JNToken
  • The JN Token Whitepaper
  • Introduction
    • Mission
    • Project Overview
    • Innovative Project Realization
  • The JAN-Navi Platform
    • Ecosystem
    • Pioneering Achievements
    • Creating New Value Through Web3
    • JAN-Navi e-Sports
  • JN Token
    • Decentralization of e-Sports with JN Token
    • JN Token Ecosystem
    • JN Token Utility
    • JN Token Event Features
    • JN Token Pricing Strategy
    • Technical Information
    • Token Allocation and Lockup Schedule
    • Private Sales
  • Mahjong Big Data DePin Project
    • Overview of Mahjong Big Data DePIN Project
    • Business Model
    • What is Big Data?
    • Online Player Data Collection
    • Offline Player Data Collection
    • Matching Algorithm
    • Global Matchmaking
    • Integration of JN Token and DePIN Ecosystem
  • Expansion of the JAN-Navi ecosystem
    • Market Development by JAN-Navi
    • Network Integration and Data Coordination of Mahjong Parlors
    • JAN-Navi White Label Expansion
    • O2O (Online to Offline) Strategy
    • Extension to Other Table Games
  • Technical Architecture
    • Technical Superiority
    • Data Decentralization and Security
  • Roadmap
    • Project Roadmap
    • Pilot Mahjong Big Data Project Deployment
    • JAN-Navi Phased Expansion
  • Appendices
    • JAN-Navi e-Sports Guidelines
    • JAN-Navi Demographics
    • Forecast for the Growth of the e-Sports Market
    • Team and Partners
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  1. Mahjong Big Data DePin Project

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.

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Last updated 11 months ago