Mike Friere - So, What is BABIP?

In an effort to make "all that is old, new again", I have resurrected an old series of articles that I put together in a previous "Mack's Mets" lifetime that focused on the new wave of statistical analysis that has shaped baseball scouting and player rankings.  Some would refer to them as "Sabermetrics" and others would argue that it isn't necessarily new anymore. 

Both of those statements are true, as we are all getting older by the minute, right?

However, to keep things somewhat fresh, I will go over a new statistic each week and then I will attempt to relate that measure to our favorite team and one or more of our current players to see how we rate, so to speak. In the second installment of this series, we took a look at OPS and how it is arguably the most comprehensive statistic used to measure a batter’s effectiveness.  For this article, we will address a statistic known as BABIP that is versatile in that it can be used to analyze BOTH pitchers and hitters. 

So, what the heck does the acronym BABIP stand for?   While it would be fun to come up with sayings that match the listed letters, it would no doubt devolve into something that

Mack would not want “in print”, so to speak.   BABIP stands for “batting average on balls in play” and as stated earlier, it can be used for batters (the number of times a batted ball in play is scored a hit) and for pitchers (the number of batted balls allowed that are scored a hit) which is pretty useful, if you ask me.

For those of you who are statistically inclined, are not afraid of math and actually enjoy figuring things out by hand (that pretty much eliminates all of us), here is the formula

that is used to figure out BABIP;

                                              Hits - HR

                    BABIP =     --------------------------

                                        AB - K - HR + SF

    

It may seem odd that home runs (HR) and strikeouts (K) are basically excluded from consideration while using this formula.  However, it is done so because a home run and/or

a strikeout are not “balls in play” where secondary factors (like defense, for example) can affect the outcome.  For example, let’s say Yoenis Cespedes plays in both ends of a double header against the Nationals and is responsible for 4 hits in 10 at bats with 1 home run, 2 doubles and 1 single.  Additionally, he also produced 6 “outs” on the day and let’s say 3 of them come via strikeout.   Using our basic formula, you would eliminate the strikeouts and the home run, leaving a BABIP of .500 (3 hits from 6 batted balls in play), which is impressive any way that you analyze his “mock” performance.

Understanding a particular statistic is one thing, but providing a frame of reference is also necessary.  As such, this formula begs the question(s) what is a good BABIP and is it the same for pitchers and hitters?  GENERALLY, an “average” BABIP for any player usually hovers around .300, with minor fluctuations from year to year due to secondary factors like the quality of a team’s defense (better defensive range usually means fewer hits for the opposition).   With that said, the fluctuations usually don’t move more then +/- .015 unless other factors are at play (more in a minute).  

This statistic is a bit sneaky in that the number itself is not as important as how it compares to the “expected baseline”.  The difference or variance from what is seen as average is more important then the actual number.  Some players, due to their talent, will sport higher (hitters) or lower (pitchers) BABIP’s and that can be used to separate better players from their average teammates, especially if you have a sustained period of performance to draw upon.  However, this statistic is also an excellent tool to identify a “fluky” season, or a “career year”  for a player that is unlikely to be repeated.  This can be done both positively and negatively and that is how I like to use the measurement.

Let’s use Jose Reyes as an example;

For his career to date, Jose has a lifetime batting average of .286 (respectable) and a lifetime BABIP of .308, which is slightly over our agreed upon benchmark of .300 but is likely explained by his above average speed which logically leads to more infield hits versus ground outs.  In 2011, Jose had an excellent season and actually won the NL “batting title” for the year with a batting average of .337 which is very good.  The listed batting average was 52 points higher then his career average and it was primarily boosted by a higher then normal BABIP of .353, which was 45 points over his career average (conveniently in a “contract year” no less).

So, did Jose suddenly become a Wade Boggs clone or was this season simply a fluke?  If you look at the year before (2010), Jose produced a batting average of .282 with a .301 BABIP which is more in line with his career averages.  The year after his magical season (2012), Jose followed with a batting average of .287 and a BABIP of .298, so it is safe to say that the 2011 season was an outlier and it happened to come at a perfect time for his bank account.

MOST players who have exceedingly high OR low BABIP figures in any given season will likely “regress to the mean” the following year.  So, if you identify a player who posted historically poor numbers and they also had a lower then average BABIP, you should expect a “bounce back” season, health notwithstanding.  Or, you can identify a player like Jose in 2011 with a higher then average BABIP and not be surprised when they “come back down to earth” in future seasons.

Taking a quick look at the Mets’ 2017 season, I could only find ONE offensive player who had a BABIP over our benchmark of .300 (Asdrubal Cabrera) and his .310 mark which was only slightly above average.  Having said that, I am sure that the excessive injuries last year played a role.  But the Mets’ team BABIP was .286, so you can logically assume that the team is due for a “bounce back” season on offense when you factor in a simple regression to the mean.   A similar theme presented itself with our pitching staff, as only one pitcher actually pitched enough to qualify for the statistic (Jacob deGrom posted a BABIP of .305), which says something in itself.

One additional player to consider is Todd Frazier and his UGLY batting average (.213) from last season.   That figure was "supported" by a BABIP of .226, which is way below his career average BABIP (.271) and even his last full season in the National League (Reds) in 2015, when he had a batting average of .255 and a BABIP of .271 (exactly his career average).  I think he is primed for a better season in 2018 and perhaps he will be a "Comeback Player of the Year" candidate.

Much like any statistic, BABIP can assist with player evaluations, but should not be used as the only factor in an evaluation.  However, BABIP can be a valuable predictive tool for future seasons, especially when you have a larger body of work to analyze.