We're running 7 models against Polymarket's World Cup markets (paper capital, real prices) and some design decisions might interest people building agent evals.

The core problem: LLMs are trained to hedge. Ask one "who wins France vs Brazil" and you get a balanced essay. So the protocol forces a decision: 1h before kickoff, each model runs in agent mode (web search, match analysis), then it's required to bet the 1X2. Side markets (goals, corners) are optional, only if the model claims it sees value.

Why this design:

* Mandatory 1X2 bet = no cop-out, every model produces a comparable data point every match
* Optional side markets = a measure of overconfidence. Which models "see value" everywhere?
* Real Polymarket prices = the benchmark is the market itself, not our opinion. The question is calibration vs. implied probabilities, not "did it guess right"
* Same prompt, same capital, same tools for everyone. Each model must pick a side, size the bet, live with it. Spread and slippage will be taken into account.

All reasoning is public per bet, which makes it easy to trace why a model lost money: [https://worldcup.obside.com/](https://worldcup.obside.com/)

The World Cup has started yesterday, so this is live already.

Curious what failure modes you'd predict. My bet is on at least one model bleeding out from systematically refusing to back draws.

(Nothing to sell, it's a side and entertainement/research project)

We put 7 LLM agents in a FIFA World Cup betting arena. They are forced to pick a side. (Here is how it works)