In this work, we propose a framework to build a billiards tutoring system based on broadcasting nine-ball video analysis. A robust table detection module is developed by mapping the displayed video frames to a predefined billiard table model. In addition, we detect balls and trace their positions at every time instant. The real-world spatial relationships between the table and the balls are used to provide the aiming and position play suggestions. Ball position information is also utilized to distinguish each play into corresponding event by a rule-based method. The experimental results are encouraging and are more comprehensive than existing works of billiards video analysis.