Fantasy premier league is more a game of skill for experienced players than it would seem. This correlation has been found in previous works. Those at the top 0.5% of the total FPL players have been doing continuously good over the years.
We intend to study the habits of these top managers and find strategies that they seem to employ to get consistent results year after year. We will also take the betting odds from different betting sites to incorporate a return ratio for each player to see how or if they seem to reflect the performances on pitch.
The end goal of this project is to make a program which can take your team data and the athelete data for the past weeks on top of historical data (past season performances) and give a prediction for the best possible transfer to make and the captain to choose. Higher the points scored w.r.t other will correlate to higher success of the algorithm.
Pre-processing data (clustering based on team composition) to get players who are common in teams of current high ranked players and high ranked players of previous years, along with how similar are the teams.Analysing output as binary (1, if player is predicted to score > threshold points otherwise, 0)
We plan to do this analysis separately for goalkeepers, defenders, midfielders and forwards.
If time permits we intend to go a step ahead and: