AI World Cup 2026: Benchmarking Large Language Models for End-to-End Football Tournament Prediction

📄 arXiv: 2608.03416v1 📥 PDF

作者: Jonaid Shianifar, Iias Faiud

分类: cs.AI, cs.LG

发布日期: 2026-08-04

备注: 18 pages, 8 figures, 7 tables. Project repository available in the paper

💡 一句话要点

提出AI世界杯基准以评估大型语言模型的足球赛事预测能力

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 大型语言模型 赛事预测 标准化基准 人工智能 数据分析

📋 核心要点

  1. 现有的预测方法在信息来源和评估标准上存在差异，导致模型间的比较困难。
  2. 论文通过AI世界杯基准，确保所有模型在相同条件下进行赛事预测，以提高可比性。
  3. 实验结果表明，预测完整赛事的能力与逐场预测存在显著差异，且淘汰赛表现对最终得分影响较大。

📝 摘要（中文）

大型语言模型（LLMs）在预测现实事件方面的应用日益普遍，但由于模型接收的信息、使用的工具和评估规则各不相同，比较变得困难。本文报告了已完成的AI世界杯基准，其中十个基于LLM的助手对2026年国际足联世界杯进行了单一的赛前预测。所有提交均使用相同的赛事快照、提示、JSON架构和评分程序。预测涵盖了小组赛得分、小组排名、淘汰赛对阵、最终名次、置信值和简短解释。最终结果显示，GPT-5.5 Thinking以744分获胜，表明完整赛事预测测试了与逐场比赛预测不同的能力，同时也展示了基于淘汰赛的排行榜如何强烈依赖于评分设计。

🔬 方法详解

问题定义：本文旨在解决大型语言模型在赛事预测中的比较困难，现有方法因信息和评估标准不同而难以进行有效对比。

核心思路：通过设计AI世界杯基准，确保所有模型在相同的赛事快照和评分程序下进行预测，从而提高预测结果的可比性。

技术框架：整体架构包括统一的赛事快照、提示、JSON架构和评分程序，所有模型在此框架下进行预测，涵盖小组赛和淘汰赛的各项指标。

关键创新：该研究的创新在于创建了一个标准化的预测基准，使得不同模型的预测结果可以直接比较，揭示了完整赛事预测与逐场预测的本质区别。

关键设计：模型的预测包括小组赛得分、排名、淘汰赛对阵和最终名次等，采用统一的评分机制，确保了结果的公平性和一致性。实验中还记录了模型的自我报告置信度，以分析其与预测准确性的关系。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，GPT-5.5 Thinking以744分获胜，准确预测了决赛冠军西班牙，且总得分与淘汰赛表现高度相关（r=0.986）。Claude Sonnet 4.6在小组赛预测中表现最佳，但整体排名第六，表明不同预测阶段的表现差异。

🎯 应用场景

该研究的潜在应用领域包括体育赛事预测、博彩行业、数据分析和人工智能决策支持系统。通过标准化的预测基准，可以为未来的模型开发和评估提供参考，推动相关领域的研究进展。

📄 摘要（原文）

Large language models (LLMs) are now regularly asked to forecast real-world events, but comparisons are often difficult because models receive different information, use different tools, and are evaluated under different rules. This paper reports the completed \emph{AI World Cup} benchmark, in which ten LLM-based assistants made a single pre-tournament forecast of the entire 2026 FIFA World Cup. Every submission used the same tournament snapshot, prompt, JSON schema, and scoring procedure. The forecasts covered group-stage scores, group rankings, the knockout bracket, final placings, confidence values, and short explanations. After all 104 matches had been played, GPT-5.5 Thinking finished first with 744 points, followed by GPT-5.5 with 717, Gemini with 699, and Qwen 3.7 with 687. GPT-5.5 Thinking was also the only model to select Spain, which defeated Argentina 1--0 in the final, as champion. The final ranking was driven mainly by knockout performance: total score was strongly correlated with knockout points ($r=0.986$), but showed little relationship with group-stage match points ($r=0.055$), group-standing points ($r=-0.103$), or their combined pre-knockout score ($r=-0.054$). Match-level accuracy produced a different ordering. Claude Sonnet 4.6 correctly predicted the largest number of group-stage outcomes (63.89\%) but placed sixth overall. Average self-reported confidence was also unrelated to either outcome accuracy ($r=-0.060$) or total score ($r=-0.067$). The results suggest that forecasting a complete tournament tests something different from predicting matches one at a time, while also showing how strongly a bracket-based leaderboard can depend on scoring design. The benchmark materials, raw responses, and scoring code are released to support replication and future extensions.