lazyfpl 0.0.1

Fantasy Premier League Team Optimizer - Win at FPL with lazines

Fantasy Premier League Team Optimizer - Win at FPL with lazines

This Python project is designed to analyze and optimize Fantasy Premier League (FPL) team selections using data-driven techniques.

Modules Overview

• lazyfpl/backevel.py: Back evaluation of player performance.
• lazyfpl/conf.py: Configuration settings.
• lazyfpl/constraints.py: Team selection constraints.
• lazyfpl/database.py: Database interactions.
• lazyfpl/fetch.py: Data fetching from FPL API.
• lazyfpl/ml_model.py: Machine learning model for player performance prediction.
• lazyfpl/optimizer.py: Team selection optimization.
• lazyfpl/populator.py: Data population from external sources.
• lazyfpl/structures.py: Data structures definition.
• lazyfpl/transfer.py: Management of player transfers.

Basic Usage Examples

# Builds local player database.
python3 -m lazyfpl.populator

# Train ml-model (used to estiate expected points per player).
python3 -m lazyfpl.ml_model

# Backeval the model (optional).
python3 -m lazyfpl.backevel

# Based on upcoming fixture thufness, team synergy and expected points (from ML-model)
# show optimal team comparisons.
# This will exclude player with news and below mean-minutes played 60
python3 -m lazyfpl.optimizer --no-news --min-mtm 60