This event group covers the women's college basketball game between Texas A&M Aggies and Tennessee Volunteers scheduled for February 19, 2026 at 6:30 PM ET. Markets on Polymarket and Kalshi are tracking the outcome of this single game, with resolution tied to the final score including overtime.
Kalshi's resolution criteria contains a logical contradiction: both Texas A&M win and Tennessee win are stated to resolve to Yes, which is impossible for a mutually exclusive binary event. This makes the Kalshi market fundamentally unresolvable as written.
Hero Tip:
This is a critical data integrity failure on Kalshi. Both outcomes cannot resolve to the same value in a binary game outcome market. Request immediate clarification from Kalshi whether the second outcome should resolve to No, or if the rule applies only to a specific subset (e.g., margin of victory). Avoid trading Kalshi until the contradiction is resolved.
Critical Divergence Points:
Polymarket: Binary outcome market with mutually exclusive resolution. Texas A&M victory resolves to Texas A&M Aggies; Tennessee victory resolves to Tennessee Volunteers. Includes edge case handling: postponement keeps market open until completion; cancellation with no makeup resolves 50-50. Resolution based on final score including overtime.
Kalshi: Logically contradictory resolution rule stating both Texas A&M win and Tennessee win resolve to Yes. This creates an impossible scenario where the market cannot distinguish between the two mutually exclusive outcomes. No edge case guidance provided.
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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