Injury Forecaster

Preventing injuries on the field is a high priority for any sports team, so it’s no surprise that systems for predicting when a player is at high risk are in high demand, particularly in contact sports like soccer, cricket and football.

Last year, Microsoft introduced its Sports Performance Platform to predict player risk by tracking their recent performance and recovery time. It analyzes a variety of physiological measurements (including heart rate, diet, sleep, muscle soreness) and performance metrics (such as speed, acceleration, deceleration). Machine learning and artificial intelligence (AI) algorithms are used to gauge training levels and recovery intervals, with the ultimate goal of improving individual and team performance.

Another system, recently developed by data scientists in Italy, utilizes GPS sensors to measure similar performance factors, but also includes data on impact forces (with the ground and other players), as well as demographic data, their role on the field, past injury history and previous playing time. The system was able to predict injuries with an accuracy rate of about 60 percent. The developers are investigating whether integrating physiological data, including heart rate and perspiration levels, can increase prediction accuracy even further.

The systems are currently being used by several professional teams throughout the world, and while specific results are likely to be kept confidential, it appears that big data can have a positive impact on reducing sports injuries.