The left ventricular (LV) deformation is associated with the LV function, an analysis of which gives insights into the LV mechanics, increasing the diagnostic value of typical parameters like volume, ejection fraction (EF) and strain. This article describes a statistical technique to characterize the LV deformation. 43 cardiac magnetic resonance imaging (MRI) datasets, consisting of normal and diseased LV, were used in the experiment. The LV endocardial contours were segmented from the MRI slices across the different frames, and reconstructed as spatiotemporal LV meshes. The geometries were aligned and vertex correspondences between the geometries were computed. The vertex deformation of the LV endocardial surface meshes were then computed at the end-diastolic (ED) and end-systolic (ES) frames. The principal component analysis (PCA) was applied to extract the different modes of variation of the LV deformation. This allows the parametric model to describe the complex LV deformation variation. The experiment shows that there are substantial statistical differences in the LV deformation between the normal and diseased LV geometries. The proposed statistical model can be used to simulate the deformation of the LV geometry, and for the categorization of the LV function in the dataset.