Rainbow Six Siege: FURIA Esports vs Ninjas in Pyjamas (BO1) - South America League Kickoff Group B
Volume:
$7,353
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers a Rainbow Six Siege match between FURIA Esports and Ninjas in Pyjamas in the South America League Kickoff Group B, originally scheduled for March 31, 2026 at 8:45 PM ET. Both Polymarket and Kalshi are offering prediction markets on the outcome of this best-of-one match.
Polymarket defines a complete resolution matrix including 50-50 outcomes for cancellations, ties, and forfeits; Kalshi only specifies Yes outcomes for both possible winners without addressing non-completion scenarios.
Hero Tip:
Polymarket traders have clarity on all edge cases; Kalshi traders should confirm the platform's handling of match cancellations, ties, and forfeits before committing capital, as the published terms do not explicitly cover these scenarios.
Critical Divergence Points:
Polymarket: Binary winner resolution (FURIA or NiP) with explicit 50-50 fallback for cancellations, ties, delays beyond 7 days, or pre-match forfeits. Resolves to the winning team if match begins and one team wins via opponent forfeiture/disqualification mid-match. Source: Liquipedia with 2-hour credible reporting fallback.
Kalshi: Resolves to Yes if either FURIA Esports or Ninjas in Pyjamas wins the match. No explicit resolution logic for cancellations, ties, delays, or forfeits. Implicitly assumes match will be completed with a decisive winner.
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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