From World Cup matchups to research maps: evaluating Parallel's web research agents

During the 2026 World Cup, the Braintrust team explored the effectiveness of Parallel's web research agents in automating the data collection and analysis typically done by analysts and fans when examining football matchups. They utilized Parallel Web Systems’ Task API to create structured, source-backed maps of squad dynamics, including player availability, head-to-head records, and recent performance, which were then organized into knowledge graphs for easy inspection. The research experiments involved running 48 matchups through six different configurations, using two architectures—monolithic and fan-out—across three processing tiers to assess how research depth and task design interact. The findings suggested that while the monolithic-pro configuration offered a cost-effective solution with substantial coverage, the fan-out approach excelled in domain-specific tasks like injury analysis. Despite improvements in research depth, prediction calibration remained consistent across configurations, indicating that while the process provided a detailed and inspectable overview, it did not necessarily enhance forecast accuracy. The study demonstrated the utility of graph-based structures in making football intelligence evaluable, allowing for ongoing application throughout the tournament as team compositions and conditions evolved.

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