner in Fenway Park, where the outfield continues past the foul pole, seems to cause fielders some trouble. There’s even evidence that BABIP is unusually high when the ball is hit at a 20- to 30- degree angle, which for alignment is about where the second baseman would usually play. Whether that abnormality is due to liners over the second baseman’s head dropping in because the right fielder is in position to cover Pesky’s Pole, or due to teams rarely shifting at Fenway (doubtful, seeing as how the Red Sox are among league leaders in runs saved via the shift, the Orioles and Yankees are two of the teams that most commonly employ the shift and play in Fenway often, and because the Red Sox employ David Ortiz, one of the most shiftable players in baseball).

Unlike Fenway, Yankee Stadium features a below-average BABIP on balls hit sharply to right field. Perhaps the very plain dimensions of Yankee Stadium are easier to play than the quirky ones of Fenway - or perhaps long fly balls that would fall on the grass or hit the wall in most stadiums carry into the right field bleachers in the Bronx, lowing the number of hits and at bats considered in the BABIP calculation while leaving the balls in play that are easily fielded.

BABIP, or Why Some People Might Make the Wrong Statistical Conclusion

Note: Most of this article was written in early September, so the information on Morse may not be especially important.  However, my desire to finally complete this post is to address something broader about statistical analysis and Morse is a good example to use in this instance.

Wander around the internets or, perhaps, spend time (a lot of time) at the ball park and you may hear about how certain hitters have been lucky or unlucky.  The comments could be as specific as how line drives are being hit straight at the third baseman, less specific by mentioning BABIP, or even less specific by indicating how performance marks in general are not up to peak career marks.  What I want to comment on is the conversation right in the middle: BABIP.

The general idea of a player being unlucky with with respect to Batting Average of Ball In Play (BABIP) is based on the population trend that the value is consistent.  That is if a component of the population is hitting 50 points over their average, they tend to revert back to their average.  Mike Morse's career BABIP is .333 while his current season BABIP is .267.  If Morse has suffered a simple fluke in the distribution of his batted bats (like most players most of the time) then we should expect that he will revert back to a .333 BABIP in the future.

That expectation dismisses his current performance of 226/283/410 and suggests he should be more in line with 280/340/480.  Having hits drop in, as you can see, can greatly increase a player's worth.  However, we need to remember that this assumption is based on the measure of a population as compared to the measure of individuals.  In other words, a forest might look incredibly healthy from afar, but up close you see that a couple trees are dying.  It is a matter of resolution.

In general, it is fine to say that given such a broad resolution that it is plausible to expect a player like Mike Morse to bounce back from such an uncharacteristic BABIP.  If this is as far as you go, then you might forget what actually impacts changes in BABIP.

The three major components of BABIP are:

1) Defense - Although the unbalanced schedule makes for the possibility of BABIP to be team specific due to differences in stadium design (e.g., Fenway) or general differences in defensive aptitude, usually it all washes out.  Defenses are very similar to each other and there is enough overlap between schedules that a batter essentially faces a league average defense over the course of a season.

2) Random Variation - For no good reason that we have been able to discern, sometimes a player tends to hit more balls in defensible territory than normal.  There seems to be little ability for a hitter to deftly place a ball to an empty spot on the field.  Think of the crazy shifts that are employed today and how no MLB hitter can exploit them with any regularity.

3) Talent - Talent is a combination of foot speed, batted ball type, as well as just how those batted balls travel.

Below is a good video that briefly reexplains what I just noted:

I do want to drill a little deeper on the talent portion of BABIP.  Why?  Because that is what changes in practice.  These chances in BABIP occur because players get stronger, more experienced, and refine their approaches.  Players can also age, adversely affecting their production, and get injured.  I compact them down to three things:

1) Foot speed - as a population, players tend to see a decrease in BABIP over the course of their careers as they lose speed.  This effects grounders almost exclusively and this trend tends to be very small due to how good defense is in the Majors unless a physiologically catastrophic event occurs to the player.

