We present a computational approach for predicting the popularity score of sneakers through the analysis of growing amount of online data. Sneakers are described in several aspects based on which a popularity prediction model is constructed. In particular, we utilize the multiple kernel learning technique with customized kernels to analyze multimodal data extracted from an online sneaker magazine. The construction of a prediction model from multiple facets is not trivial - the effectiveness of each feature depends on the way we compute and combine it with the others. We examine a few design choices and study how multimodal data should be utilized to achieve practical prediction.