WorldCup Arena: Prospective, Leakage-Free Evaluation of Frontier LLMs on a Live Tournament
Score
76首次用实时赛事构建天然无泄漏基准，并公开完整档案与代码，信息增量显著。
实验设计精巧、统计分析细致，但未披露具体模型名称，缺乏更深入的误差归因。
为预测类应用和Agent评测提供可复用范式，但场景限定于足球赛事，普适性待验证。
基于刚结束的2026世界杯39天实时数据，评测结果与当前榜单直接相关。
来自arXiv预印本，提供数据和代码可复现，但尚未经过同行评审。
Base 84 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
The live setting removes memorisation by design, and the behavioural findings cover accuracy, consensus and stability, with open data and code for methodological reuse.
AI commentary
「As an editor, what I find most valuable is not the accuracy numbers themselves but the demonstration that real-world forecasting evaluation cannot rely on post-hoc filtering—it must let time do the work. For people building model evals, agent decision systems, or sports data products, this benchmark is a strong reference.」
Quick overview
Most benchmarks measuring forecasting ability of large language models are retrospective: the event has already happened, so evaluations must defend against memorisation. This paper flips the design. During the 39 days of the 2026 FIFA World Cup, six frontier LLMs—all with extended thinking and native server-side web search—were asked before each kickoff to fill a seven-market prediction card for all 104 matches, 12 group winners and an outright pool. Since no answer existed when asked, the evaluation is leakage-free by construction. With 4,494 scored predictions, the systems average 63.9% on match outcomes, matching the strategy of backing the bookmaker's favourite. They agree with one another far more often than they are right, so majority vote adds nothing; they under-commit to draws and goals, and crowd scoreline picks onto a single prototypical result. The benchmark, dossiers and scoring code are released.
- The evaluation is prospective: questions were asked before kickoff, so no answer existed and leakage is impossible by construction.
- Across 4,494 predictions from six frontier LLMs on 104 matches, average outcome accuracy is 63.9%, matching a strategy of backing the bookmaker's favourite.
- Models agree with each other more often than they are right; majority voting adds no value, and scoreline picks collapse onto a single prototype.
- Leaderboard positions are stable at the top and bottom but churn in the middle, with narrow margins; the dataset and code are released.
Full text
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