The owners of America’s baseball teams, gathered at a Houston hotel last year, were discussing once again how their games had become so plodding. This time, however, the explanation was different.
Two Major League Baseball officials and a statistician told the group that the sport was being brought to a standstill by the very phenomenon that has revolutionized it in recent years—the embrace of data analytics to drive strategy.
Analytics, in promoting strikeouts as an optimal outcome, have extended the battle between pitcher and hitter. Teams increasingly value pitchers who can generate swings and misses, because other kinds of outs require varying degrees of good defense and good fortune. Strikeout levels have reached record highs for 10 years in a row.
Pitchers “are not allowing you to put the ball in play as much as they used to,” says Yankees third baseman Chase Headley. “That’s a huge change.”
Hitters aren’t as interested in routine ground-ball hits, either, a trend driven in part by two analytic insights. The first was more data on hitters’ tendencies, which prompted teams to position their fielders in extreme ways. That so-called defensive shifting has made a ground ball less promising as a means of reaching base.
The second was a revelation born of a statistic that only recently came into existence—the launch angle. Radar and camera measurements of the angle at which balls leave the bat have shown that the optimal swing angle looks more like an uppercut than many hitters preferred. Hitters, in turn, have started swinging for the fences in droves. Home runs this season reached a record level.
That all-or-nothing approach means that between each home run there is a lot of standing around and waiting. Some classic displays of athleticism—a daring attempt by a runner to advance more than one base on a teammate’s hit, for instance—have become rarer.