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Repository designed to work with data from the Premier League. It is used as both a personal self-study as well as a tool to fetch, analyze and model performance metrics to maximize returns in a returns-based game called Fantasy Premier League (FPL).

Python 94.95% Jupyter Notebook 5.05%
dashboards data datacollection datavisualization fantasy-football jupyter-notebook machine-learning premier-league fantasy-premier-league

fpl-analytics's Introduction

Performance Analysis for FPL (Fantasy Premier League)

This project is used a personal self-study into several areas such as data collection & wrangling, feature engineering, data analysis & modeling and visualization. At surface level, it is used as a broad view of in-form players currently in the Premier League to inform an ideal Fantasy Premier League (FPL) team to maximize performance/points output. FPL is a game which assigns points to a player based on their performance output in a given game week, and so, being able to assess performance over time either by modeling or by assessment of surface statistics is vital to maximize returns.

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