BABIP-column-audio.mp3

 

here has been an awful lot written about baseball statistics, most notably from pioneers such as Voros McCracken, Bill James, and Ron Shandler. You can read about the history and use of sabermetrics in real and fantasy baseball in our Draft Guide Chapter 1: Fantasy Baseball 101. I have also included several reference links at the end of this article in case you would like to read the in-depth information. However, this piece is not intended to give you another boring sabermetric review, so relax and enjoy the show. I do intend, however, to demonstrate that the use of BABIP (defined below) is a bunch of BS (I’m sure that you are aware of that acronym). What got me in such a tizzy about this?

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First, the term BABIP

 

One of the most commonly used statistics in baseball research, especially in fantasy baseball, is Batting Average on Balls in Play, or BABIP. According to research by Voros McCracken, BABIP is commonly used as a red flag in sabermetric analysis, as a consistently high or low BABIP is hard to maintain – much more so for pitchers than hitters. Therefore, BABIP can be used to spot fluky seasons by pitchers (or hitters), as those whose BABIPs are extremely high can often be expected to improve in the following season, and those pitchers whose BABIPs are extremely low can often be expected to regress in the following season. In general, a pitcher’s or hitter’s ability to control what happens to a ball hit in play (e.g., a hit or an out) is mostly random and a result of luck.

 

I started being a little nerved when…

 

Last year I read a column entitled “Deconstructing Upton” published on ESPN from renowned sabermetrician Ron Shandler of baseballHQ. The article link is posted at the end. In that article, Shandler argued that Justin Upton‘s BABIP was abnormally high, meaning “a crash is coming” and he recommended trading him away. No big deal to me at the time. Although when he wrote the article, it stuck in my head, and I was watching Upton all year as a result. At the time of his article, Upton was hitting .339 with 12 HR’s and 13 SB’s (through July 25, 2007, more than half of the season). I also recall Upton finishing the season at .300, 24 HR and 22 SB’s, doubling his HR total in less at-bats for the remainder of the season. Finishing at .300 surely meant that he hit below .300 for the remainder of the year, but nothing near a “crash,” especially in the HR department. In fact, here are Upton‘s post-all-star rankings among all second baseman:

 

B.J. Upton – 2007 Post-All-Star Break Stat Rankings
HR	RBI	Runs	OBP	BA	SB	OPS
1st	2nd	3rd	6th	.285	7th	7th

 

Crash? So, okay, that really didn’t bother me, much. I also found it a little ironic, however, that Shandler paid $18 for Mike Lowell in this year’s Tout Wars-AL draft (7^th most $$ for a 3B in draft) when in 2007 Lowell finished with a career-high BABIP of .342. Oops.

 

But then…

 

I read an article contributed by Ron Shandler in a recent USA Today Sports Weekly issue, and to quote:

 

“A high BABIP (bad luck) means a pitcher’s performance is expected to improve. A low BABIP (good luck) means a pitcher’s performance is expected to drop off.”

 

Shandler also went on to say “All pitchers, whether they are Cy Young candidates or hurlers who have never seen the underside of a 5.00 ERA, will post a BABIP that approaches .300.”

 

Now I started to boil. I would first like to say that I have a fair amount of respect for Ron Shandler, Bill James, and other baseball sabermetricians. Surely, they have a lot more experience than I have, and are probably a bit more educated, but I’m no dummy either. With that said, the above quotes, in my humble opinion, are a bunch of BS, as is the usefulness and the theory of BABIP to predict pitcher or hitter performance, and quite frankly, I’m getting tired of reading about it.

 

Furthermore, in a critique of McCracken’s research on BABIP, another researcher concluded that the differences between pitchers in preventing hits on balls in play were at least partially the result of the pitcher’s skill. The link for that research is also provided. If you are reading this, Mr. Shandler, I have no doubts that you and your colleagues will develop sophisitcated counter-arguments to what I present here. After all, that is what is fun and interesting about statistics – they can be presented and interpeted in many ways, often times with conflicting results. Personally, I prefer not to evaluate players mostly from a computer, and I certainly do not subscribe to the Law of Averages, especially when it comes to the unpredictable nature of the game of baseball with a plethera of variables. I still hold you, and your colleagues, in high regard.

