Data Implementation

So much data is being collected, in so many novel ways, that we can be distracted from the real question: what are we going to do with it?

In-game performance decisions. The data can tell you the starter runs 20 mph in practice, while the second-stringer runs 19 mph. On game day, sensors will alert the coaches that the starter has dropped to 17 mph with fatigue, and it’s time to sub in the bench player.

Pubertal status calibration in youth sports. Forever, youth sports talent identification has famously favored older athletes within a given age bracket who are more mature. But late-blooming 15-year-olds can’t really be fairly compared to 13-year-olds, either. Sensors and machine learning will finally solve this conundrum, factoring skeletal age and hormone escalation into player experience and performance metrics to make sure the players with the most potential are picked over the players who merely hit puberty early.

Career extension. We don’t really know whether aging athletes, in order to compete, need to put in double the training load or if they need to emphasize getting rest between workouts. The science of career extension is not really a science yet—mainly hunches and anecdotes. “Recovery” will become an accurately measurable event, parsed through tangible factors. Expect to be surprised; much of the “wisdom” we take for granted will be overturned.