This event is for the WBB game between Notre Dame Fighting Irish and Ohio State Buckeyes on March 23 at 4:00 PM ET.
If the game is postponed, this market will remain open until the game has been completed.
If the game is canceled entirely, with no make-up game, this market will resolve 50-50.
Kalshi resolves YES for either team winning (logical contradiction making the market unresolvable), while Polymarket correctly resolves to the actual winner. Kalshi's rules state both 'If Ohio St. wins... resolves to Yes' AND 'If Notre Dame wins... resolves to Yes', creating a market that cannot distinguish between outcomes.
Hero Tip:
Avoid Kalshi entirely for this event — the market is fundamentally broken and will resolve YES regardless of the actual game result. Trade only on Polymarket, which correctly resolves to 'Notre Dame Fighting Irish' or 'Ohio State Buckeyes' based on the final score.
Critical Divergence Points:
Polymarket:
Outlier (correct logic): Resolves to the actual winner — 'Notre Dame Fighting Irish' if Notre Dame wins, 'Ohio State Buckeyes' if Ohio State wins, based on final score including overtime. 'If the Notre Dame Fighting Irish win, the market will resolve to Notre Dame Fighting Irish. If the Ohio State Buckeyes win, the market will resolve to Ohio State Buckeyes.'
Kalshi:
Outlier (data integrity failure): Contains a logical contradiction — both 'If Ohio St. wins... resolves to Yes' AND 'If Notre Dame wins... resolves to Yes', meaning the market resolves YES for every possible outcome and cannot differentiate between winners.
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.