SETTLED.

Prediction-market claims, checked against resolution data.

Buying near-certain favorites before resolution

The certainty premium is real: 90–98¢ favorites hours from resolution are slightly underpriced. It pays in cents and takes back in dollars — one correlated upset can erase forty-five wins.

The hypothesisHolders of near-certain positions sell early to free capital, and gamblers prefer the lottery side — so favorites at 90–98¢ with hours to resolution trade a hair below fair. Buy them, collect the last cents, recycle capital fast.

The test

A family of live strategies bought high-probability favorites near expiry across sports, esports, and general markets, at small real size with fill confirmation, every entry recorded at its executable ask. Variants tested tighter windows, whale confirmation, volume floors, and sustained-price filters. This is the same mechanism behind every "steel nerves" post ever written — see our claim-check on one — tested with our own money instead of a screenshot.

The result

The premium exists: win rates ran slightly above what entry prices implied, and the esports lane's canary opened 24W–3L. Then the structure of the risk showed itself, twice. One ordinary day, a favorite that held 90–98¢ for hours got re-bought every scan cycle until 13 real positions sat on a single match — which lost, along with two other games, for −$162 and a tripped circuit breaker in one afternoon. Later, a single esports upset killed four positions at once (match winner ×2, map winner ×2 — the same event wearing four market costumes) and flipped the lane's lifetime net negative. At ~92¢ a win earns 8¢ and a loss costs 92: one correlated miss erases eleven to forty-five wins, and correlation is everywhere because markets multiply faster than events do.

Conclusion

Confirmed as a premium, condemned as a free lunch. The strategy is insurance underwriting: small premiums collected fast, catastrophic correlated claims paid rarely. It is only survivable with hard per-event position caps, whole-book exposure limits, and a daily loss breaker — none of which appear in any viral post about it. Sized wrong, this strategy doesn't lose slowly; it loses all at once.