Sacred Heart Pioneers vs. Saint Peter's Peacocks (W)
Volume:
$1,712
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers a women's college basketball game between Sacred Heart Pioneers and Saint Peter's Peacocks scheduled for February 14, 2026 at 2:00 PM ET. The markets track the outcome of this single game, with resolution based on the final score including overtime.
Kalshi market contains a logical contradiction: both possible outcomes (Saint Peter's win and Sacred Heart win) are specified to resolve to Yes, making the market fundamentally unresolvable. Polymarket uses a sound categorical resolution model.
Hero Tip:
Do not trade Kalshi until the platform clarifies whether the Yes outcome applies to only one team or if this is a data entry error. Polymarket's market is resolvable and should be treated as authoritative for this event.
Critical Divergence Points:
Kalshi: Market states both Saint Peter's win and Sacred Heart win resolve to Yes. This is a logical impossibility in a binary market. Quote: 'If Saint Peter's wins...then the market resolves to Yes. If Sacred Heart wins...then the market resolves to Yes.'
Polymarket: Market resolves categorically to winner name (Sacred Heart Pioneers or Saint Peter's Peacocks). Postponement keeps market open; cancellation without makeup resolves 50-50. Quote: 'If the Sacred Heart Pioneers win, the market will resolve to Sacred Heart Pioneers. If the Saint Peter's Peacocks win, the market will resolve to Saint Peter's Peacocks.'
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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