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Automatic events annotation is an essential requirement for constructing an effective sports video summary. Researchers worldwide have actively been seeking the most robust and powerful solutions to detect and classify key events (or highlights) in different sports. Most of the current and widely used approaches have employed rules that model the typical pattern of audiovisual features within particular sport events. These rules are mainly based on manual observation and heuristic knowledge; therefore, machine learning can be used as an alternative. To bridge the gap between the two alternatives, we propose a hybrid approach, which integrates statistics into logical rule-based models during highlight detection. We have also successfully pioneered the use of play-break segment as a universal scope of detection and a standard set of features that can be applied for different sports, including soccer, basketball, and Australian football. The proposed method uses a limited amount of domain knowledge, making this method less subjective and more robust for different sports. An experiment using a large data set of sports video has demonstrated the effectiveness and robustness of the algorithms.