OpenFPL: An open-source forecasting method rivaling state-of-the-art Fantasy Premier League services
This democratizes access to accurate forecasts for Fantasy Premier League participants, though it's incremental as it applies existing ensemble modeling techniques to this specific domain.
The paper tackles the problem of limited access to accurate Fantasy Premier League player performance forecasts by developing OpenFPL, an open-source method using public data that achieves accuracy comparable to a leading commercial service and surpasses it for high-return players (>2 points).
Fantasy Premier League engages the football community in selecting the Premier League players who will perform best from gameweek to gameweek. Access to accurate performance forecasts gives participants an edge over competitors by guiding expectations about player outcomes and reducing uncertainty in squad selection. However, high-accuracy forecasts are currently limited to commercial services whose inner workings are undisclosed and that rely on proprietary data. This paper aims to democratize access to highly accurate forecasts of player performance by presenting OpenFPL, an open-source Fantasy Premier League forecasting method developed exclusively from public data. Comprising position-specific ensemble models optimized on Fantasy Premier League and Understat data from four previous seasons (2020-21 to 2023-24), OpenFPL achieves accuracy comparable to a leading commercial service when tested prospectively on data from the 2024-25 season. OpenFPL also surpasses the commercial benchmark for high-return players ($>$ 2 points), which are most influential for rank gains. These findings hold across one-, two-, and three-gameweek forecast horizons, supporting long-term planning of transfers and strategies while also informing final-day decisions.