Elliott’s team has long used three-dimensional motion capture technology to assess factors they suspected might lead to NBA injuries. (For example, in some players, the long bones of the leg rotate as the knee bends. That’s an important predictor of knee injuries.) More recently, though, the P3 team has subjected their growing datasets to the scrutiny of machine learning algorithms, which can see correlations that humans might not expect, especially between multiple factors. It has been revelatory, mostly in ways Elliott has yet to discuss. One little finding: being overweight is not a big predictor of knee trouble if you move well. But in combination with other movement red flags, like that bone rotation thing, extra weight makes injuries more likely. One year into what Elliott expects will eventually be a published study with two years of data, P3 now has an incredible ability to predict NBA knee injuries. “If we said you were at risk of knee injury,” he said by phone, “there was a 70 percent likelihood you had an acute injury within the next year. We are adding another year of data, then we will publish.”