Goal events are important in automatic analysis of broadcast sports game videos, but previous approaches rely on visual or audio information which are hard to obtain. In this paper, we use superimposed texts to detect goals (both the occurrences of goal events and their types) for broadcast basketball video, and we propose a transition pattern based approach for both text extraction and goal detection. Our approach is lightweight and effectively handles main challenges in extracting superimposed texts: complex background, low-resolution and blur of the texts, which made standard localization and character recognition algorithms inaccurate. We focus on extracting superimposed game clock and game score texts in broadcast basketball video. We exploit transition patterns to develop a Hough transform for localization, and conditional random fields (CRFs) for both score digit recognition and goal detection. The experiments show that our transition pattern based approach leads to high accuracy for both superimposed text extraction and goal detection.