Analyzing Tennis Game through Sensor Data with Machine Learning and Multi-Objective Optimization

Wearable devices are heavily used in many sports. However, the existing sports wearables are either not tennis-specific, or are limited to information on shots. We therefore added tennis-specific information to a leading commercial device. Firstly, we developed a method for classifying shot types into forehand, backhand and serve. Secondly, we used multi-objective optimization to distinguish active play from the time in-between points. By combining both parts with the general movement information already provided by the device, we get comprehensive metrics for professional players and coaches to objectively measure a player's performance and enable in-depth tactical analysis.