TOTAL VOLUME:
$54.6b
24H VOL:
$283,259,638
OPEN INTEREST:
$1,235,549,422
505,521
Markets across
13,661
events
MATCHED EVENTS:
1,892
PLATFORM COVERAGE:
4
Polymarket:
51%
VS.
Kalshi:
49%
Binghamton Bearcats vs. Albany Great Danes (W)
Volume:
$34,155
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers the women's college basketball game between Binghamton Bearcats and Albany Great Danes scheduled for February 14, 2026 at 7:00 PM ET. The markets predict which team will win the matchup, with resolution based on the final score including overtime.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi's resolution rules contain a logical contradiction where both possible game outcomes (Binghamton win and Albany win) are stated to resolve to Yes, making the market fundamentally unresolvable and non-predictive.Hero Tip:
Do not trade on Kalshi until this contradiction is resolved. Contact Kalshi support to confirm whether this is a drafting error. Polymarket offers a clear, standard binary structure and should be preferred for this event.Critical Divergence Points:
- Kalshi: Both outcomes resolve to Yes. States: If Binghamton wins, resolves Yes. If Albany wins, resolves Yes. This creates a logical impossibility where no outcome can resolve to No.
- Polymarket: Standard binary resolution. Binghamton win resolves to Binghamton Bearcats, Albany win resolves to Albany Great Danes. Includes explicit edge case handling: postponement keeps market open, cancellation without makeup resolves 50-50.
Our PredictionHero Resolution Divergence Alerts (RDA) are there to help users identify potential differences across platforms. They do not replace or supersede the official rules and description of any prediction market. Users are solely responsible for reviewing and understanding the applicable rules and resolution criteria before placing any trade or bet. If you notice a potential inconsistency, discrepancy, or error in an alert, please report it to our team so we can review and improve the accuracy of our data.
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