There are certain types of stories that are often told in very structured ways; sports stories or financial reports are two examples. Readers care about these narratives because they are passionately interested in the topic and want to read about the specific details of the event. In other words, they care about the data and want to read a story that presents that data to them. However, in order to be compelling these narratives cannot merely repeat the data, rather they must tell a story from the data. In this paper, we will present a model for data-driven story-telling and discuss StatsMonkey, a system that automatically writes baseball stories from raw baseball game numerical data available online. We will show that a machine can generate interesting, readable stories and that it can make editorial decisions about what aspects of a situation to highlight. Further we will show that a machine can determine in what manner those aspects should be shared.