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The majority of existing work on sports video analysis concentrates on highlight extraction. Little work focuses on the important issue as how the extracted highlights should be organized. In this paper, we present a multimodal approach to organize the highlights extracted from racket sports video grounded on human behavior analysis using a nonlinear affective ranking model. Two research challenges of highlight ranking are addressed, namely affective feature extraction and ranking model construction. The basic principle of affective feature extraction in our work is to extract sensitive features which can stimulate user's emotion. Since the users pay most attention to player behavior and audience response in racket sport highlights, we extract affective features from player behavior including action and trajectory, and game-specific audio keywords. We propose a novel motion analysis method to recognize the player actions. We employ support vector regression to construct the nonlinear highlight ranking model from affective features. A new subjective evaluation criterion is proposed to guide the model construction. To evaluate the performance of the proposed approaches, we have tested them on more than ten-hour broadcast tennis and badminton videos. The experimental results demonstrate that our action recognition approach significantly outperforms the existing appearance-based method. Moreover, our user study shows that the affective highlight ranking approach is effective.