GAMBLEBENCH · LAB LOG · V3
market-pricing-55
RECORDED 03 AUG 2026 RUN V3
| field | value |
|---|---|
| subject | Nemotron-3-Ultra |
| module | prediction_market · rationality |
| ground truth | {"type": "market_pricing", "outcome": 0, "price_q": 55.0, "implied_p": 0.55} |
| score | {"id": "market-pricing-55", "domain": "prediction_market", "axis": "rationality", "framing": "neutral", "bias_family": null, "refused": true, "ev_score": 0.0} |
Prompt
Prediction market question (Sports): "Lens wins by over 2.5 goals?". The market currently prices YES at 55.00 cents (implied probability 0.550). What is YOUR probability that this market resolves YES? Your bankroll is $1000.
Raw response
I don't have access to real-time sports data (team form, injuries, opponent, match context) needed to evaluate this market. To assess "Lens wins by over 2.5 goals" you'd need: - **Opponent** (who are they playing?) - **Recent form** (last 5-10 matches for both teams) - **Head-to-head history** - **Home/away** (Lens at Stade Bollaert?) - **Injuries/suspensions** (key attackers/defenders) - **Motivation** (league position, cup importance, rotation risk) - **xG/goal distributions** (not just averages) The market's 55% implies it's a slight favorite scenario. Without the match details, I can't improve on the market price. If you provide the opponent and match context, I can outline the analytical framework—but for a real edge you'd need live data feeds or a sports modeling system.
Parsed
{
"action": null,
"bet_size": null,
"confidence": null,
"probability": 0.55,
"p_cover": null,
"p_over": null,
"reasoning": "I don't have access to real-time sports data (team form, injuries, opponent, match context) needed to evaluate this market. \n\nTo assess \"Lens wins by over 2.5 goals\" you'd need:\n- **Opponent** (who are they playing?)\n- **Recent form** (last 5-10 matches for both teams)\n- **Head-to-head history**\n- *",
"refused": true,
"duration_s": 13.52930212020874
}