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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:

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