 

Law of Averages

 

Much of the theories expressed about BABIP and other sabermetric statistics are based in great part on the Law of Averages, which is a belief that outcomes of a random event will “even out” over a large sample. The “law” typically assumes that unnatural short-term “balance” will occur.  However, I do remember one interested thing I learned in my college genetics class about 25 years ago: If you flip a coin and it comes up “heads” ten times in a row, ya’ll would think that odds are that “tails” has to come up soon, right? Think again. Every single time a coin is flipped, even after 10 or 20 straight times of landing “heads”, there is still only a 50/50 chance that either “heads” or “tails” will result on each flip. The law isn’t really a scientific law, but rather a perception that is not based on any scientific calculations.

 

If you buy into the fact that hitters (or pitchers) will eventual regress (or increase) towards the mean of a .300 BABIP, which has been stated as the average benchmark to which all players will gravitate towards (with the law of averages), then I guess the carrer BABIP’s of Wade Boggs (.344), Rod Carew (.359) and Ichiro Suzuki (.357) are just abhorations. Clearly, all hitters are not average hitters who just get lucky (or unlucky) from time-to-time. Tell that to Ted Williams, whose career BA of .344 is higher than his career BABIP of .328. I guess Ted’s relatively “poor” BABIP was dragged down by his 521 career home runs and 1.116 OPS. D’oh! (note: the HR stat is used in the formula for deriving BABIP, and the higher the percentage of home runs in total hits reduces the BABIP result).

 

Good hitters know how to find the holes, and get good wood on the ball. Squaring up a pitch for a hit (and HR) is a skill, unless you believe that Barry Bonds couldn’t hit his way out of a paper bag with his “below average” career BABIP of .285. Maybe Barry needs to play one more year to get his BABIP back up to that .300 benchmark – but he’ll need to get more base hits and fewer home runs. – Hey, I didn’t create the formula.

 

Okay, I get it…

 

Yes, I understand that when you look at a player’s BABIP, whether a pitcher or a hitter, you are comparing their current BABIP to their own personal career BABIP, not the league average (although I don’t know why they mention a .300 benchmark); and that if a player’s current BABIP is significantly higher or lower than their own personal career BABIP, that something must be out of whack. I get it, but I don’t necessarily buy it, either.

 

If one believes in personal BABIP’s, then of course that would explain the career-year anomalies. I guess it would also mean that advocates of BABIP don’t put much faith in the 27-year-old breakout theory either. Clearly, it is difficult to translate stats, including BABIP, from the minors to the majors, and I guess BABIP would ignore the natural progression, improvement, or growth of players, and that some of them cannot all-of-a-sudden “get it.” Conversely, it would suggest that players like Andruw Jones shouldn’t all-of-a-sudden suck and continue to suck either. BABIP would suggest that career years are mostly luck, and have less to do with a player’s improvement, coaching, prime, juiced balls, opponents, personal issues, and a dizzying amount of other factors. So, let’s look at one more example – a pitcher, perhaps.

 

The Brian Bannister Brainteaser

 

Re-quoting from Shandler again, “All pitchers, whether they are Cy Young candidates or hurlers who have never seen the underside of a 5.00 ERA, will post a BABIP that approaches .300. Pitchers whose BABIP is significantly different from .300 can be expected to regress. A high BABIP (bad luck) means a pitcher’s performance is expected to improve. A low BABIP (good luck) means a pitcher’s performance is expected to drop off.”

 

In the same article from where the above quote was taken, Shandler discussed Brian Bannister and referred to his 2008 “low .240 BABIP” and mediocre K/9 rate, further stating “he’s a soft-tosser, and a lucky one at that.” Well, his current .240 BABIP corresponds with his 2.42 ERA through his first four starts. Interestingly, last April of 2007, Bannister posted a similar .243 BABIP, but had an ERA of 4.91. Also, in September/October, he had a “low” BABIP of .253 but had an ERA of 7.30. In fact, the month (August) with his second worse BABIP (.290), he had his second best ERA (2.90). What gives? He had a 2007 season BABIP of .264. His three-year career BABIP average in the majors is .261. After looking at all pitchers three-year averages from 2005-7, there were 17 pitchers with lower BABIP’s than Bannister. Furthermore, there were 50 pitchers with BABIP’s of .275 or lower. While Bannister’s K/9 rate was the lowest of all pitchers in his BABIP range, Bannister also had one of the lowest B/9 rates as well.

