This paper explores a simple discriminative preordering model for statistical machine translation. Our model traverses binary constituent trees, and classifies whether children of each node should be reordered. The model itself is not extremely novel, but herein we introduce a new procedure to determine oracle labels so as to maximize Kendall's $\tau$. Experiments in Japanese-to-English translation revealed that our simple method is comparable with, or superior to, state-of-the-art methods in translation accuracy.