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%)