FPL Team Picker (Auto FPL)

Auto FPL is an application that acts as a readonly wrapper around your FPL team. It provides data-driven optimization for Fantasy Premier League team selection using advanced algorithms and statistical analysis.

🎯 Main Features

1. Optimal Team Calculation - Uses mathematical optimization to suggest the best possible team configuration
2. Expected Points Analysis - Leverages xP (expected points) data for informed decision making
3. Real-time Data Integration - Connects directly to the official FPL API for up-to-date information

🚀 Use Cases

1. Calculate Transfers

• Analyzes your current team and suggests optimal player transfers
• Considers transfer costs (-4 points) vs potential point gains
• Evaluates both short-term and long-term transfer strategies
• Takes into account upcoming fixtures, player form, and injury status

2. Calculate Wildcard

• Builds an entirely new optimized team when using your wildcard chip
• No transfer cost constraints - complete team reconstruction
• Optimizes budget allocation across all positions (GK, DEF, MID, FWD)
• Considers team diversity and fixture difficulty

3. TOTS (Team of the Season)

• Identifies the highest-performing players across the entire season
• Useful for end-of-season analysis and planning
• Helps identify consistent performers vs. differential picks

🏗️ Architecture

The application follows a clean architecture pattern with the following components:

Backend (.NET 8 API)

• FplTeamPicker.Api - REST API endpoints and controllers
• FplTeamPicker.Domain - Core business entities and contracts
• FplTeamPicker.Services - Business logic and use cases
• FplTeamPicker.Optimisation - Mathematical optimization algorithms
• FplTeamPicker.Tests - Unit and integration tests

Frontend (React + TypeScript)

• React 19 with TypeScript for type safety
• Vite for fast development and building
• Tailwind CSS for styling
• Headless UI for accessible components

Data Analysis (Python)

• Jupyter Notebooks for data exploration and model training
• Expected Points (xP) Analysis for player performance prediction
• Goalkeeper-specific models for position-based optimization

🎲 FPL Rules & Constraints

The optimization algorithm respects all official FPL rules:

Team Composition

• 15 total players: 11 starting, 4 bench
• 2 Goalkeepers: 1 starting, 1 bench
• 5 Defenders: 3-5 can start
• 5 Midfielders: 2-5 can start
• 3 Forwards: 1-3 can start
• £100.0m total budget

Team Restrictions

• Maximum 3 players from any single Premier League team
• 11 starting players must be selected each gameweek
• Captain scores double points, Vice-captain as backup

Transfer System

• 1 free transfer per gameweek (can bank up to 2)
• -4 point penalty for each additional transfer
• Wildcard chip allows unlimited free transfers (limited uses)

🧠 Optimization Model

Objective Function

Maximize expected points while respecting all FPL constraints:

Maximize: Σ(player_xp * selection_weight) - transfer_penalties

Key Metrics

• xP (Expected Points) - Predicted points based on underlying statistics
• xP per £ - Value efficiency metric
• Fixture Difficulty Rating - Upcoming match difficulty
• Ownership % - Template vs. differential considerations
• Form - Recent performance trends

Position-Specific Analysis

• Goalkeepers - Clean sheet probability, save points, bonus potential
• Defenders - Clean sheets, attacking returns, bonus points
• Midfielders - Goals, assists, clean sheet points (if playing defense)
• Forwards - Goals, assists, penalty taking

🛠️ Technology Stack

Backend

• .NET 8 - Modern C# framework
• MediatR - CQRS pattern implementation
• ASP.NET Core - Web API framework
• HTTP Client - FPL API integration

Frontend

• React 19 - Latest React with concurrent features
• TypeScript - Type-safe JavaScript
• Vite - Fast build tool and dev server
• Tailwind CSS - Utility-first CSS framework
• Axios - HTTP client for API calls

Data Science

• Python - Data analysis and modeling
• Jupyter Notebooks - Interactive data exploration
• Pandas/NumPy - Data manipulation and analysis

🚦 Getting Started

Prerequisites

• .NET 8 SDK
• Node.js 18+
• Python 3.8+ (for data analysis)

Backend Setup

cd Api
dotnet restore
dotnet run --project FplTeamPicker.Api

Frontend Setup

cd Web/fpl-team-picker
npm install
npm run dev

API Documentation

Once running, visit http://localhost:5079/swagger for interactive API documentation.

📊 Data Sources

• Official FPL API - Player data, fixtures, team information
• Historical Performance - Season-long statistics and trends
• Expected Points Models - Custom xP calculations based on underlying stats

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

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

⚠️ Disclaimer

This tool is for educational and entertainment purposes. Fantasy Premier League involves an element of luck, and no algorithm can guarantee success. Always make your own informed decisions!