A data-driven framework for team selection in Fantasy Premier League

TL;DR

This paper presents a data-driven optimization framework for selecting fantasy football teams in the Premier League, integrating predictive modeling and robust linear programming to improve decision-making under constraints.

Contribution

It introduces a novel mixed-integer linear programming approach combined with predictive scoring models for optimized fantasy team selection, including robustness and hybrid scoring enhancements.

Findings

ARIMA with a constrained budget performs best in out-of-sample tests.

Weighted averages and Monte Carlo simulations are also effective.

Robust and hybrid scoring metrics offer improvements but are not always superior.

Abstract

Fantasy football is a billion-dollar industry with millions of participants. Under a fixed budget, managers select squads to maximize future Fantasy Premier League (FPL) points. This study formulates lineup selection as data-driven optimization and develops deterministic and robust mixed-integer linear programs that choose the starting eleven, bench, and captain under budget, formation, and club-quota constraints (maximum three players per club). The objective is parameterized by a hybrid scoring metric that combines realized FPL points with predictions from a linear regression model trained on match-performance features identified using exploratory data analysis techniques. The study benchmarks alternative objectives and cost estimators, including simple and recency-weighted averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Monte Carlo simulation. Experiments on the 2023/24 Premier League season show that ARIMA with a constrained budget and a rolling window yields the most consistent out-of-sample performance; weighted averages and Monte Carlo are also competitive. Robust variants and hybrid scoring metrics improve some objectives but are not uniformly superior. The framework provides transparent decision support for fantasy roster construction and extends to FPL chips, multi-week rolling-horizon transfer planning, and week-by-week dynamic captaincy.