Time Series Modeling for Dream Team in Fantasy Premier League

TL;DR

This paper presents a hybrid time series forecasting method combining ARIMA and RNNs, optimized with Linear Programming, to predict player points and assemble an optimal fantasy football team, demonstrating promising results for future seasons.

Contribution

It introduces a novel hybrid forecasting approach for fantasy football player points and an optimization framework for team selection, addressing the dynamic points system challenge.

Findings

Predictions outperform baseline models in accuracy.

Optimized team selection improves total points achieved.

Method successfully validated with upcoming season performance.

Abstract

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.