BABIP and Such

Today we'll begin our look at the lesser known statistics used to judge players, first up a combination of stats I like to use; balls in play tendencies, batting average on balls in play, and expected batting average on balls in play.

Balls in play tendencies are exactly what they sound like - the percentage of each outcome of batted balls. For example, let's say Jonny Gomes puts 10 balls in play during the first week of the season; five are flyouts, three groundouts, and two line drives. His tendencies would be as follows: 50% FB, 30% GB, 20% LD.

From those three numbers we can dictate whether a player is unlucky by taking his line drive percentage, turning it into a decimal, and adding .120 to it. That's a very rough and basic way to get an expected BABIP - batting average on balls in play. Line drives are the most likely types of batted balls to generate hits; groundballs can work for faster players or those who absolutely crush the ball on the ground, and flyballs can turn into bloopers or homeruns, but generally speaking line drives account for the most types of hits. What does the xBABIP tell us? If the number is far below or above the real BABIP one could point to it as the reason for a slump or hot streak.

HR/FB% is also a common form in judging either positional players or pitchers, and GB% is commonly looked at for relievers - asserting whether they can fit in with a certain team or not. For example I would assume J.P. Ricciardi looked at Shawn Camp's high GB% and saw a player who, when combined with the spectacular middle infield of John McDonald and Aaron Hill, could become a very useful situational reliever. Of course the Jays then signed David Eckstein, shooting that theory to hell.