Data Oracle - League of Legends Esports Analytics Dashboard

Overview

Data Oracle is a comprehensive analytics dashboard for League of Legends esports data. It provides in-depth analysis and visualization of professional League of Legends matches, allowing users to explore champion statistics, player performance, team metrics, matchups, and more.

Features

Data Analysis

• Champion Statistics: Track pick/ban rates, win rates, KDA, damage share, gold share, and position preferences
• Player Performance: Analyze individual player metrics across different champions and positions
• Team Analytics: Compare team performance, objective control, and side selection advantages
• Game Analysis: Detailed breakdown of individual matches with player and team performance
• Matchups: Head-to-head champion and duo matchup statistics with performance metrics
• Draft Simulator: Experiment with team compositions using synergy and matchup data

Data Visualization

• Interactive tables with sorting and filtering capabilities
• Performance metrics with time-series data (gold, XP, CS differences at 10/15/20/25 minutes)
• Champion synergy and counter relationships
• Team and player tendencies

Data Import

• CSV file upload functionality for custom data analysis
• Support for standard League of Legends esports data formats
• Data validation to ensure proper formatting

Getting Started

Prerequisites

• Node.js (version 14 or higher)
• npm or pnpm

Installation

1. Clone the repository

   git clone https://github.com/CloudyWSA/oracle-data
   cd oracle-data
   

2. Install dependencies

   npm install
   # or
   pnpm install
   

3. Start the development server

   npm run dev
   # or
   pnpm dev
   

4. Open http://localhost:3000 in your browser

Usage

1. Upload Data: Use the file uploader on the home page to import your League of Legends match data in CSV format
2. Explore Dashboard: Navigate through the different tabs to analyze various aspects of the data
3. Filter and Sort: Use the filtering options to focus on specific patches, leagues, or teams
4. Analyze Matchups: Explore champion matchups and synergies to understand meta trends

Data Format Requirements

The uploaded CSV file should contain the following required columns:

• gameid: Unique identifier for each game
• participantid: Player ID (1-10) or team ID (100 for blue side, 200 for red side)
• side: Team side ("Blue" or "Red")
• result: Match result (1 for win, 0 for loss)

For player data (participantid 1-10):

• playername: Name of the player
• champion: Champion played
• position: Role position (top, jng, mid, bot, sup)

For team data (participantid 100/200):

• teamname: Name of the team

Additional columns for enhanced analysis:

• Performance metrics (kills, deaths, assists, etc.)
• Economy metrics (gold, CS)
• Objective control (dragons, barons, towers)
• Time-series data (stats at 10/15/20/25 minutes)

Technologies

• Next.js: React framework for the frontend
• TypeScript: Type-safe JavaScript
• Tailwind CSS: Utility-first CSS framework
• Radix UI: Unstyled, accessible UI components
• Papa Parse: CSV parsing library

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• League of Legends esports data providers
• The League of Legends competitive community