Collaborative synchronous writing tools like Google Docs and Etherpad let multiple users edit the same document and see each others edits in near real-time to simplify collaboration and avoid merge-conflicts. These tools are used extensively across many domains, including education, in both research and industry. The very nature of needing to constantly synchronize state between multiple users means that very granular editing data is automatically captured and stored. In theory, this data could provide important insights into the editing process, the contributions of the different users, how the text developed over time, and other questions relevant to researchers studying writing from different theoretical and methodological angles. However, this extreme granularity of the data (down to individual key presses), makes analysis very complex. Most of the research focused on automatic analysis of collaborative writing to date has focused on asynchronous writing, and looked at the "diffs" between one editing session and the next. In this paper, we present a method and a tool to construct informative operations from text data, as well as preliminary metrics for measuring the collaborative writing process. Additionally, our method adds to previous work in that it can be used to assess the writing during the writing process rather than just being applied to an end product.