Devices, real-time analytics and the next wave of athletics

My colleagues and I have written about how professional sports franchises are applying business intelligence tools to their operations, and the conversation deserves expansion. 

As opposed to giving you a general overview of how certain teams are using data visualization, I'm going to tackle this discussion from two angles: smart devices and real-time analytics. How are coaches using these technologies to develop training programs, create game plans and measure athlete performance? What could they be doing?

"Chester University's study is one that involves historical data, not real-time analytics."

The machines and how to use them 
The Guardian noted a study conducted by Chester University in 2011. By putting small GPSs in player uniforms, the organization discovered that elite rugby players cover between 2.76 miles and 4.22 miles per game. The company discovered that outside backs often prove to be the fastest players, reaching 19 miles per hour on average during games. 

This is just one example of devices being used to provide a different perspective into the game. Based on the distance statistic noted above, a rugby coach could design regimens in a way that forces players to run about 6 miles per practice. 

Yet Chester University's approach is one that involves historical data, not real-time analytics. While the data provided by the GPS technology was streaming, it wasn't being processed, but simply recorded. Still, that doesn't mean real-time analysis can't be applied. 

"The way coaches use real-time analytics is similar to the way manufacturers conduct predicative maintenance."

Opening up creative approaches 
The reason why real-time data collection and visualization tools likely weren't applied to the GPS information is probably due to a lack of other hardware. The more diverse your sensor portfolio is, the greater number of perspectives you'll be able to aggregate. If you can tackle a problem from multiple angles, you'll be able to cross-validate information and thus develop more reliable conclusions. Integrating real-time data analysis simply allows you to reach these judgment as events are occurring. 

For instance, The Guardian noted that Cityzen Science is equipping the French elite rugby team with garments with embedded GPSs, heart monitors, speedometers, and other smart sensors. This gives the French coaches access to myriad data points. Allow those jerseys to send information to a tablet running a data visualization program, and new possibilities unfold. For instance, a visualization could tell a coach whether a certain player performs better at a certain end of the field, how he responds to certain situations, etc. 

In a way, this approach to real-time analytics isn't too far from the way manufacturers conduct predictive maintenance. From this perspective, what's stopping athletic trainers from using the same technology to prevent player injuries?