The problem with sabermetrics, according to many baseball analysts, is that while they attempt to dig deeper than traditional stats, they often simply trade one flawed stat for another.
An example of this is BABIP (Batting Average for Balls In Play). What this statistic measures is what a hitters batting average is for everything that was not a home run, a strike out or a foul out. It essentially attempts to measure how lucky a hitter gets. Statistics would say that regardless of how good or bad a player is, 30% of all balls put into play should fall for a base hit.
So when a player has a .212 BABIP, as Chris Iannetta had in 2010, it is easy for fans to suggest that he simply was a victim of bad luck.
On the other hand, Jonathan Herrera, a guy firmly in the mix for the second base job, logged a .330 BABIP in 2010. According to sabermetrics, Herrera was lucky, and Iannetta was extremely unlucky.
The problem with this stat? It assumes that all balls put into play are equal. It doesn't take into account the pitches that a player chooses to swing at, or any flaws in his swing. For instance, Iannetta, who struggles with a loopy swing, found himself consistently popping up. Instead of squaring balls up, he was constantly working underneath pitches, lifting them high in the air, which were then easy for the defense to catch.
Herrera, on the other hand, is a hitter who has a plan at the plate. When he doesn't get the pitch that he wants, he simply fouls off the pitch and waits until he gets the one that he likes.
While Herrera is by no means the definition of a great Major League hitter, his .330 BABIP should not simply be excused as luck. Same goes for Iannetta, as his low BABIP was not simply line drives finding gloves, it was pop flies waiting for a defender to camp underneath the ball.