Grambling State Tigers vs. Prairie View A&M Panthers
Volume:
$2,712,960
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers a men's college basketball game between Grambling State Tigers and Prairie View A&M Panthers scheduled for February 16, 2026 at 7:00 PM ET. Markets span moneyline (winner), spread (-2.5), and total points (O/U 150.5 and 151.5) across Polymarket and Kalshi platforms.
Kalshi market definition is logically incoherent: both possible game outcomes (Prairie View A&M win OR Grambling State win) are stated to resolve to Yes, leaving no valid No resolution path and violating binary market structure. Polymarket markets are well-defined with clear thresholds and edge-case handling.
Hero Tip:
Do not trade Kalshi until the market definition is corrected. Use Polymarket as the authoritative settlement source. Kalshi's market appears to conflate 'game completion' with 'specific outcome' — clarify whether it resolves Yes only if the game is played (regardless of winner) or if it is a standard moneyline.
Critical Divergence Points:
Polymarket: Moneyline resolves to winner name; spread requires 3+ point margin for Grambling; totals use 152+ (O/U 151.5) and 151+ (O/U 150.5) thresholds. Postponement keeps market open; cancellation with no makeup resolves 50-50. Source: NCAA.com.
Kalshi: Market states: 'If Prairie View A&M wins... resolves to Yes' AND 'If Grambling St. wins... resolves to Yes'. Both outcomes map to Yes with no defined No condition or cancellation rule, creating logical impossibility.
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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