Coaches have always taken into account the condition of players when scheduling training sessions. Now with the help of artificial intelligence, they can calculate more precisely the probability that individual athletes will get injured during the next match, the next week or the next month. "We follow a team for an entire season, recording GPS data during training and matches, "Rossi explains. He then uses machine learning to try to detect patterns. "This gives us the probability that a player will get injured in the next days or next weeks."
These data reveal an athlete's workload-how often they train and how intensely. Just enough training can pave the way to medals, but too much puts pressure on the body and can lead to injuries.
Sport is gradually entering a new era, in which artificial intelligence might act as an assistant coach. Algorithms (算法) could enable a teenager to train smarter and avoid a career-ending injury, or help a professional athlete to compete for a few years longer. But the technology's success depends, in part, on the ability of scientists to convince coaches to include data in their decision process.
The teams that McHugh has worked with have seen a reduction in injuries of between 5% and 40%. Yet not every coach is happy to join forces with AI. "Coaches sometimes don't feel good, because it seems like trying to substitute the human element," Rossi says. But in reality, data is only a tool. "The interpretation of the results, the change of the training load, is done by coaches," he says.
McHugh agrees that people have to make the final call. "Once the injury probability for an athlete on a given day is output from an injury model, the athlete or coach must then decide whether the predicted risk is acceptable or not, usually depending on the context, " he says. There might be a big game that day, and the layer might be especially important to the learn. "Even though the predicted injury probability be as high as 70%, the coco may be willing to take that chance," he says.
