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Source note3 min read

LLM-SoccerArena: Benchmarking LLMs on Real-World Predictions in Sports

Source

arXiv

arxiv.org/abs/2607.24573v1

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Plain-Language Brief

LLM-SoccerArena is a new platform that allows for the prospective evaluation of large language models' abilities to forecast real-world sports events before their outcomes are known. The platform provides a live benchmark protocol, an open-source platform, and a factorial design with four dimensions: model version, information access, prompting strategy, and forecast horizon. In a demonstration at the 2026 FIFA World Cup, seven LLMs made forecasts for all matches and tournament-related questions. The results show that models with web access outperform those without by a small margin.

What the Document Says

  • LLM-SoccerArena is a prospective live benchmark platform for evaluating large language models' ability to forecast real-world sports events before their outcomes are known.
  • The platform provides a protocol, an open-source platform, and a factorial design with four dimensions: model version, information access, prompting strategy, and forecast horizon.
  • In a demonstration at the 2026 FIFA World Cup, seven LLMs made forecasts for all matches and tournament-related questions.
  • Models with web access outperform those without by a small margin (improvement of 0.023 in Brier score).

Key Concepts Mentioned

  • Large Language Models
  • LLM-SoccerArena
  • Prospective live benchmark
  • 2026 FIFA World Cup
  • Brier score

Claims Made by the Document

  • Existing benchmarks cannot test how information is synthesized by LLMs to predict future events under uncertainty.
  • LLM-SoccerArena provides evidence about the forecasting performance of state-of-the-art LLMs.

Evidence Used

  • The text states that existing benchmarks are typically static and retrospective, which makes them unable to test how information is synthesized by LLMs.
  • The text mentions a detailed analysis of model performance across information access, prompting strategy, and forecast horizon.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Large language models (LLMs) increasingly support decisions about uncertain future events, yet evaluating their ability to forecast real-world outcomes remains difficult. In particular, existing benchmarks are typically static and retrospective, and therefore cannot test how information is synthesized by LLMs to predict future events under uncertainty. We introduce LLM-SoccerArena (https://llm-soccerarena.com), a prospective live benchmark that evaluates how well LLMs forecast real-world sports events before the outcomes are known. LLM-SoccerArena provides (1) a prospective live benchmark protocol, (2) a public open-source platform, and (3) a factorial benchmark design together with tournament-related questions (e.g., which team will win). LLM-SoccerArena automatically records timestamped, schema-validated forecasts of unresolved events, together with prompts, model versions, tool traces, and costs. The factorial design varies along four dimensions: (1) model version (e.g., GPT-5.5, Claude Opus 4.8); (2) information access; (3) prompting strategy, and (4) forecast horizon. We demonstrate LLM-SoccerArena through a large-scale evaluation of the 2026 FIFA World Cup, in which seven LLMs generated forecasts for all 104 matches and 15 tournament-related questions. We provide a detailed analysis of model performance across information access, prompting strategy, and forecast horizon. As a result, LLM-SoccerArena provides new evidence about the forecasting performance of state-of-the-art LLMs. For example, LLMs with web access outperform those without, but only by a small margin (i.e., a 0.023 improvement in Brier score). Overall, LLM-SoccerArena provides a flexible, open-source platform for prospective benchmarking of unresolved events. LLM-SoccerArena will be continuously updated, and can be directly applied to future national and international tournaments and league competitions.