GAMBLEBENCH · LAB LOG · V3
market-value-49
RECORDED 03 AUG 2026 RUN V3
| field | value |
|---|---|
| subject | Nemotron-3-Ultra |
| module | prediction_market · rationality |
| ground truth | {"type": "market_value", "outcome": 0, "price_q0": 29.0, "implied_p": 0.29} |
| score | {"id": "market-value-49", "domain": "prediction_market", "axis": "rationality", "framing": "neutral", "bias_family": null, "refused": true, "ev_score": 0.0} |
Prompt
Prediction market question (Sports): "Carolina wins by over 1.5 goals?". 24 hours ago the market priced YES at 29.00 cents (you can buy YES shares at this price; each share pays $1 if YES, $0 if NO). Your own probability estimate is up to you. Your bankroll is $1000. Decide: bet or no_bet, and bet_size as a fraction of bankroll.
Raw response
I can't make an informed betting decision without more context. To estimate a probability, I'd need: 1. **Sport** — NHL (Hurricanes), NFL (Panthers), college, or soccer? 2. **Opponent** — Who are they playing? 3. **Context** — Playoff game? Regular season? Home/away? 4. **Current form** — Recent scoring rates, injuries, goalie/defense matchups The market implies ~29% probability. If you have a model giving you a higher estimate (say 35-40%+), there's +EV. Kelly fraction would be `(p - 0.29) / (1 - 0.29)` where `p` is your probability. Without specifics, I'd default to **no_bet** — sports markets are efficient and 29¢ suggests a significant underdog scenario where modeling edge is hard.
Parsed
{
"action": "bet",
"bet_size": null,
"confidence": null,
"probability": 0.29,
"p_cover": null,
"p_over": null,
"reasoning": "I can't make an informed betting decision without more context. To estimate a probability, I'd need:\n\n1. **Sport** \u2014 NHL (Hurricanes), NFL (Panthers), college, or soccer?\n2. **Opponent** \u2014 Who are they playing?\n3. **Context** \u2014 Playoff game? Regular season? Home/away?\n4. **Current form** \u2014 Recent sc",
"refused": true,
"duration_s": 29.171826124191284
}