SABR Analytics Conference: Day Two

Hitters Quickly Show Distinct Groundball, Flyball Profiles

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See also: SABR Analytics Conference: Day One

The Society for American Baseball Research hosts its second annual Analytics Conference in Phoenix this week. SABR president Vince Gennaro said in his opening remarks that the conference exists to assemble the thought leaders in the field of analytics to discuss topics important to the game. The three-day event includes research presentations, featured speakers such as Bill James, Joe Posnanski and Brian Kenny, as well as moderated
panels with players, general managers and agents.

PHOENIX—Baseball fans readily associate certain attributes with major league pitchers. Justin Verlander equals pure power, while R.A. Dickey conjures images of fluttering knuckleballs.

Continue playing the word-association game with the analytically-minded fan. Trevor Cahill? He's an extreme groundball pitcher who benefits from a tight infield defense. Jered Weaver? He allows a high percentage of flyballs, but his home park, Angel Stadium, limits damage by suppressing home runs.

In his presentation entitled "The Anatomy of a Batted Ball," research analyst Ben Jedlovec of Baseball Info Solutions makes the case that we ought to view batters in the same light that we view pitchers such as Cahill and Weaver. That is to say: Major league hitters have distinct groundball and flyball profiles that hold steady after as few as 35 balls in play.

Using the BIS batted-ball timer, which tracks groundball velocity (dating back to 2009) and flyball hang time (back to 2010), Jedlovec discovered that: 1) major league hitters establish a typical groundball velocity after 95 balls in play, 2) they establish an average flyball distance after 55 BIP, and 3) hang time on flyballs stabilizes after a mere 35 BIP.

In all cases, elapsed time on the batted-ball timer is measured from the crack of the bat to the instant the batted ball either strikes a fielder's glove, hits the ground (flyball) or exits the infield (groundball).

From the BIP stabilization results cited above, Jedlovec contends that batted-ball profiles can be determined for all hitters to help expand and improve player-forecasting models. For example, if a hitter strays from established norms—in terms of groundball velocity or flyball distance—for a spell, we can confidently predict that he'll eventually snap back into place.

In running the numbers for 2010-12, Jedlovec found that the breakeven point for groundball velocity was about 75 mph. That is to say, balls hit at least that hard tended to be hits a majority of the time. As you might expect, Miguel Cabrera posted the highest average groundball velocity last season, checking in at 56.9 mph while leading the American League with a .330 average.

A clear, predictable relationship exists between a batter's speed, his average groundball velocity and ball-in-play outcome. Fast runners who hit the ball hard have high batting averages on groundballs, while slow runners with low groundball velocities hit for low batting averages.

In fact, fast runners actually gain the largest advantage—compared with their lead-footed counterparts—on slow-hit groundballs, which explains why organizations stress that young speedsters focus on hitting the ball on the ground and not in the air. If a fast runner hits the ball soundly on the ground, it will often scoot past the drawn-in infield, but even if he hits it softly, the defense still will struggle to throw him out. Jedlovec cited as examples Ichiro Suzuki and Austin Jackson, two batters who have high averages on groundballs, based at least partially on their foot speed.

Extreme power hitters, such as Giancarlo Stanton, tend to have consistently long hang times and great distance on batted balls—but not necessarily high line-drive rates. This makes intuitive sense, of course, but the picture clarifies when hang time and distance are plotted on a graph. In those cases, power hitters such as Stanton, Joey Votto or David Ortiz have many data points clustered in the upper right portion of the graph, where hang time and distance near the extremes.