In this work, we improve the efficiency by exploring the connection between time and locality. We propose a statistical model converting cheap time distance to costly reuse distance. Compared to the state-of-the-art technique, this approach reduces measuring time by 17 times, and approximates cache line reuses with over 99% accuracy. Experiments demonstrate the effective uses of the approximated reuse distance in cache miss rate prediction. This work, for the first time, reveals the strong correlations between time and locality. It makes precise locality as easy to obtain as data access frequency, removes the obstacles to reuse distance's practical uses, and opens new opportunities for program optimizations.