Sensors in soccer

At a glance

© Next11 Technologies

In sport and in soccer especially, interest in analysis and analysis techniques is constantly growing. In the professional leagues, games and players' performance are analyzed in a variety of different ways. And new rules now permit digital assistance in the coaching zone. In conjunction with real-time positioning systems, wearable technology for the players can already supply a variety of data automatically during the course of play. Given that such systems are relatively expensive and time-consuming to install, however, only the top clubs use them.

As part of the Eurostarts project iBall-R2 (funded by the Federal Ministry of Education and Research BMBF, report on "sensitive footballs"), Fraunhofer IIS and its partners - NEXT11 Technologies ApS, Select Sports A/s and MING Labs GmbH - developed a cost-effective system that enables coaches and players in amateur clubs to analyze their training and game performance and to work on improving specific aspects of their game. They developed a small, lightweight sensor module in close collaboration with the manufacturer, Select Sports A/s, and integrated it into a professional soccer ball in such a way that the sensor did not impair the ball's flight characteristics. The players are given a wearable device, which, like the ball module, can measure acceleration, turns, and magnetic fields and transmits the data to a central device, such as a tablet, via Bluetooth. Using an app, the coach and/or players can subsequently view the results of the analysis.


Click here to read the BMBF report (German).

© Eurostars
© Eurostars

Technical advantages

  • Affordable for all
    • Cost-effective system for analyzing sport during training and the game itself
    • Low, if any, installation costs
  • Recording and analysis of movements, speeds and actions to track players' fitness
  • Recognition and analysis of match events, notably shots, passes and ball possession
  • Can be connected to positioning systems

Sensors and machine learning in soccer

© Fraunhofer IIS/Milena Seeland

What’s NEW

Determining each team’s number of kicks, shots and passes and its share of possession is achieved through traditional methods of general and Bayesian statistical analysis. But when it comes to estimating players’ speeds, analysts turn to machine learning. Each player wears a sensor module that calculates various characteristics from the sensor data and sends these to the central processing unit, which in turn uses support vector regression to estimate each player’s speed.


© Fraunhofer IIS
  • Tag ID
  • Characteristics from sensor data
  • Kicks and shots
  • Indication of distance from ball
  • Time stamp
  • Battery status
  • States and actions

© Next11 Technologies
  • Robust, state-of-the-art Bluetooth communication between modules and to the central processing unit
  • Interface for wireless charging of ball modules
  • Sensors for measuring acceleration, rates of rotation, magnetic fields and air pressure
  • Software for preprocessing measurement data and optimizing data transfer

© Next11 Technologies
  • User and graphic interfaces
  • System administration and configuration
  • Software for analyzing players’ fitness, events during the game and course of play; analyzes individual players and the team as a whole

© Next11 Technologies


  • Management of Bluetooth communication with modules
  • Processing software that uses machine learning methods
  • Software for calculating all training- and game-relevant data
  • Database for training sessions and games
  • Live interface to data via Wi-Fi