I won't post the article in it's entirety (though it is available for free above). However, I posted in another thread that BABIP has taken on a life of it's own. I think this point deserves it's own thread as we try to analyze whether certain Reds pitchers have just been unlucky (Mike Stanton?) or truly are doing something which makes them get hit harder (Gary Majewski?)
Many of us, stat-heads and non, have started to run with the idea that BABIP is truly random and that any variation is due to luck. We look at a .350 BABIP and say that the pitcher will necessarily improve and say that the guy with a .260 is bound to regress. This simply isn't true. Yes, there are unsustainable extremities. However, that doesn't mean all variation is random.
It's VERY important to understand that unexplained variance can be random, but is not random by definition. Random is a property of the event which is completely free from control. The outcome of a coin flip is random because we cannot exert any control over it. BABIP has an observed mean and variance, but that variance is due only in part to luck. We simply haven't had access to the data which explains the variation. It may be difficult to prove control, particularly with aggregate data which suggests outliers even in a truly random distribution. However, that doesn't mean real, controllable variation doesn't exist.
Below is the basic premise of the article.
If major league pitchers are skillful at controlling whether batters make hard contact, and BABIP becomes less random in a data-rich world that includes batted ball velocities, then it follows that pitchers have more control over batted ball outcomes than previously thought. BABIP is a lucky number only to the extent that hits occur more or less frequently than predicted by the 0.59 and 0.19 hit conversion rates for well-hit and other batted balls. The pitcher controls his rate of well-hit in-park balls in the same manner as he controls strikeouts, walks, and home runs.