Jeremy Guthrie has a career BABIP of .268. The league average is usually in the .295-.300 range (and over his tenure in Baltimore, the O’s have had a .300 BABIP as a team), and (major league) pitchers are generally thought to not have much control over the outcomes when a ball is put into play. Guthrie has pitched enough innings with such a low mark though, that we need to start thinking that there’s something more than luck involved. With about 2,600 career balls in play against, we’d need to regress Guthrie’s BABIP about 60% to the mean. That means our current estimate of his true talent BABIP is around .285 (or about what he posted in 2009). Some starters who had career BABIPs of .285? Steve Trachsel, Rick Sutcliffe, and Rubes Marquard and Walberg.
So how has he managed to have balls find gloves so often? It’s a tricky question that’ll be hard to answer with any certainty until HitFX comes out, but here are some ideas:
First off is batted ball type. Line drives go for hits way more often than flyballs or groundballs, and pop-ups in particular are virtually always caught. For his career, Guthrie has given up about 18% line-drives, 41% groundballs, and 41% flyballs (with rounding). Averages are about 19%, 43%, and 38%. So Guthrie has given up fewer line-drives than average, but only a little. And more flyballs and pop-ups, but only a little. So that doesn’t explain the difference all by itself. If you use expected BABIP by batted ball type, Guthrie would come in in the .280-.290 range – nothing too out of the ordinary.
Here are Guthrie’s BABIPs on each batted ball type, compared to approximate averages:
So on flyballs it’s very close, and difference can probably be explained by Guthrie’s higher pop-up rate. On line-drives it’s 24 points lower, and on groundballs it’s 30 points lower. That at least narrows things down a little.
Looking at it by pitch type:
Line-drives (using PitchFX data from 2008-2010, with Guthrie’s BABIP on line-drives actually being a relatively pedestrian .729):
So the breaking-balls led to more line-drive going for hits. None of the sample sizes were too big, with there being a little over 200 fastball lined and only 15-50 of each of the other pitches.
Groundballs (BABIP of .217 for 2008-2010):
Once again the fastball and change-up produced the lower BABIP.
By pitch-type for all balls in play:
Fastball and change-up. So what is about those pitches that results in the lower BABIP?
I tried breaking down the fastballs by movement (there are much fewer change-ups, but since the pitch moves more like a fastball than like a breaking-ball and the former is where the lower BABIPs are, that makes me think the movement might be the thing, at least partially):
|X < -5.5||X > -5.5||Z > 9||Z < 9|
Guthrie’s average fastball movement over the three years had about 5.5 inches of tail in towards a right-handed batter and 9 inches of “rise”. The fastballs that tailed more had the lower BABIP than the ones that tailed less.
Just horizontal movement:
|X > -4||-4 > X > -6||-6 > X > -8||-8 > X|
So when the fastball’s straight, it gets hit. When it tails at all though, it produces that lowered BABIP.
Just the vertical:
|Z > 11||11 > X > 9||9 > X > 7||7 > X|
More rise or more sink were good; in the middle not so much.
I looked at different break-downs by velocity, but the pattern changed quite a bit with the different groupings (that is, it looked like his BABIP wasn’t that related to velocity – except lower velos had lower numbers, probably because those pitches are “two-seamers” that have the sink and tail that helps, as you can see above).
So there does appear to be something in Guthrie’s fastball movement, perhaps, that might cause hitters to not put good wood on the ball. Maybe. When the pitch sinks more it should generate more groundballs (lower BABIP in general, with the tail making them easier to field), while when it rides more it should generate more flyballs (also lower BABIP – and though his BABIP on “flyballs” is more or less normal, it’s possible that the movement creates more fliners (line-drives closer to being flyballs, and thus potentially easier to field) which drags the line-drive BABIP down). The sample sizes in any individual grouping aren’t going to be large, and so I wouldn’t read a ton into it. But that’s the best I’ve come up with, and it’s still probably mostly luck (as far as his low career rate is concerned).
If he keeps it up for 3-4 more years, I’ll probably be convinced there’s really something there. And I’m more than open to hearing other ideas for further study in this area.
Stats: BABIP, GB%, LD%, FB%, PitchFX