This event group covers the NBA game between the New York Knicks and Cleveland Cavaliers scheduled for February 24, 2026 at 7:30 PM ET. Markets span moneyline, spreads, totals, player props (points, rebounds, assists), and first-half variants across Polymarket and Kalshi platforms.
Kalshi markets resolve exclusively on second-half regulation outcomes, while Polymarket markets resolve on full game including overtime. This creates logically incompatible resolution paths for the same underlying event.
Hero Tip:
These are fundamentally different markets despite sharing teams and date. Kalshi second-half markets cannot be directly arbitraged against Polymarket full-game markets. Understand your platform scope before trading. If you want full-game exposure, use Polymarket. If you specifically want second-half action, Kalshi is the only option.
Critical Divergence Points:
Kalshi: Resolves YES if any outcome occurs in the second half of regulation time only (tie, Cleveland win, or New York win). Quote: 'If Tie is the result of the second half of regulation time... then the market resolves to Yes.' This creates a tautological YES resolution regardless of outcome, making the market a pure second-half occurrence bet rather than a directional bet.
Polymarket: Resolves on final score including all overtime periods. Moneyline resolves Knicks or Cavaliers based on full-game winner. Spreads and totals include overtime. Quote: 'The result will be determined based on the final score including any overtime periods.' This is standard full-game resolution.
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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