Big Data for the Minor Leagues

16 February 2017

In an effort to make big data analytics more accessible for the sports industry, researchers from the University of Illinois at Urbana-Champaign have used IoT devices—low-cost sensors and radios—that can be embedded into sports equipment (balls, rackets, and shoes), as well as in wearable devices.

Sensors are wrapped in a protective case and embedded in a cricket ball.Sensors are wrapped in a protective case and embedded in a cricket ball.According to researchers, there's a lot of interest in analyzing sports data though high-speed cameras, but a system can cost up to $1 million to implement and maintain. Researchers want to cut down the expense by replacing cameras with inexpensive internet-of-things devices (costing less than $100 in total) to make it possible for many other organizations to use the technology.

The research team developed advanced motion tracking algorithms from the various incomplete and noisy measurements of inertial measurement unit (IMU) sensors and wireless radios, fitted inside a ball and players' shoes. If the technology gains traction, real-time analytics should be possible at anytime, anywhere.

The sensors, which are wrapped in a protective case and distributed evenly in equipment, use inferencing algorithms that can track movement to within a few centimeters. They can accurately characterize 3-D ball motion, such as trajectory, orientation, and revolutions per second.

According to researchers, the level of accuracy and accessibility could help players in local clubs read their own performance from their smartphones via Bluetooth. Or school coaches could offer feedback to their students. The feedback could also help with detecting and analyzing player injuries, such as concussions.

The sensor inside a soccer ball, for example, can measure how hard it hits a player's head, giving coaches an indication about whether to treat the player for head injury.

The paper, to be published in USENIX NSDI 2017, explores tracking the 3-D trajectory and spin parameters of a cricket ball; however, the core motion tracking techniques can be generalized to many different sports analytics.

The research team has also been developing methods to charge the sensors, including harvesting energy from the spin of thea ball.

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