Texas A&M-Corpus Christi Islanders vs. Southeastern Louisiana Lions (W)
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$6,234
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24h
7d
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Description
This event group covers the women's college basketball game between Texas A&M-Corpus Christi Islanders and Southeastern Louisiana Lions scheduled for February 19, 2026 at 7:00 PM ET. Markets on Polymarket and Kalshi are tracking the outcome of this matchup, with resolution based on the final score including overtime.
Kalshi's resolution logic contains a logical contradiction where both possible game outcomes (Texas A&M-Corpus Christi win OR Southeastern Louisiana win) resolve to the same state (Yes), making the market fundamentally unresolvable as a binary prediction instrument.
Hero Tip:
Trade only on Polymarket for this matchup. Kalshi's market structure appears to have a documentation or configuration error. Do not risk capital on Kalshi until the platform clarifies whether this is intended as a Yes/No market on a single team or if the resolution criteria have been corrected.
Critical Divergence Points:
Polymarket: Clear winner-take-all binary structure. Islanders win resolves to 'Texas A&M-Corpus Christi Islanders'; Lions win resolves to 'Southeastern Louisiana Lions'. Handles postponement (market stays open) and cancellation (50-50 split). Resolution based on final score including overtime.
Kalshi: Contradictory dual-affirmative resolution: both Texas A&M-Corpus Christi victory AND Southeastern Louisiana victory are stated to resolve to Yes. This creates logical impossibility—only one team can win, yet both outcomes map to identical resolution state.
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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