2) Batted Ball Type - again, as a population, balls that are grounders become hits 23% of the time, line drives fall in 69% of the time, and fly balls are wonderful for the hitter 13% of the time.  A player who tend to have more line drives than fly balls will tend to have a higher BABIP.  If something about the player changes the types of batted balls he hits, then that BABIP will change as well.

3) Types of Batted Ball Type - As we all know, a fly ball is not a fly ball is not a fly ball.  We tend to think of a Chris Davis moon shot flyball differently as opposed to a Nick Markakis fly ball.  Within grounders, line drives, and fly balls we get a lot of variety within those classifications.  Those differences can mean a great deal with respect to how well a player will perform.  Soft liners are easily to turn into outs than hard liners.  Shallow flies tend to be caught more often than deep flies (and we are not even considering how deep flies can turn into home runs).

Now, let us use Mike Morse as a vehicle for this discussion.  Below is a table indicating his previous performance.  His 2013 time with the Orioles is not included because I find performance after a trade to be not important with respect to making a trade because that portion is unknown.  Later we will revisit what he did for the Orioles.

Year	Age	Tm	PA	2B	3B	HR	BB	SO	BA	OBP	SLG	OPS+	BABIP
2005	23	SEA	258	10	1	3	18	50	.278	.349	.370	97	.341
2006	24	SEA	48	5	0	0	3	7	.372	.396	.488	131	.421
2007	25	SEA	20	2	0	0	1	4	.444	.500	.556	184	.571
2008	26	SEA	11	1	0	0	1	4	.222	.364	.333	91	.400
2009	27	WSN	55	3	0	3	3	16	.250	.291	.481	101	.303
2010	28	WSN	293	12	2	15	22	64	.289	.352	.519	133	.330
2011	29	WSN	575	36	0	31	36	126	.303	.360	.550	147	.344
2012	30	WSN	430	17	1	18	16	97	.291	.321	.470	112	.339
2013	31	SEA	307	13	0	13	20	80	.226	.283	.410	98	.267

Provided by Baseball-Reference.com: View Original Table
Generated 9/1/2013.

A good exercise is now to run through the different variables because upon looking at his .267 BABIP so far in 2013, we would think that he has been incredibly unlucky.  Why?  Because the greater population of baseball players tend to perform at their established career BABIP.  In other words, we should expect Morse to have a BABIP of about .330 to .340 if what has transgressed so far this season is due to random variation (aka balls finding gloves).

However, has his talent level changed?  If it has, then we would expect the BABIP to not be random and instead be a characteristic of that player.

Foot speed

Although Morse began his MLB career as a shortstop, foot speed was never a quality he enjoyed.  He may be slower, particularly with his quad injury that forced him on the disabled list in June through almost all of July.  That said, running out ground balls was simply not a major part of his game.  True, a loss of speed will negatively affect his performance somewhat as a loss in footspeed would result in a slight alteration in singles and doubles, but probably nothing profound.  His game is not a singles game and we do not see a great increase in hitting singles from would-be doubles.  I severely doubt foot speed has caused his BABIP to drop.

Change in Batted Ball Type

Another possibility would be that a leg injury could impact his mechanics and result in him creating a different proportion of fly balls.  The table below shows his batted ball types over the past nine seasons.  Again, I am not considering his time in Baltimore.

Apparently, the table failed to load properly when posting.  I will have to wait until tonight to correct it.  Until then, feel free to go over to Fangraphs here and see how similar his distribution is to his prime years to date.

From that table, nothing appears to be out of the ordinary.  It also appears that from this there is no great reason why we should suspect his poor hitting performance with respect to his BABIP is not a product of a change in batted ball percentages.

Types of Batted Ball Type

One thing that concerned me about Morse was that leg injuries can greatly impact power transfer from a player's lower half into the bat.  If that transfer is off, then swings become all arms, which greatly reduces the distance of those balls.  Even when you see a guy like Chris Davis flip a bat into the stands with only his arms, you can still see a portion of his power is coming up through his legs.  Below is a table showing Morse's average distance for his line drives and home runs.  These numbers are somewhat difficult to find on your own, so I decided to include his few Baltimore data points here.  To be clear here are the three designations for 2013: (a) Seattle before leg injury, (b) Seattle after leg injury, and (c) Baltimore.

