In 2005, 17-year-old aspiring footballer Alessio Rossi tore two ligaments in his right ankle during training for lower league Italian football club USD Olginatese. The injury ended his dream of playing at the highest level. Today, Rossi is a postdoctoral researcher at the University of Pisa, Italy, where he collects and analyses reams of data to help prevent players at top teams getting injuries of their own. When Rossi was playing, his coaches` instincts and experiences were all they had to predict whether he might receive an injury. Now, a footballer training with top-level teams, such as those in the English Premier League, will wear a tight top under their jersey outfitted with GPS, an accelerometer, a gyroscope and a digital compass. While they run drills, the sensors track their heart rate, speed and distance covered. "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. Coaches have always taken into account the condition of players when scheduling training sessions. Now they can calculate more precisely the probability that individual athletes will get injured during the next match, the next week or the next month. Professional footballers experience between 2.5 and 9.4 injuries per 1,000 hours of exertion (D. Pfirrmann et al. J. Athl. Train. 51, 410-424; 2016), about one-third of which are from overuse and therefore potentially predictable. Most injuries last about a week, but recurrent ones — about 15% of the total — often require more rest. During this time, the player`s physical and mental conditioning declines, and their careers might suffer as a result of damage to their reputation or concerns over their ability to fully recover. Their time off might also increase their teammates` workload, increasing the likelihood of injuries throughout the squad. The problem is even worse outside elite athletics. Youth sport, for instance, is experiencing "a pandemic of injuries", says Dhruv Seshadri, a biomedical engineer at Case Western Reserve University in Cleveland, Ohio. Athletes as young as 10 are pushing themselves harder, trying to propel themselves towards a career in professional sports. The rate of injury for youth football players, for example, can be as high as 19.4 injuries per 1,000 hours of exertion. Sports scientists started using data analytics such as those Rossi employs only in the past decade, but the hope is that the approach could save careers and money, as well as improve results. Researchers and coaches are working to develop methods of collecting and analysing the quantity of data required to make predictions, not only for team sports such as football or basketball, but also for events such as figure skating or tennis, in which people often compete as individuals.
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