This event group covers the outcome of a women's college basketball game between San Diego State Aztecs and Nevada Wolf Pack scheduled for February 14, 2026. Both platforms are predicting which team will win the matchup, with consistent binary resolution (one team wins or the other).
Unified Resolution Criteria (Consistent across platforms)
Both platforms resolve on the same binary outcome: San Diego State wins or Nevada wins, determined by final score including overtime, with consistent handling of postponements.
Primary resolution logic:
NCAA official game records and final score (https://www.ncaa.com/)
Core resolution logic:
If San Diego State Aztecs win the game, market resolves to San Diego State Aztecs (Polymarket) or Yes (Kalshi)
If Nevada Wolf Pack win the game, market resolves to Nevada Wolf Pack (Polymarket) or Yes (Kalshi)
Final score determination includes any overtime periods played
If game is postponed, both markets remain open until the game is completed
Edge cases & Clarifications:
Game Cancellation: Polymarket explicitly resolves 50-50 if game is canceled with no makeup game. Kalshi does not specify cancellation protocol, creating minor ambiguity.
Overtime: Both platforms confirm final score includes overtime periods, so no tie resolution needed.
Postponement: Both platforms keep markets open if game is postponed; resolution occurs only after game completion.
Timing:
Resolution occurs immediately after the conclusion of the game on February 14, 2026 (or rescheduled date if postponed), based on final score including overtime.
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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