Big Data’s Next Big Thing: Sports Training and Personalized Medicine

Big data analytics is having a huge impact across lots of industries, with financial services and retail leading the pack. But according to one expert, there’s a curious lack of human analytics talent focused on the potential to apply big data techniques to personalized athletic training and medicine.

There’s massive untapped potential to use big data analytics to develop personalized regimens to improve athletic performance and people’s overall health, says David Epstein, the author of The Sports Gene and longtime contributor to Sports Illustrated.

“There’s a tremendous opportunity now, from the highest level of sports to just getting people healthier,” Epstein said at the Hadoop Summit last month. “I hope that people here will apply some of the magic they’ve applied in other industries.”

During a fascinating 30-minute keynote, Epstein shared a short history of big data in sports, which was all the more impressive because he didn’t mention “Moneyball” once. From obscure sports like handball, shotput, and long jump to more visible ones like baseball, basketball, and golf, Epstein showed how athletes are extracting non-intuitive patterns from big data collections and using that knowledge to make small adjustments to improve their performance in the field.

Take, for example, the work that trainers did preparing Great Britain’s athletes for the 2012 Summer Olympic Games. An analysis of the biomechanics of the long jump show there are three main variables that impact the result: the speed of the runner when he hits the board, the force he exerts on the board, and the angle he takes off the board.

“Her job was just to see which of these three variables they could do something with,” Epstein said. “She figured out really quickly….she couldn’t change sprint speed at all at that point and she couldn’t change force on the board because everybody has pretty good technique. Technique is the thing that most of the people at that level spend all their time working on. She did find she could change angle very easily. People never work on angle. It seems like something silly.”

For two years prior to the Games, the trainer worked with Great Britain’s best jumper, and focused on nothing but the jump angle. “Come the Olympics, he doesn’t have one of the 10 fastest approaches to board, or one of five highest forces on board. But he jumps at the perfect angle and wins the gold medal,” Epstein says. “Here’s a guy who probably isn’t one of the 10 best athletes in the world at his sport, but by drilling down into big data and figuring out what mattered, they figured out what they could actually do something about, and what they could change by adding a little science to that they found.”

Those sorts of stories are becoming more common as athletic trainers figure out how to use big data to their advantage. In many cases, Epstein says, this big data approach can help amateur athletes learn in a matter of months what it takes world-class athletes a lifetime of training to develop.

World-class athletes may appear to have super-human abilities. How else can you explain how soccer superstar Cristiano Ronaldo perfectly heads a ball into the net when the lights are turned off just after the pass? It’s all about how athletes learn to “chunk” data in their brains, says Epstein.

“The way that elite athletes do what they do, to make it look like they have super-human reaction speed, is they pick up body queues–the rotation of shoulders and torso, shifts of the lower leg and rotation of the ball,” Epstein said. “So they see the future of where the ball is going before it gets there. It turns out that kind of information processing is the hallmark of expertise, and we’ve learned a lot about it by gaze tracking.”

Armed with the insight from gaze tracking machines, amateur athletes are emulating their elite colleagues and shortcutting the training required to achieve super-human capabilities, Epstein says.