There are three primary factors that drive a player’s batting average. Last week, we discussed the first: contact rate. Today, I’d like to talk about Batting Average on Balls in Play (BABIP). You may recall me talking about this before for pitchers, but today I’d like to talk about it in the context of hitters.

Contact rate measures how often a player puts the ball into play. Naturally, the more balls are put into play, the more have the opportunity to fall for a hit. And that’s where BABIP comes in. BABIP measures the percentage of these balls that actually do fall for hits. It is calculated like this:

First, let’s look at Jake Peavy, one of the best pitchers in baseball.

If you’ll recall, we determined in a previous column that the BABIP of a pitcher generally regresses towards league average – around .300. Some pitchers have the ability to do a little better than this (and some worse), but not by much. Peavy seems to regress towards .300. He never actually reaches .300, but that is the nature of BABIP; it is prone to luck and fluctuations, but his figures fluctuate around .300, just as we would expect.

With hitters, though, you can’t employ such a strict regression. It is true that there is a luck involved anytime you deal with balls in play. All it takes is an outfielder to be positioned a couple feet in the wrong direction or having the fortune of seeing Manny Ramirez out in left field a few times more than everyone else for an otherwise probable out to turn into a hit. When examining hitters, though, it becomes obvious that hitters do not follow the same set of rules for BABIP that pitchers do. Let’s look at some examples.

There is some definite fluctuation, but notice that the fluctuation does not occur around .300, as it did with Peavy. In fact, Jeter’s BABIP never went below .317, and I would be confident to say that there’s a good chance this was due to a larger proportion of bad luck than we might consider reasonable. Instead of fluctuation around .300, it looks as though Jeter’s BABIP fluctuates around a number much higher. If we take a straight, unweighted average of these BABIPs, we get .358.

Now let’s look at Neifi Perez.

Much different looking than Jeter. The only thing they have in common is that neither ever really got very close to .300. Conversely, Neifi Perez’s BABIPs are consistently, significantly lower. He seems to regress to .270 or so.

It is apparent that every hitter has his own distinct hitting ability and that they each regress to their own unique BABIP. It can sometimes be difficult to pinpoint what that number is (I generally use a three-year weighted average as a starting point), but that’s the nature of the beast. Some hitters simply hit the ball harder or have a better technique.

Let’s look at one more player.

Anything look out of place? From 2002 to 2006, Posada’s BABIP revolved around .305 or .310. Then in 2007, Posada puts out a .389 BABIP. This is not a product of skill. This is a product of extreme luck, and by the laws of statistics, there will always be a few players to do this every single year. Taking a standard deviation approach, 5% of players will always be two standard deviations or more away from the mean. In 2007, Posada was one such player.

Don’t be fooled when someone does this. Don’t be tricked into thinking that they have established a new hitting baseline. It’s possible that they have, but don’t risk your fantasy season on it. Expect regression, and wait another year or two and find out what the case is for sure – most times it will simply be luck at play. In the case of Posada in 2008, expect some serious regression.

Sometimes, we don’t have the luxury of years of major league BABIPs to judge players by. We see a guy like Reggie Willits post a .363 BABIP or Jack Cust post a .366 BABIP or Chris Young post a .260 BABIP, and we don’t know what to think. In this case, look at the player’s minor league BABIPs and the respective Major League Equivalencies (a topic I discussed a few weeks ago). This should give you extra data to work with to see if a high (or low) BABIP is actually warranted or a function of luck.

Here is a list of hitters from 2007 with BABIPs that stray far from league average. I think it’ll be useful for you guys to through the past few years for these guys and see if you can tell which are due to regress and which are for real. Let me know if you have any questions.

Once you decide whether a player is going to regress, realize that this will have a significant effect on batting average. I’ll show you next week how to figure out the exact effect, but for now, just know that as a player’s BABIP regresses downward, his batting average will go down. When the BABIP regresses upward, his batting average will go up.

Also, be sure to track BABIP throughout the season. If a player is hitting .350 through April with a .400 BABIP, realize that he is not a .350 and will come down. If you happen to own this player, then might be a good time to trade him.

If you have any questions, feel free to send me an e-mail.