CA Paranaense and EC Vitória will compete in a Brasileiro Serie A match on April 26, 2026. The outcome will be determined by the final score after 90 minutes of regular play plus stoppage time, with no extra time or penalties considered. Resolution will be based on official CBF statistics or credible consensus reporting if official data is unavailable within 2 hours of match conclusion.
Kalshi market resolves YES for ALL possible outcomes (win, loss, or draw), making it logically incoherent and fundamentally unresolvable as a predictive instrument. Polymarket markets are mutually exclusive and collectively exhaustive.
Hero Tip:
Do not trade the Kalshi market. It is a logical trap—it will resolve YES regardless of the match result, rendering it worthless for prediction or hedging. Polymarket offers the only coherent markets for this event.
Critical Divergence Points:
Polymarket: Three mutually exclusive markets covering all outcomes: Paranaense win (YES/NO), Draw (YES/NO), Vitória win (YES/NO). Exactly one resolves YES. Resolution source: official CBF statistics or credible reporting consensus within 2 hours of match conclusion. Cancellation with no makeup resolves draw market to YES, others to NO.
Kalshi: Single market with three resolution conditions: 'If Tie wins... then YES. If Paranaense wins... then YES. If Vitoria wins... then YES.' This creates a logical contradiction—the market resolves YES for every possible match outcome, making it a certainty bet rather than a prediction market. No cancellation or postponement rules provided.
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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