	Line Drive (n)	Fly Ball (n)
2011	266 ft (72)	316 ft (141)
2012	249 ft (59)	309 ft (81)
2013a	276 ft (31)	302 ft (54)
2013b	230 ft (12)	298 ft (16)
2013c	189 ft (3)	269 ft (4)

When I look at the data above, I see potential red flags.  Looking at 2011, 2012, and 2013a, we see some variation in Line Drive distance, which I believe is normally highly variable.  That does not concern me.  What does concern me is the decrease in Fly Ball distance, which I think is a more stable number (again, though, I have not studied this, so this is a bit anecdotal).  I think his early season numbers this year were inflated due to solid line drive contact.  The decrease in his fly ball percentage makes this difficult.  For every 10 feet you lose in your average fly ball distance, the player experiences about a 20% decrease in home runs.  Now, I would not write him off completely long term, but I would be concerned that he might be a player who will flame out early even if he is healthy.

That said, the long term prognosis is not what we have interest in with Morse as a trade candidate.  As a trade candidate, we have more of a concern with respect to how well he performed after coming off the disabled list.  Now, 12 ground balls and 16 fly balls are a very, very small sample size (I assume, I do not know how many batted ball events are needed to stabilize one of these numbers, but I assume this is an accurate statement), but a drop of 46 ft in line drives and his fly balls falling below 300 ft were big concerns of mine.  That suggests to me that even though he was healthy enough to play baseball, he likely was not healthy enough to be Mike Morse and that is a significant issue.  That drop was a major reason why I panned the Avery for Morse deal.  The distance is an illustration that even though his BABIP is low in comparison to his career line, his current BABIP might be completely founded in a change in ability due to his leg injury.

Population vs Individual

This is a major issue for some who are new to baseball statistics and projections.  A good rule of thumb is to expect players to behave like the population of players behave.  This really is true about any population model.  It makes sense for drug trials and it makes sense for ecosystem assessments.  You will often be right more often if you make this assumption.  However, if you are able to assess a single individual, you may have some ability to tease apart the causes for a change in behavior.  For instance, your patient not doing well in the trial may have a secondary issue that causes him to respond differently to the drug.  Or perhaps, your pristine habitat does not act like other pristine habitats because someone is dumping all of their excess paint into the stream.  In Mike Morse's case, it should not have been expected of him to bounce back perhaps because there was an underlying cause (leg injury) and evidence (lack of batted ball distance) that suggested there were real issue there.

In other words, one should not be fundamentalist when it comes to assigning population responses to an individual.  It has been shown consistently that players suffer from variability in their performance.  Much of that wobbles back and forth each year as a result of chance, but there are also real issues that can cause a player to deviate from the expected path.  It is important to recognize these things.  Luck is not all.

How Has Mike Morse performed for the Orioles?

In 27 plate appearances, Morse has three singles and a walk.  He has a slash line of 115/148/115 and a OPS+ of -27.  His offense has been worth -4.1 runs, roughly meaning that in those 27 appearances he was responsible for about half of a loss in comparison to a replacement level player.  He saw 49 innings in the outfield and was credited for -7.5 runs when extrapolated out to a full season.  In other words, he played poor defense.  In a general sense, he has done nothing to improve the team and he cost the team a million dollars.  As you know, money is not a thing that is spent wildly in Baltimore, so losing a million hurts a little bit.  Add that to the loss of a genuine 4th/5th outfielder with options in Xavier Avery and the deal looks slightly worse.  Twenty three year old toolsy, poor skilled outfielders are not exactly a dime a dozen, but they are probably close.  Avery certainly has value and a scant chance at become something more like a second division starter.  With the paucity of talent in the Orioles upper minors, it does potentially hurt a little to give up on Avery's slight promise and his certain versatility.

What Happened to Xavier Avery? 

