> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/zingg/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/zingg/versions/community-v0.7.0/explainoutput/stats.md). # Output Statistics Under the hoods of the matching process [Zingg Enterprise Feature](#user-content-fn-1)[^1] If you’ve ever asked “how deterministic rules are performing?” or “did my latest incremental run improve cluster quality?”, Output Statistics is your answer. The Output Statistics surface information about the linkages Zingg found among records within a cluster. While running Zingg incrementally, Output Statistics expose how cluster numbers change as records get inserted and updated into the identity graph. Match Statistics surfaces those insights by writing structured metrics for every match or incremental run, so you can: * See how dense or sparse your clusters are * Understand how much of a cluster is explained by deterministic rules vs. probabilistic links * Identify highly central records (connectors) and outliers * Track how clusters change across runs (growth, splits, merges, reassignments) If the number of clusters changes disproportionately to the number of records updated or added, an alert could be triggered. *** ## What gets written Zingg writes statistics to the stats directory whenever you run phases like match or incremental. The output comprises of three types: * SUMMARY: High-level run summary * CLUSTER: One row per cluster with cluster level matching metrics * RECORD: One row per record with its matching metrics within its cluster [^1]: Zingg Enterprise is the suite of proprietary products licensed by Zingg. Please refer to [Zingg Entity Resolution Compare Versions](https://www.zingg.ai/product/zingg-entity-resolution-compare-versions) for individual tier features. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.zingg.ai/zingg/versions/community-v0.7.0/explainoutput/stats.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.