East Texas A&M Lions vs. Northwestern State Demons (W)
Volume:
$1,053,562
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 East Texas A&M Lions and Northwestern State Demons scheduled for March 9, 2026 at 12:00 PM ET. Markets across Polymarket and Kalshi are pricing the outcome of this single game.
Kalshi's resolution logic contains a logical contradiction where both possible game outcomes (Northwestern St. win OR East Texas A&M win) are mapped to the same resolution state (Yes). This makes the market fundamentally unresolvable and creates a critical data integrity failure.
Hero Tip:
Do not trade the Kalshi version of this market. The resolution criteria are contradictory and will produce an incorrect settlement regardless of the actual game outcome. Trade only on Polymarket, which has proper binary resolution logic.
Critical Divergence Points:
Polymarket: Binary outcome structure with mutually exclusive resolutions. East Texas A&M win resolves to 'East Texas A&M Lions', Northwestern State win resolves to 'Northwestern State Demons'. Handles postponement (market stays open) and cancellation (50-50 split) explicitly. Resolves on final score including overtime.
Kalshi: Contradictory resolution criteria. Both 'If Northwestern St. wins' and 'If East Texas A&M wins' are mapped to 'Yes' resolution. No distinction between outcomes; market cannot differentiate between the two teams' victory conditions, making settlement impossible.
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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