 

If “one of the core elements is a pitcher dominance over hitters, which we measure using his rate of strikeouts per nine innings (K/9),” is true, then someone please tell me why Chien-Ming Wang managed to post a three-year average ERA of 3.84 and ranked 9th best overall in quality start percentage when he had the 9th worse (lowest) three-year average K/9 rate of 3.84?

 

Ground Ball Stats and Other Useless Information

 

Since I have spent the majority of this column doing nothing but criticism, you might ask what I have to offer in a positive sense. Well, first of all, the notion of not buying into BABIP when making roster or trade decisions is one way to look at it. Many prognosticators, including Professor Sabergeek, fancy the groundball pitchers, although perhaps not as much as the strikeout pitcher. The theory is that groundball pitchers give up less home runs and are also adept at getting more double-plays, thus helping their ERA. However, some basic research of the previous three year averages among all pitchers do not necessarily show that groundball pitchers give up less hits either.

 

To finish, let’s compare some stats between ground ball and fly ball pitchers and their corresponding batting average against, ERA, and BABIP, and see if we can draw some conclusions, or at least make some interesting observations.

 

Statistical Comparison among GO/AO, ERA, BAA, and BABIP
3-yr Avg 2005-7	INN	BBd9	Kd9	ERA	BAA	GdAO	BABIP	QS%
Groundball Pitchers	20915.9	3.693	6.112	4.29	0.269	1.558	0.306	51%
Fly ball Pitchers	19704.7	4.347	6.961	4.53	0.262	0.673	0.299	44%
TOTALS-OVERALL	66411.4	4.065	6.574	4.38	0.267	1.052	0.304	49%

 

Stats shown in blue text are the best, while stat numbers in red text are the worst.

 

As indicated by the above statistics of 3-year averages among all pitchers, groundball pitchers have the better ERA and QS%, but worse K/9 rate, BAA and BABIP. Conversely, the exact opposite is true of fly ball pitchers. The other interesting comparison is between ERA and BABIP alone, where pitchers with the lower BABIP have had higher ERA’s. Therefore, if a current pitcher has an abnormal low BABIP, if his BABIP actually does concede to the law of averages over time during a season (which I don’t believe in all the time), it doesn’t necessarily mean that a rising BABIP corresponds with rising ERA or QS%, in fact, the reverse may be true!

 

Concluding Remarks

 

It is clear that pitchers with K/9 rates above the league average have much better ERA’s, BAA, and QS%. However, their BABIP’s are relatively the same, which further confirms that BABIP is BS.

 

Far too much over-analysis is spent on a pitcher’s and hitter’s BABIP, including a hitter’s contact rate, especially early in the season. Variables that are likely to have more of an affect on a pitcher’s (or hitter’s) effectiveness and production include, but are not limited to: monthly splits, ballpark effects, opposition (schedule), team-mates, and defense. A pitcher’s “stuff” and skills, and a hitter’s natural skills, have more affect, while “luck” has the least effect. If a pitcher with seemingly less “stuff” appears to be on a hot streak, and perhaps may have figured things out or mastered his craft (like Bannister), it doesn’t necessarily mean that he cannot sustain his success; and analyzing his BABIP, or any other pitcher’s BABIP for that matter, to draw any resemblance of an accurate prediction about his near future production, is not worth getting brain damage over thinking about it.

 

“…the knowledge of who will improve is vastly more important than the knowledge of who is good. Stats can tell you who is good, but they’re almost 100 percent useless when it comes to who will improve.” Bill James.

 

I guess logic dictates that the reverse is true as well, meaning statistics are practically useless to determine if a player will regress.

 

Reference Links

 

http://en.wikipedia.org/wiki/Defense_Independent_Pitching_Statistics

http://www.diamond-mind.com/articles/ipavg2.htm

http://groups.google.com/group/rec.sport.baseball.analysis/msg/b450fe58c05a5a82

http://insider.espn.go.com/fantasy/baseball/flb/story?page=shandler724