FIELD: A Prospective Cross-Sport Benchmark for Language-Model Forecasting and Information Use

Forecasting unresolved events requires models to use evolving evidence, express uncertainty, and update beliefs, yet how observable information use and belief updating relate to forecast quality remains unclear. Sports provide a natural testbed for studying these relationships, with scheduled events, verifiable outcomes, and meaningful intermediate states. We introduce FIELD, a dynamic, prospective cross-sport benchmark for early and near-event forecasting of how sporting events unfold and end. FIELD freezes forecasts and observable workflow records before each event, enabling assessment of both predictive quality and information use. We evaluate seven frontier language-model systems on 232 football, basketball, and baseball events. Across sports, forecast performance is strongest for final Outcome, while richer score distributions and trajectories remain harder. We further find that better trajectory forecasts are associated with better outcome forecasts; observable workflow does not reliably track forecast quality; and later forecasts mostly refine probabilities rather than change the leading prediction. A separate STEP study prospectively tests whether these findings can be translated into a structured forecasting procedure, with effects varying substantially across events.