Avery logged 14 plate appearances over three games in AAA Tacoma.  He walked once, had a sacrifice hit, and had six hits, including a home run and a double.  He also managed a stolen base.  He started once at each outfield position.  For those three games, Avery was a wunderkind.  He was not added to the expanded roster in Seattle.  He is expected to be given a legitimate shot at the fourth outfield position with the club next year.  He is fully expected to log time at some point in Seattle next season.

Daniel Cabrera's Batted Balls

Daniel Cabrera pitches tonight against the typically patient and talented Red Sox offense. He has been able to string together six seemingly good games in a row. Several of his game scores were not impressive during this period as three were in the 50s. This helps give the impression that he has changed and is a much better pitcher. In fact, quotes are beginning to be dug up by unnamed scouts declaring the Cabrera has turned the corner and is now a legitimately fulfilled talent. Now, he might be. He might actually be a very good pitcher now. He certainly is throwing differently as in he has really imploded in any start this year other than his first. He has certainly comes close several times during this stretch though. I mentioned in a previous post how his peripheral numbers do not make any sense and that he is more truly a 3 or 4 and not an ace as his current ERA suggests. Today we are going to look more into his batted ball data.

Battered Balls
I have taken his batted ball data from baseball reference and normalized it to the 2007 AL batted ball data. As you may realize, changes in batted ball data can be explained by: 1) changed pitch run (this is quite rare for an established pitcher to drastically change how his pitches run to the point that his batted ball data would also change dramatically), 2) improved defense (this can greatly improve or hinder BABIP, but typically teams do not make wholesale changes in defense from year to year), and 3) pure luck (batted balls are typically not uniform in their dispersion over the course of partial seasons and sometimes full seasons). Using that base knowledge, we are able to discern potential discrepancies in Cabrera's batted ball data.

Basics
Infield and Outfield - BABIP of balls hit to these respective areas
BABIP - Overall batting average of balls in play
First, Middle, and Third - BABIP of balls hit to these areas of the field, including the outfield
GB, FB, LD - BABIP of balls classified as groundballs, fly balls, and line drives.

Results
When you look at a graph like this, you look for extreme differences. A difference of about 20% is a good rule of thumb for identifying extremes. Using that as a benchmark we have these weird BABIPs: overall BABIP, infield, third, middle, groundballs, and line drives. It is good to categorize the data because it can give us better ways to identify what is going on here. BABIP alone shows that something weird is going on, but the rest of the data suggests why.

Fielding cannot really be relied on as having changed. Much of the difference looks like it applies to groundballs going up the middle and to the right as well as line drives in the same area. The only difference in the infield is Luis Hernandez and he got benched because of poor defense. There are seems to be little reason to think that Brian Roberts or Melvin Mora have been significantly better at what they do this year. In the outfield, we have Adam Jones and Luke Scott. I think we can say Luke Scott improved our defense. I also think Adam Jones is better than Corey Patterson. I also think the differences between this year and last is just not enough to explain the differences. What is telling is that difference in line drive percentage. You can explain away grounders by suggesting Cabrera is inducing weak groundballs, which is plausible (though I doubt with his repertoire of four seamers--it should be noted that althought I see little difference between pitch quality this year and last beyond placement . . . I could certainly be wrong about the quality of his stuff compared to last year). You really can't explain his dramatic difference in line drives. Line drive success has little to do with good defense and more to do with simple luck. Not only is his line drive percentage unsustainably depressed . . . the line drives that are actually hit off him are going straight to his defenders at an unsustainable rate. Add this to my doubts about the groundballs being more effectively covered and you have a guy who is ripe for a downturn in success.

Conclusion
I will be bold and predict that Cabrera has trouble tonight. Some people will point to his 116 pitches in his last outing. Others will just say he is no good. Or maybe he will balance a dozen plates and toss 8 innings and give up a run or two. My guess though is that he will eventually drop down to about an era in the high 4s by the end of the season. He has been lucky avoiding baserunners and getting out of jams. His LOB% is about 10% above where it should be. His K/BB is 1 when people are on base, which is a third worse than league average. There just isn't much to like with him going up against a team with the 3rd most walks, best obp, and the best slg in the AL. Anyway, cross your fingers