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Source note4 min read

WorldCupArena: Fine-Grained Evaluation of Language Models and Deep-Research Agents on Football Forecasting

Source

arXiv

arxiv.org/abs/2607.18084v1

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Source

Plain-Language Brief

The paper introduces WorldCupArena, a dynamic benchmark for evaluating language models and deep-research agents in predicting football matches. The 2026 FIFA World Cup was its first evaluation, with systems receiving either common evidence or searching for information themselves before making predictions. The accuracy of predictions, including the result, score, likely players, events, match statistics, and competition outcome were compared with recorded results.

What the Document Says

  • WorldCupArena is a dynamic benchmark for evaluating language models and deep-research agents in football forecasting.
  • The 2026 FIFA World Cup was the first evaluation, but it can be reused for future leagues and cups.
  • Before each match, models receive either common evidence or search for information themselves, then predict the result, score, likely players, events, match statistics, and competition outcome.
  • After the match, these predictions are compared with the recorded results.
  • Metrics used include result accuracy, exact-score accuracy, a scoreline score, and other prediction task scores.
  • Across 104 matches and 13 systems, models with similar result accuracy differ more clearly on detailed predictions.
  • Compared with betting-market and human-fan baselines, the best system shows only small gains in result and exact-score accuracy, but a clearer gain in Scoreline.

Key Concepts Mentioned

  • WorldCupArena
  • football forecasting
  • language models
  • deep-research agents
  • 2026 FIFA World Cup
  • match predictions
  • result accuracy
  • exact-score accuracy
  • Scoreline

Claims Made by the Document

  • WorldCupArena is a useful benchmark for evaluating language models and deep-research agents in football forecasting.
  • The best system shows a clearer gain in Scoreline compared to betting-market and human-fan baselines.

Evidence Used

  • Predicting a football match before kickoff requires more than knowing past results. A model must use changing information and make a clear prediction before the answer is available.
  • We present WorldCupArena, a dynamic benchmark for language models and deep-research agents. The 2026 FIFA World Cup is its first evaluation, and the same process can be reused for future leagues and cups.
  • Before each match, a model either receives a common evidence package or searches for information itself.
  • It predicts the result and score, likely players and events, match statistics, and the outcome of the competition.
  • After the match, these predictions are compared with the recorded result.
  • We report result accuracy, exact-score accuracy, and a scoreline score that gives some credit when a predicted score is close but not exact, together with scores for the other prediction tasks.
  • Across 104 matches and 13 systems, models with similar result accuracy differ more clearly on detailed predictions.
  • Compared with betting-market and human-fan baselines, the best system shows only small gains in result and exact-score accuracy, but a clearer gain in Scoreline.

Methods / Approach

  • Dynamic benchmarking
  • Football match prediction
  • Evaluation of language models and deep-research agents

Limitations Stated by the Authors

  • UNKNOWN: The text does not provide information on potential limitations mentioned by the authors.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The data provided is for research purposes only, and may not fully represent real-world performance.
  • The evaluation may be influenced by the quality and availability of information during the match prediction process.

Open Questions

  • What are the specific details of how models search for information themselves?
  • How does the benchmark handle potential discrepancies between the predicted and recorded results?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The paper provides a general idea of how the models receive information, but it does not specify the exact details or methodology.
  • The comparison of predictions with recorded results likely involves some form of error handling or adjustment.

Raw Abstract / Excerpt

Predicting a football match before kickoff requires more than knowing past results: a model must use changing information and make a clear prediction before the answer is available. We present WorldCupArena, a dynamic benchmark for language models and deep-research agents. The 2026 FIFA World Cup is its first evaluation, and the same process can be reused for future leagues and cups. Before each match, a model either receives a common evidence package or searches for information itself. It predicts the result and score, likely players and events, match statistics, and the outcome of the competition. After the match, these predictions are compared with the recorded result. We report result accuracy, exact-score accuracy, and a scoreline score that gives some credit when a predicted score is close but not exact, together with scores for the other prediction tasks. Across 104 matches and 13 systems, models with similar result accuracy differ more clearly on detailed predictions. Compared with betting-market and human-fan baselines, the best system shows only small gains in result and exact-score accuracy, but a clearer gain in Scoreline. New schedules can be added as they begin, allowing the benchmark to evaluate future models without using outcomes that are already known. Code, prompts, predictions, and evaluation scripts are open sourced at https://github.com/wzk1015/WorldCupArena.