New Mexico State Aggies vs. Louisiana Tech Bulldogs (W)
Volume:
$58,232
Markets
Outcome
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Volume
24h
7d
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Description
This event group covers a women's college basketball game between New Mexico State Aggies and Louisiana Tech Bulldogs scheduled for March 5, 2026 at 6:30 PM ET. Markets on Polymarket and Kalshi are betting on the outcome of this single game, with resolution based on the final score including overtime.
Kalshi's resolution logic contains a logical contradiction where both possible game outcomes (Louisiana Tech win and New Mexico State win) are mapped to the same resolution value (Yes), making the market fundamentally unresolvable and creating a data integrity failure.
Hero Tip:
Avoid trading on Kalshi until the resolution logic is corrected. The market as written guarantees a Yes resolution regardless of game outcome, which violates basic binary market structure. Use Polymarket as your authoritative source. Contact Kalshi support to confirm whether the second condition should resolve to No or if this is an error in the market specification.
Critical Divergence Points:
Polymarket: Standard binary outcome market: New Mexico State win resolves to 'New Mexico State Aggies', Louisiana Tech win resolves to 'Louisiana Tech Bulldogs'. Postponement keeps market open; cancellation without makeup resolves 50-50. Resolution based on final score including overtime.
Kalshi: Contradictory resolution logic: both 'If Louisiana Tech wins' and 'If New Mexico St. wins' resolve to Yes, creating logical impossibility. No resolution path for No outcome exists.
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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