High-speed nanoindentation allows the acquisition of large mechanical property maps with micron-scale resolution with indentation test lasting down to 1 second. This provides datasets with sufficient density of data to perform statistical analysis, enabling the understanding of the mechanical performance of complex materials, such as hardmetals, with a level of detail not available if a small number of data is collected. In addition, in case these maps are combined with other techniques that capture chemical, crystallography or microstructure, a more in depth understanding can be achieved. In this study a coarse and a fine grain of WC-Co and WC-Ni hardmetal grades were used. High-speed nanoindentation technique has been used to obtain hardness and elastic modulus maps. Data has been statistically analyzed finding 2D clusters considering hardness and elastic modulus by means of the Gaussian Mixture Model (GMM) determining the optimal number of Gaussians with the Bayesian Information Criterion (BIC). The statistical analysis has been compared to the gaussian fitting used in the literature. SEM images of the indentation and EBSD maps have been performed to correlate microstructure with nanoindentation maps and measured mechanical properties.