Tarleton State Texans vs. Utah Valley Wolverines (W)
Volume:
$40,782
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers a women's college basketball game between Tarleton State Texans and Utah Valley Wolverines scheduled for March 12, 2026 at 3:00 PM ET. Markets on Polymarket and Kalshi are pricing the outcome of this single game, with resolution based on the final score including overtime.
Kalshi's resolution logic contains a logical contradiction stating both Utah Valley winning and Tarleton State winning both resolve to Yes, which is impossible in a binary sporting event. This makes Kalshi's market fundamentally unresolvable as written.
Hero Tip:
Kalshi's resolution text is malformed. Interpret it as a standard binary: Yes if Utah Valley wins, No if Tarleton State wins. Verify with Kalshi support before trading. Polymarket provides clear, standard binary logic and should be treated as the reliable reference.
Critical Divergence Points:
Polymarket: Standard binary winner-take-all structure. Resolves to the winning team's name based on final score including overtime. Cancellation without makeup resolves 50-50. Clear and logically sound.
Kalshi: Contradictory specification: 'If Utah Valley wins...resolves to Yes' AND 'If Tarleton St. wins...resolves to Yes.' Both outcomes cannot simultaneously be Yes in a binary event. This is a critical data integrity failure.
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.
Follow the signals, not the noise
Get insights on market conviction, notable shifts, and what the data is quietly signaling.