Northern Colorado Bears vs. Portland State Vikings (W)
Volume:
$7,870
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers the outcome of a women's college basketball game between Northern Colorado Bears and Portland State Vikings scheduled for February 14, 2026 at 5:00 PM ET. Markets across Kalshi and Polymarket are betting on which team will win the matchup.
Kalshi's resolution logic contains a logical contradiction where both possible outcomes (Northern Colorado win or Portland State win) resolve to Yes, making the market fundamentally unresolvable. Polymarket uses standard binary winner-determination logic.
Hero Tip:
Do not trade on Kalshi's version of this market. The contradiction means Kalshi cannot settle this market correctly regardless of the game outcome. Trade only on Polymarket, which has coherent resolution logic: one team wins and resolves to that team's name, with explicit handling of postponements and cancellations.
Critical Divergence Points:
Kalshi: Both outcomes (Northern Colorado win OR Portland State win) are mapped to Yes resolution. This creates a logical impossibility where the market cannot distinguish between the two teams. Quote: If Northern Colorado wins resolve Yes; If Portland St wins resolve Yes.
Polymarket: Standard binary outcome: Northern Colorado win resolves to Northern Colorado Bears; Portland State win resolves to Portland State Vikings. Includes explicit postponement (market stays open) and cancellation (50-50 split) protocols.
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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