An optimisation approach towards soccer Fantasy Premiere League team selection

Abstract

\textit{Fantasy Premiere League} (FPL) is a popular online sports prediction game based on the well-known (soccer) \textit{English Premiere League} (EPL). In FPL, each participant forms a series of imaginary teams over the course of a season of the EPL, each composed of real-world soccer players. Points are then awarded to participants based on the real-world performances of the players in the EPL. The goal is to accumulate as many points as possible during the season. FPL Participants are allocated a budget for team selection which gives rise to the constrained team selection optimisation problem of filling playing positions in the team for each game week of the FPL season. This team selection problem is further complicated by the facts that player performance is not known in advance with certainty and that participants may only make limited changes to their team compositions in the form of transfers during any game week. In this paper we adopt a combinatorial optimisation approach towards participating in FPL, in which future player performances are forecast by statistical and machine learning techniques. We demonstrate retrospectively that our approach would have placed within the top 4\% of players worldwide during the 2020/2021 FPL season.