Big League Stew - MLB

The world of advanced baseball statistics can be an intimidating place for those of us who slept our way through advanced algebra or haven't been a follower of the Bill James revolution from the beginning. 

But that still doesn't mean that we should feel left out when it comes to another way of understanding and appreciating the game we all love. With that in mind, BLS stat doctor Alex Remington will explore a new advanced statistic each week during the offseason, providing a simple primer for the uninitiated.

Today's statistic: BABIP

What it stands for: Batting Average on Balls In Play

How you calculate BABIP:


What BABIP is good for: You often hear BABIP being brought up by stats-minded folks, but what does it actually mean? Simply put, BABIP tells us how many hits a player is getting — or a pitcher is giving up — when the ball is put in play. (In this case, "put in play" means the ball stays fair and in the ballpark, rather than the play resulting in a strikeout, walk, home run, hit  by pitch or error.)

If the batter's line drive rate remains constant, BABIP can be a good indictator of "luck" or random chance, which tends to even out over the course of a long season. If a lot of hits are falling when put into play, a hitter is usually due for a dry spell or a pitcher is usuall due for a break. If very few hits are falling, a hitter may be nearing a hot streak, while a hot pitcher may be due for an upcoming reality check.

Why BABIP works: A well-known sabermetrician named Voros McCracken has demonstrated that pitchers have relatively high control over strikeouts, homers, and walks, but have relatively little control over balls in play. If a ball stays in play, the only things determining whether it will fall for a hit or turn into an out are the defense and random chance. This implies that the "pitching to contact" approach is either a myth or a byproduct of a stellar defense.

How BABIP works for pitchers: For pitchers, BABIP tends to converge around .290 or .300. A pitcher whose BABIP is significantly higher than .300 will either wash out of the league or see it come down. Meanwhile, a pitcher whose BABIP is significantly lower than .300 will see it rise. There's generally not that much variance: Greg Maddux's(notes) career BABIP against was .286 while Jose Lima's(notes) was .301.

A strikeout/flyout pitcher will generally have a higher BABIP than a groundball pitcher (Curt Schilling(notes), .297; Kevin Brown, .293), but the differences tend to be extremely minor.

Knuckleballers tend to have an abnormally low BABIP: Tim Wakefield's(notes) is .275, and Phil Niekro's was .273. Nolan Ryan's career BABIP is an absurdly low .269, but he is an extreme case because he was both so dominating and wild that he didn't put the ball in play very often. Ryan is the all-time leader in both strikeouts and walks, eighth all-time in beanballs, and 35th all-time in home runs allowed.

There is actually an ongoing dispute in the stat community as to whether a pitcher is capable of consciously affecting his BABIP. Brian Bannister(notes) of the Royals, perhaps the most stat-savvy player in the majors, gave an interview last year in which he revealed his efforts to improve his BABIP by throwing more strikes and getting into pitchers' counts.

How BABIP works for hitters: For hitters there is more variance in BABIP, though the mean is obviously the same. Extremely speedy hitters tend to have a higher BABIP, because they are capable of beating out infield hits, like Ichiro Suzuki(notes) and his career BABIP of .357. Hitters less fleet of foot tend to have lower BABIPs. Free swingers like Vladimir Guerrero(notes) and Matt Diaz(notes), who manage to sustain high batting averages despite a cheerful disregard for the strike zone, have sustained accordingly high BABIPs — .319 for Guerrero and .362 for Diaz.

Jimmy Rollins'(notes) awful 2009 was accompanied by a .245 BABIP, far off of his career .295 mark and the .303 BABIP he enjoyed during his 2007 MVP campaign.

What accounted for the decline?

For one thing, Rollins had fewer infield hits than ever before, just 10 in 2009 compared to 15 the previous year.

For another, he had the fewest walks in any full season in his career — and tied for the second-fewest HBP — which meant that most every time he strode to the plate, he was putting himself into a position to make more outs. Had Rollins drawn more walks, leaned into a few more pitches and dragged a few more infield singles, he would have had more hits in fewer at-bats and his average would have been closer to the .277 he hit in 2006 and 2008.

Rollins was unlucky in 2009, but he was also lucky in 2007, when he set personal bests in PA, AB, runs, hits, triples, homers, RBI, batting average, slugging, and OPS, en route to winning the MVP. Luck breaks both ways. BABIP isn't the reason he did poorly; it's just merely a measure of how poorly he did.

When BABIP doesn't work: As I just said, BABIP is not a determinant of success, it's only a measure of what's going on. It's only a byproduct of all the things that batters and pitchers have no control over — the little bounces and breaks and streaks that sometimes even out over the course of a season and sometimes can carry a guy to a career high or a career low.

Random chance isn't the only reason a player might fail. Persistently low hitter BABIPs or high pitcher BABIPs might be an indication of poor mechanics, injury, or insufficient skill or talent. There's wild BABIP variance in the minor leagues, where the talent spread is a lot wider.

Why we care about BABIP: Once you know how it works, it's very easy to eyeball and predict whether someone is likely to do better or worse. BABIP-based predictions certainly aren't perfect — and one should guard against attributing all the variation in a player's BABIP to "luck" — but it is quick and easy and it is very useful if you keep that warning in mind. I use it constantly when writing my Slumpbot and Streaking columns, because I know that BABIP should be around .300, and if a player's BABIP is too high or too low, then I can make a prediction for whether his streak or slump is likely to end in the near future.

Next week's lesson: OPS+


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