Recognizing the importance of smart grid data analytics, modern statistical learning tools are applied here to wholesale electricity market inference. Market clearing congestion patterns are uniquely modeled as rank-one components in the matrix of spatiotemporally correlated prices. Upon postulating a low-rank matrix factorization, kernels across pricing nodes and hours are systematically selected via a novel methodology. To process the high-dimensional market data involved, a block-coordinate descent algorithm is developed by generalizing block-sparse vector recovery results to the matrix case. Preliminary numerical tests on real data corroborate the prediction merits of the developed approach.