The construction of realistic subject-specific models of the myocardial fiber architecture is relevant to the understanding and simulation of the electromechanical behavior of the heart. This paper presents a statistical approach for the prediction of fiber orientation from myocardial morphology based on the Knutsson mapping. In this space, the orientation of each fiber is represented in a continuous and distance preserving manner, thus allowing for consistent statistical analysis of the data. Furthermore, the directions in the shape space which correlate most with the myocardial fiber orientations are extracted and used for subsequent prediction. With this approach and unlike existing models, all shape information is taken into account in the analysis and the obtained latent variables are statistically optimal to predict fiber orientation in new datasets. The proposed technique is validated based on a sample of canine Diffusion Tensor Imaging (DTI) datasets and the results demonstrate marked improvement in cardiac fiber orientation modeling and prediction.