In theory, fast players can turn outs into hits by utilizing their speed: they beat out ground balls, they lay down bunts, they force infielders to play more shallow than they otherwise might. But can fast players actually outperform their "expected" batting average on balls in play?

To find "expected" BABIP, you add .120 to a player's line-drive percentage (LD%). Any deviation in the player's actual BABIP from his expected BABIP is often attributed to good or bad luck, and is usually subject to regression to the mean. In this study, I set out to identify whether speedy hitters are subject to these same rules of luck, or whether they can use their speed to get more hits than their expected BABIP predicts.

I looked at the expected and actual BABIP of 12 of the fastest players in baseball over the last five years: Michael Bourn, Willy Taveras, Jacoby Ellsbury, Ichiro, Jose Reyes, Joey Gathright, Hanley Ramirez, Carl Crawford, Juan Pierre, Brian Roberts, Grady Sizemore, and Chone Figgins.

Here is what I found:

Each dot represents one season (within the past five years, including 2008) of one of the 12 aforementioned players. A dot corresponding to a negative number on the left-hand side indicates that the player’s actual BABIP was higher than his expected BABIP – which is what we expected to be the case most often for speedy players. A dot corresponding to a positive number on the left-hand side means that the player’s actual BABIP is lower than his expected BABIP.

If our hypothesis – namely, that these speedsters would be able to have their actual BABIP consistently exceed their expected BABIP – was correct, we would see a lot of dots below zero. However, as you can see, there are almost exactly as many players with negative differences as there are players with positive differences.

These 12 players have had 49 partial or full seasons since 2004, and their actual BABIP has exceeded their expected BABIP in only 24 of these seasons. This supports the idea that actual BABIP randomly fluctuates above and below expected BABIP.  The differences range from -93 points (in 2004, Ichiro’s expected BABIP was .306, while his actual BABIP was .399) to +73 points (in 2005, Juan Pierre’s expected BABIP was .371, while his actual BABIP was .298). 

So what does this tell us? Well, the sample size is small, therefore we cannot draw definite conclusions from this study. However, the results are still very interesting: these players are among the league leaders in stolen bases and, anecdotally, are some of the fastest players in the entire game. If anyone can exceed their expected batting average on balls in play using their speech, it’s these players. And yet, in half of these seasons the player’s actual BABIP does not exceed his expected BABIP – in half of these seasons, the player has been "unlucky" according to expected BABIP.

Thus, perhaps Carl Crawford’s struggles this season (only a .279 batting average) can be attributed to a regression to the mean on his balls in play (his actual BABIP is 17 points below his expected this season; whereas over the last two years his actual BABIP exceeded his expected BABIP by 53 and 28 points, respectively). Perhaps Chone Figgins’s batting average this season is higher than it will be going forward (his actual BABIP has exceeded his expected BABIP by 28 points this year), and perhaps Jacoby Ellsbury’s batting average will be higher than has been so far (his actual BABIP is 48 points lower than his expected BABIP).

It may be true that some players are able to consistently exceed their expected BABIP because of their speed; however, this study suggests otherwise.