开源模型OpenFPL用公开数据实现顶尖足球预测精度
OpenFPL: An open-source forecasting method rivaling state-of-the-art Fantasy Premier League services
- 基于公开数据构建分位置集成模型，优化于四赛季历史数据
- 2024-25赛季前瞻测试中表现媲美头部商业服务
- 对高回报球员预测更优，适合长期转会与临场决策
梦幻英超（Fantasy Premier League）鼓励球迷从每轮比赛中选出表现最佳的英超球员。准确的性能预测能帮助参赛者预判球员表现、降低选队不确定性，从而获得竞争优势。然而，高精度预测目前仅限于不公开算法和依赖专有数据的商业服务。本文提出OpenFPL——一个完全基于公开数据的开源梦幻英超预测方法。该模型由四个赛季（2020-21 至 2023-24）的梦幻英超与Understat数据训练而成，包含分位置的集成模型。在2024-25赛季前瞻性测试中，其预测精度与领先商业服务相当，并在高回报球员（>2分）的预测上超越商业基准。该优势在1、2、3轮预测周期中均成立，支持长期转会规划与末轮决策。
原文摘要 · Abstract (English)
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.
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