With the rapid development of sharing economy, massive sharing systems such as Uber, Airbnb, and bikeshare have percolated into people's daily life. The sharing economy, at its core, is to achieve efficient use of resources. The actual usage of shared resources, however, is unclear to us. Little measurement or analysis, if any, has been conducted to investigate the resource usage status with the large-scale data collected from these sharing systems. In this paper, we analyze the bike usage status in three typical bikeshare systems based on 140-month multi-event data. Our analysis shows that the most used 20% of bikes account for 45% of usage, while the least used 20% of bikes account for less than 1% of usage. To efficiently utilize shared bikes, we propose a usage balancing design called eShare which has three components: (i) a statistical model based on archived data to infer historical usage; (ii) an entropy-based prediction model based on both real-time and archived data to infer future usage; (iii) a model-driven optimal calibration engine for bike selection to dynamically balance usage. We develop an ID swapping based evaluation methodology and measure the efficiency of eShare with data from three systems including the world's largest bikeshare system with 84,000 bikes and 3,300 stations. Our results show that eShare not only fully utilizes shared bikes but also improves service quality.