Support Vector Regression (SVR) is a powerful supervised machine learning model especially well suited to the normalized or binarized data. However, its quadratic complexity in the number of training examples eliminates it from training on large datasets, especially high dimensional with frequent retraining requirement. We propose a simple two-stage greedy selection of training data for SVR to maximize its validation set accuracy at the minimum number of training examples and illustrate the performance of such strategy in the context of Clash Royale Challenge 2019, concerned with efficient decks’ win rate prediction. Hundreds of thousands of labelled data examples were reduced to hundreds, optimized SVR was trained on to maximize the validation R2 score. The proposed model scored the first place in the Cash Royale 2019 challenge, outperforming over hundred of competitive teams from around the world.