Kalshi's three markets create a logical contradiction where every possible outcome (Juarez win, Tijuana win, or tie) resolves to YES, making the market group fundamentally unresolvable. Polymarket correctly structures three mutually exclusive binary markets (Juarez win, Tijuana win, draw) where exactly one resolves YES. This is a data integrity failure on Kalshi's platform.
Hero Tip:
Do not trade on Kalshi's markets in this group — they contain a critical logical error that makes settlement impossible. All three Kalshi markets will resolve YES regardless of the match outcome, which violates basic market design. Trade only on Polymarket, where the three markets are properly structured as mutually exclusive outcomes.
Critical Divergence Points:
Kalshi: Outlier: Kalshi's three markets each resolve YES for different outcomes (Juarez win, Tijuana win, or tie), meaning all three markets will resolve YES simultaneously after the match concludes. This creates a logical impossibility. Key quote: 'If Juarez wins... then the market resolves to Yes. If Tijuana... wins... then the market resolves to Yes. If Tie wins... then the market resolves to Yes.'
Polymarket: Aligned with sound market design: Polymarket structures three mutually exclusive binary markets where exactly one outcome can occur and exactly one market resolves YES. The Juarez win, Tijuana win, and draw markets are properly exclusive. Key quote: 'If FC Juárez wins, this market will resolve to Yes. Otherwise, this market will resolve to No' — applied separately to each outcome market.
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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