Fantasy Premier League Team Optimizer - Win at FPL with lazines
Project description
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
Project details
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