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Which St. Louis Cardinals Were Lucky on Batted Balls in 2011?

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Batting Average on Balls In Play (BABIP) is a stat measuring that which its name suggests. It is a stat narrower in focus than good ole batting average (BA). Whereas BA measures the rate at which a player hits safely minus walks and hit-by-pitches, BABIP measures a player's average on those balls put in the field of play, excluding home runs. It is a stat that helps put a player's numbers into context; namely, it helps to determine just how sustainable a player's production has been.

One can get an idea of how lucky a hitter has been by glancing at his BABIP. Typically, the average BABIP is around .300; in 2011, it was .295 for all of Major League Baseball. One should not stop there in evaluating a player's BABIP. The next step is to look at the player's batted-ball profile. Quality of contact has an effect on BABIP. The harder a ball is struck, the more likely it will result in a hit. A batter will have a higher BABIP on line drives (LD) than on grounders (GB) than on fly balls (FB).

According to Fangraphs, in 2011, Major League hitters posted the following BABIP by batted-ball type:
















It is possible to get an idea of how lucky or unlucky a player's numbers were in a given year by comparing his BABIP and batted-ball profile to that of Major League Baseball as a whole. Today I thought we might utilize some of the work being done over at The Hardball Times on expected BABIP (xBABIP) and how it relates to BA, on-base percentage (OBP), slugging percentage (SLG), and on-base plus slugging (OPS) to see which Cardinals were lucky and which were unlucky on balls in play in 2011 and how that luck affected their production.

Using information from Baseball Prospectus for the years 2002 through 2008, Chris Dutton and Peter Bendix created a formula for calculating xBABIP. In an article published at The Hardball Times entitled "Batters and BABIP" (that is very much worth reading), the duo discuss their formula, which includes factors for player speed, hitter eye, pitches per extra-base hit, line drive percentage, fly ball/ ground ball ratio, contact rate, spray distribution, pitches per plate appearance, park, year, and batter handedness. Dutton and Bendix offer a simplified description of their regression model, a model that is meant to "determine the relationship between each factor and a hitter's BABIP."

Essentially, the model takes seven years worth of data and compresses it into a single formula that inputs the variables above and spits out a predicted BABIP. Using this, we can compare players' actual and predicted BABIP to identify instances in which a player significantly outperformed or underperformed his expectations. Furthermore, we can use the model to strip luck from the equation and calculated a "luck-neutral" measure of BABIP.

After the 2011 regular season came to an end, Jeffrey Gross revisited the Dutton and Bendix xBABIP formula for The Hardball Times's fantasy baseball arm, "THT Fantasy," in an article entitled "Looking ahead: 2011 xBABIP-adjusted batting lines." Using their formula as his starting point, Gross calculated players' xBABIP and then calculated players' xBA, xOBP, and xSLG based on xBABIP. There are some problems with doing such calculations, which Gross discusses in his article. I encourage you to read the Gross article to review these problems so that you digest this post with the proper salt-grainage.

Aside from the problems in calculating xBA, xOBP, and xSLG that are discussed by Gross, there is also the fundamental problem in projecting a player's 2011 xBABIP onto his 2012 season, which the author explains:

xBABIP analyzes past luck based on past results, but it does not forecast the underlying elements that go in to figuring out the difference between skill and luck-based reality for future situations. To the extent a player's expected future walk rate, strikeout rate, groundball rate, flyball rate, infield flyball rate, line drive rate and home run rate—to name a few areas—could/will deviate next year from this year, xBABIP will not reflect those deviations.

Hence, if you think a player's line drive rate will increase in 2012 compared to 2011, then you should assume that his real expected future BABIP will be higher than his xBABIP. Let's call this difference nominal xBABIP and real xBABIP.

With this context, I thought we might take a look at the St. Louis Cardinals' numbers from Gross's xBABIP spreadsheet (which can downloaded as a Microsoft Excel spreadsheet via the article which is linked to above). In looking at Gross's spreadsheet, I noticed some discrepancies between his spreadsheet's SLG for a player and that player's SLG according to Baseball-Reference and Fangraphs. So, I went through and calculated a player's xSLG on my own by adding Gross's xBA to each player's 2011 ISO. Because I use only OPS on the chart, I then added this simple xSLG to Gross's xOBP for the players' xOPS.

Lastly, Gross limited his calculations to those player's with 300 or more plate appearances since this is the threshold at which a player's batted-ball rates will stabilize (except for IFFB%). Because only nine Cardinals currently penciled in on the 25-man roster reached the 300-PA threshold in 2011, only nine Cardinals are included in the graph. I've listed the Cardinals players in descending order from luckiest to unluckiest.








































































The sustainability of David Freese's production has often been questioned due to his abnormally high BABIP and relatively low walk rate (6.6% in 2011 and 7.0% for his MLB career). As we prepare for 2012, however, concern about the ability of the newly signed Carlos Beltran to hit on par with his 2011 production should weigh heaviest on Cardinals fans' minds. With Daniel Descalso, one must hope that he shows improvement in his approach at the plate and contact in his second full big-league season. As for Matt Holliday and Jon Jay, any expected fluctuation in luck seems likely to be minimal. That being said, Jay's low walk rate (5.6% in 2011 and 6.3% in his big-league career) make his value much more closely tied to his batted-ball luck.


It's incredible to me that Lance Berkman, he of the .959 OPS, was ever so slightly unlucky in 2011. Skip Schumaker was a bit unlucky, as well. This is somewhat heartening as the aging slap hitter remains dependent on a BABIP of .320 to manage a pathetic .685 OPS. Lastly, there is Rafael Furcal, who seems positioned for a much better 2012, given good health and the batted-ball luck one would expect. Neither is a given, but if he gets both, Furcal has the potential to be a very valuable player in 2012.


Molina posted the best offensive season of his career in 2011. It is a breakout that I've been skeptical of for various reasons. How repeatable his power increase will be remains an open question. Molina's .160 ISO in 2011 is double his .080 ISO from 2010 and 54 points higher than his prior career high of .106 which he put up in both 2005 and 2006. The other question is BABIP. In 2011, Molina posted a career-high BABIP of .311 that is 25 points higher than his career average of .286 but not horribly out of line when compared to the prior three seasons: .281, .309, and .310. Gross's xBABIP formula shows that Molina's BABIP was neither lucky nor unlucky. Hopefully this means we'll be seeing more offensive production near Molina's 2011 level.


While this exercise in BABIP may help to boost and temper expectations for players heading into 2012, it is important that we keep in mind that this chart shows how a player fared on batted balls during the 2011 season. A player's luck in a given season can take a sharp turn and his 2012 BABIP-xBABIP gap may look much different than the chart above. What's more, a player's improving in plate discipline, power, and/or quality of contact can lead to better BABIP numbers moving forward. Even with these caveats, looking at players' BABIP-xBABIP gaps provides an interesting insight into the 2011 season.