bbFIP: A Primer

A lot of people believe that BABIP (Batting Average On Balls In Play), as it refers to pitchers, is a regression based statistic, and to outperform the regression point is more the result of luck rather than 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.  The resulting bbFIP should not, however, be confused with tERA (true ERA). Although the two are similar metrics, tERA does not use regression, but rather linear weights to predict future performance.

The simple explanation is that Tom Tango figured out unintentional walks, hit by pitches, line drives, strikeouts, and pop-ups had roughly similar run expectancies on either the positive or negative side.  This first part of the formula incorporates these outcomes and calls them the “BIGS”, which is [(unintentional walks + hit by pitch + line drives) – (Strikeouts + Pop-ups)]. Tango then takes that total and divides it by the number of batters faced.  Let’s use the shorthand formula of:

[(UBB + HBP + LD) -  (K + PU)] / PA.

The next part of the equation is what Tango called the “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

Now we 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 us the difference in run expectancy. At the very end of the equation we add a simple constant (C) to get bbFIP onto a scale similar to ERA.  In the end our final calculation is:

bbFIP = {[(UBB + HBP + LD) - (K + PU) / PA]  + [(FB – GB) / PA]} + C

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

Blue Jays righthander Brandon Morrow is one example of a pitcher who under-performs his raw peripherals (walks, strikeouts and HR/FB ratio). He gives up a higher percentage of flyballs and line drives (compared to the league average) and a lower percentage of groundballs and pop-ups.  This leads to a slightly higher bbFIP than FIP (3.81 to 3.64 in 2011).

Conversely, Cole Hamels generates a higher than average rate of groundballs and flyballs and a lower percentage of flyballs and line drives.  As a result, Hamels posted a 2.41 bbFIP as compared to a 3.02 FIP in 2011, outperforming his already solid raw peripherals.

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