Appalachian State Mountaineers vs. Coastal Carolina Chanticleers (W)
Volume:
$861,212
Markets
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24h
7d
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Description
This event group covers a Women's College Basketball (WBB) matchup between Appalachian State Mountaineers and Coastal Carolina Chanticleers scheduled for February 18, 2026 at 11:00 AM ET. The markets resolve based on which team wins the game, with provisions for postponement, cancellation, and overtime.
Kalshi market contains a logical contradiction where both possible game outcomes (Appalachian St. win OR Coastal Carolina win) are mapped to the same resolution value (Yes), making the market fundamentally unresolvable and creating data integrity failure.
Hero Tip:
This is a critical market design flaw on Kalshi. The market cannot distinguish between the two mutually exclusive outcomes. Avoid trading on Kalshi. Use Polymarket exclusively, which correctly implements binary winner-take-all resolution.
Critical Divergence Points:
Polymarket: Proper binary structure: Appalachian State victory resolves to 'Appalachian State Mountaineers'; Coastal Carolina victory resolves to 'Coastal Carolina Chanticleers'. Includes edge case handling for postponement (market stays open) and cancellation without makeup (50-50 split). Resolution based on final score including overtime.
Kalshi: Defective conditional logic: Both 'If Appalachian St. wins' and 'If Coastal Carolina wins' are stated to resolve to Yes. This creates logical impossibility since exactly one team must win, but both outcomes map to identical resolution. Market is unresolvable as written.
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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