Alot of people out there, believe that BABIP (Batting Average
On Balls In Play) is a regression based statistic, and to out perform the
regression point is more luck then skill.
What do you do if you do believe that BABIP control is a skill based on
the type of contact given up? FIP (Fielding Independent Pitching) regresses
BABIP to the league average so it isn't of any use to measure from that
standpoint. Early in 2010, Tom Tango ran
run expectancy based regressions (or in layman terms, he derived how much
impact on runs each batted ball type had) on data from 2002-09 and developed a
formula that would finally include batted ball type into FIP. Not to confuse this bbFIP with tERA, which is based completely on linear
weights
(http://www.insidethebook.com/ee/index.php/site/comments/tangos_lab_batted_ball_fip/)
Here you can find everything that went into creating the statistic.

The simple explanation is through what Tom Tango figured out
unintentional walks, hit by pitch, line drives, strikeouts and popups had
roughly a similar run expectancy whether for positive or negative. The first part of the formula is the “BIGS”,
which is (( unintentional walks + hit by pitch + line drives) – (Strikeouts +
Popups)). Then taking that total and dividing it by the number of batters
faced. Lets use the shorthand formula
of:

( ( UBB + HBP + LD ) -
( K + PU) ) / PA.

The next part of the equation is what we call “SMALLS”. Outfield fly balls and ground balls had
similar run expectancy so the second part of the equation is (outfield fly
balls – ground balls). Of course we divide this by batters faced as well. The final equation for “SMALLS” is:

( FB – GB ) / PA

Finally, its time to put it all together as an equation.
Doing some fancy math that I won't bother to get into, we multiply our BIGS/PA
by 11 and SMALLS/PA by 3, thus giving ourselves the difference in run
expectancy. Then at the very end of the
whole equation we add a simple constant (C) to get bbFIP onto an ERA
scale. In the end our final calculation
is:

bbFIP = (11* ( ( UBB + HBP + LD ) - ( K + PU ) / PA) ) +(3* ( ( FB
– GB ) / PA ) ) + C

bbFIP really allows us to weed out those outliers from FIP
that out or under perform their peripherals. Original FIP is highly dependent
on home runs, strikeouts and walks, and basically assumes that each pitcher
should regress towards the mean in terms of BABIP. The thing about BABIP is
that it really still depends on batted ball type. Ground balls and popups will
turn into runs much less often then fly balls or line drives.

Brandon Morrow is an example of a pitcher who under performs
his raw peripherals (BB, K, HR/FB). The fact that he gives up a higher % of FB
and LD (compared to league average) and a low % of GB and PU. This leads to a higher bbFIP then FIP (3.81
to 3.64 in 2011).

Conversely, Cole Hamels generates a higher then average rate
of GB and FB and a lower % of FB and LD.
Hamels posted a 2.41 bbFIP and a 3.02 FIP in 2011, outperforming his
already solid raw peripherals.

In the end, just like all statistics bbFIP is just another
tool in the grand scheme. There is, nor
will there ever be all encompassing statistic for independent player
performance, although bbFIP brings another different approach to evaluation.