FPL-Team-Prediction

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FPL-Team-Prediction

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Fantasy Premier League prediction using Machine Learning

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# FPL-Team-Prediction
This project uses machine learning to predict the fantasy premier league performance of each player.

* **current year/ -** Current season's FPL data of each player 
* **data/ -** Historical FPL data of each player 
* **prediction/ -** Each gameweek's prediction data as well as model predictions 
* **training-data_2016-19/ -** Training data from previos seasons 
* **CalculatingFunctions.py -** Functions to shift rows, fill Nan values, calculate average of features in last few select gws 
* **FetchFPLData.py -** Functions to fetch team and player data using FPL APIs - (Fixture, gw data, player history, etc) 
* **FetchUnderstatData.py -** Functions to fetch team and player data from understat - (player xG, xA, team results, offensive/defensive form, etc) 
* **GBMFPLModel.py -** Build an XGBoost model for points prediction 
* **LinearRegressionFPLModel.py -** Build a Linear Regression model for points prediction 
* **RandomForrestFPLModel.py -** Build a Random Forest model for points prediction 
* **GetTeamPoints.py -** Fetch gameweek points of each player using FPL APIs 
* **MapUnderstatToFPL.py -** Functions to match and Consolidate the player's FPL and understat data 
* **PickTeam.py -** Use Linear Programming to pick optimal team 
* **PreparePredictionData.py -** Gather data for each player to make a prediction for the following gameweek 
* **PrepareTrainingData.py -** Gather historical data for each player for training the models 
* **ReadFPLData.py -** Read and process the collected FPL data of each player

For each gameweek, the models are trained using all historical data prior to that week starting from the 2016/17 season.
Some of the features used are:
* Player performance in the season so far, ex - goals, assists, clean sheets, bonus points, minutes played, etc
* Understat player data such as xG, xA and more
* Offensive and defensive form of the player's team
* Offensive and defensive form of the opponent team

After points forecast, Linear Programming is used to find an optimal team.

A comparison of the performance of all models will be added at the end of the season.

## Acknowledgments
**Sincere thanks to:** 
* https://github.com/vaastav/Fantasy-Premier-League for providing historical FPL data of each player
* https://github.com/amosbastian/understat for providing a Python package for Understat

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