The translation of a myriad of data from multiple types of devices or sources on a person’s training, nutrition, biomechanics and sleep can spur actionable approaches to prevent injury, achieve fitness goals and reduce injury risk. At the University of Michigan, researchers are using data science and analytics to analyze individual athletes in relation to everyone else on a team, while also analyzing team data as a whole. Researchers also can tap data science for purposes of individualized augmented reality and virtual reality, which can improve the experiences of athletes by enabling accurate self-assessments of performance, and improving the experiences of spectators by allowing them to be part of the action.
Jenna Wiens, U-M assistant professor of computer science and engineering, works with professional basketball teams, using analytics to help athletes and coaches gain a competitive advantage on the court.
As part of her work, Wiens identified more than 340,000 screens from five NBA seasons, and then tabulated how each screen was defended, as well as the outcome. She also analyzed 6,500 missed jump shots from one NBA season to determine whether players should position themselves for an offensive rebound or simply get back on defense.
“Every team nowadays is trying to find a way to capitalize on data like this,” said Wiens, who someday hopes to tap big data to help predict and prevent sports injuries. “One of the holy grails in sports analytics would be predicting injuries because teams spend so much money on players who can’t play. I think that could have an immense impact, not only at the professional level, but also at the collegiate level.”