Automated 3-D modeling from real sports videos can provide useful resources for visual design in sports-related computer games, saving a lot of effort in manual design of visual contents. However, image-based 3-D reconstruction usually suffers from inaccuracy caused by statistic image analysis. In this paper, we propose an information-theoretical scheme to minimize errors of automated 3-D modeling from monocular sports videos. In the proposed scheme, mutual information (MI) was exploited to compute the fitting scores of a 3-D model against the observed single-view scene, and the optimization of model fitting was carried out subsequently. With this optimization scheme, errors in model fitting were minimized without human intervention, allowing automated reconstruction of 3-D animation from consecutive monocular video frames at high accuracy. In our work, the Snooker videos were taken as our case study, balls were positioned in 3-D space from single-view frames, and 3-D animation was reproduced from real Snooker videos. Our experimental results validated that the proposed information-theoretical scheme can help attain better accuracy in the automated reconstruction of 3-D animation, and demonstrated that information-theoretical evaluation can be an effective approach for model-based reconstruction from single-view videos.