Many web documents are dynamic, with content changing in varying amounts at varying frequencies. However, current document search algorithms have a static view of the document content, with only a single version of the document in the index at any point in time. In this paper, we present the ﬁrst published analysis of using the temporal dynamics of document content to improve relevance ranking. We show that there is a strong relationship between the amount and frequency of content change and relevance. We develop a novel probabilistic document ranking algorithm that allows diﬀerential weighting of terms based on their temporal characteristics. By leveraging such content dynamics we show signiﬁcant performance improvements for navigational queries.