How Lucky or Unlucky Were the St. Louis Cardinals on Batted Balls in 2013?

Batted ball fortunes have been a part of the baseball conversation for decades. The notion that a batter can put good swings on the ball and make solid contact yet still hit the ball (or many balls) at fielders has long been a baseball reality. Nothing testifies more eloquently to this fact than Crash Davis in Bull Durham:

Do you know what the difference between hitting .250 and .300 is? That's 25 hits. 25 hits in 500 at-bats is 50 points, okay? There's six months in a season. That's about 25 weeks. That means if you get just one extra flair a week, just one--a gork, you get a ground ball, you get a ground ball withe eyes! You get a dying quail, just one more dying quail a week and you're in Yankee Stadium.

One of the sabermetrics' creations quantifies Crash's monologue. Batting Average on Balls in Play (BABIP) looks at how a player--batter or pitcher--fared when the ball was put in play. The formula for BABIP is:

(H - HR) / (AB - SO - HR + SF)

In recent years, it feels like one cannot discuss a player's performance without addressing his BABIP. The adjectives often employed in such discussions are "lucky" and "unlucky." Such labels are usually oversimplifications.

There are multiple layers to BABIP. As a general rule, .300 is about average. In 2013, for example, the league-average BABIP was .297. This reference point is just the tip of the iceberg, though. Players who are more fleet of foot tend to have a higher BABIP than average. The same is true for players who hit more line drives and ground balls than average. In other words, a high BABIP or low BABIP may be due, in full or in part, to a player's skill set.

As interest in BABIP has increased, so have efforts to create a formula by which we can calculate a player's expected BABIP (xBABIP). One such formula, created by slash12 and shared at Beyond the Box Score, has been called the most accurate by some. However, VEB community member Paulspike thought it could be improved upon and created his own mousetrap, which he believes is even more accurate.

Paulspike was kind enough to share his xBABIP spreadsheet with me. It is based on LD%, GB%, IFH%, Fangraphs Spd, Contact %, and Isolated Power (ISO). As you can see by its inputs, this xBABIP formula takes into account a player's line drives, speed, and ability to make solid contact. Using Paulspike's formula, I calculated the xBABIP for the 2013 Cardinals.

Next, I used the work of Jeffrey Gross at The Hardball Times to calculate the Cardinals' individual xBA, xOBP, xSLG, xOPS, and xwOBA based on their xBABIP. In doing so, I assumed--rightly or wrongly--that all xBABIP hits added or lost were singles. You can read Gross's article for the problems in extrapolating other offensive stats from xBABIP. Keep grains of salt in supply, for the expected offensive numbers are mere approximations.

Each individual Cardinal is listed from luckiest to the unluckiest, so to speak.

MATT ADAMS

YADIER MOLINA

ALLEN CRAIG

MATT CARPENTER

MATT HOLLIDAY

DAVID FREESE

CARLOS BELTRAN

DANIEL DESCALSO

PETE KOZMA

JON JAY

TONY CRUZ

SHANE ROBINSON

It's interesting to see--in hindsight--which Cardinals benefited from good fortune on batted balls and which suffered. It's important to keep in mind that there are limits to this information's predictive value. Players may hit more line drives next season or fewer. More fly balls may sail over the wall. An injury may hamper foot speed. Nonetheless, xBABIP and the other "expected" offensive stats help give us some context as to how batted balls can impact a player's batting line.