Identifying Basketball Plays from Sensor Data; Towards a Low-Cost Automatic Extraction of Advanced Statistics

Abstract

Advanced statistics have proved to be a crucial tool for basketball coaches in order to improve training skills. Indeed, the performance of the team can be further optimized by studying the behaviour of players under certain conditions. In the United States of America, companies such as STATS or Second Spectrum use a complex multi-camera setup to deliver advanced statistics to all NBA teams, but the price of this service is far beyond the budget of the vast majority of European teams. For this reason, a first prototype based on positioning sensors is presented. An experimental dataset has been created and meaningful basketball features have been extracted. 97.9% accuracy is obtained using Support Vector Machines when identifying 5 different classic plays: floppy offense, pick and roll, press break, post-up situation and fast breaks. After recognizing these plays in video sequences, advanced statistics could be extracted with ease.