  The performance of football players in English Premier League varies largely
from season to season and for different teams. It is evident that a method
capable of forecasting and analyzing the future of these players on-field
antics shall assist the management to a great extent. In a simulated
environment like the Fantasy Premier League, enthusiasts from all over the
world participate and manage the players catalogue for the entire season. Due
to the dynamic nature of points system, there is no known approach for the
formulation of a dream team. This study aims to tackle this problem by using a
hybrid of Autoregressive Integrated Moving Average (ARIMA) and Recurrent Neural
Networks (RNNs) for time series prediction of player points and subsequent
maximization of total points using Linear Programming (LPP). Given the player
points for the past three seasons, the predictions have been made for the
current season by modeling differently for ARIMA and RNN, and then creating an
ensemble of the same. Prior to that, proper data preprocessing techniques were
deployed to enhance the efficacy of the prepared model. Constraints on the type
of players like goalkeepers, defenders, midfielders and forwards along with the
total budget were effectively optimized using LPP approach. The validation of
the proposed team was done with the performance in upcoming season, where the
players outperform as expected, and helped in strengthening the feasibility of
the solution. Likewise, the proposed approach can be extended to English
Premier League by official managers on-field.
