My
post a couple days ago talked briefly about the major differences in the
direction and distance of the ball’s flight that can come from a small change
in where on the bat the ball is hit.
No statistic captures this concept more than the percentage of fly balls
that become home runs (HR/FB% -- yes
it’s an awkward name). I’m going
to help describe this using simple geometry, because I don’t feel like dealing
with the physics of it, and I’m sure you don’t feel like reading about it.
Take
a fly ball that reaches a peak of 150 feet and travels 300 feet (this is
obviously just a rough number used to make the calculations easier, but go with
it). To save you the time
reading (and the time criticizing my calculations), a change in 1/10th
of an inch in the position of the ball on the bat (and thus a reduction in the
angle of flight by just 2 degrees) causes the ball to fly 10 feet further. In short, there can be pretty strong
deviations in the way the ball flies resulting from small, possibly uncontrollable
changes in the way that the ball hits the bat.
Therefore,
to some extent there is a good bit of variability in the distance a ball will
travel (or the height it will fly) that is not so strictly under the control of
the hitter. HR/FB% captures a bit
of that randomness. The average
percentage of fly balls that become home runs is just under 10%, with a likely
range of 0-30. Batters with
different styles tend to have different career average HR/FB%, just like with
BABIP. For example, Ryan Howard’s
career average is 28.7%, while Juan Pierre’s is 1.2%.
However,
variance over the course of a career can produce pretty large shifts in power
numbers, which contribute to a player’s RBI, HR, and R totals, as well as his
batting average (fly balls are incredibly likely to be outs if they are not
home runs). Since Howard’s first
full season in 2006, his best-to-worst HR/FB% per season has ranked like so:
2006, 2008, 2007, 2009, 2011, 2010.
His home-run totals in those seasons rank like so: 2006, 2008, 2007,
2009, 2011, 2010. Notice a
correlation? In 2009, Joe Mauer
hit 28 home runs after hitting 29 in the previous three seasons combined. One needs to look no further than his
HR/FB% of 20.4%, which was a full 10% higher than any other season he has
posted before or since.
In
a similar way to BABIP, HR/FB% is a better estimator of randomness with
pitchers than with hitters because the variability in the statistic due to a
batter’s skill set should even out over the course of a pitcher’s season. Roy Halladay shouldn’t face a
significant amount more sluggers or slap hitters than other pitchers in the
league (or than himself in previous years) over the course of a full season, so
large changes from year to year can be at least partially attributed to
luck. Or, you know, throwing beach
balls up there.
Take
Ubaldo Jimenez’s breakout 2010, for example. Without significant changes in his other underlying
statistics (strikeout and walk rates, BABIP, etc.), his HR/FB% was extremely
low at 5.1%, and he posted an ERA a full run below his career average. Not only that, but his 9.3% and 10.5%
HR/FB% in the next two years represent more average outcomes, and he has posted
an ERA above 4.80 since the start of last season.
With home
run rates in mind, here are some players I expect to start to decline:
HITTERS: Matt Kemp (41.4%), Josh
Hamilton (40%), Bryan LaHair (33.3%)
PITCHERS: Brandon Beachy (1.7%), Gio
Gonzalez (2.6%), Ted Lilly (3.9%)
Here are
some players I expect to improve when it comes to home run rates:
HITTERS: Jimmy Rollins (2.0%), Alex
Rios (2.2%), Jeff Francoeur (2.6%)
PITCHERS: Ervin Santana (23.1%), Adam
Wainwright (21.9%), Jonathan Niese (21.1%)
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