Capturing Player Statistics in an Armature Basketball Game

In its simplest description, sports analytics is the application of mathematical and statistical principles to sports and other peripheral activities. While many features and objectives are unique to the sports industry, sports analysts use the same core methodology and approaches as any other kind of data analyst. However, monitoring data has paved the path for new ways to sports analysis via the use of Artificial Intelligence. Recent sports analysis literature has investigated the use of deep learning approaches to obtain meaningful data by employing object tracking and identification to gather data for athletes by using action recognition patterns to detect and monitor the player’s behavior on the field of play. The main objective of this project is to establish a framework for amateur basketball analytics. The system will be able to capture salient events developed on the court, using a robotic rover design with embedded control capability, and a server-side software system including specially trained objet detection and event classification models and a database solution to store player and event statistical data. 

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