> For the complete documentation index, see [llms.txt](https://wp.jannavi.net/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://wp.jannavi.net/mahjong-big-data-depin-project/matching-algorithm.md). # Matching Algorithm This section outlines the sophisticated matching algorithm used by JAN-Navi to enhance the competitiveness and enjoyment of mahjong games. By leveraging advanced analytics and machine learning, the platform ensures players are matched in a manner that promotes balanced and engaging gameplay. * **Skill Level Assessment** On JAN-Navi, a player's rank and game data, along with their match history on the blockchain, are analyzed to assess their overall skill level. This comprehensive evaluation helps in creating matches that are fair and challenging for all participants. * **Optimal Matchups** The algorithm developed by JAN-Navi matches players with optimal opponents based on a variety of factors, including skill level, game style, and geographic proximity. This tailored approach not only enhances player satisfaction but also fosters a competitive environment that is both fun and rewarding. * **Learning and Updates** Utilizing AI and machine learning, JAN-Navi continuously analyzes matching results to refine and improve the algorithm. This ongoing optimization process ensures that matchups become progressively fairer and more exciting, keeping the gaming experience fresh and engaging for players. --- # 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 dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://wp.jannavi.net/mahjong-big-data-depin-project/matching-algorithm.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.