LoL: LEO vs Ruddy Esports (BO1) - NLC Regular Season
Volume:
$29,789
Markets
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This market group covers a single League of Legends Best-of-1 match between LEO and Ruddy Esports scheduled for February 26, 2026, at 3:00 PM ET in the NLC Regular Season. The outcome is a binary event: either LEO wins or Ruddy Esports wins, with specific handling for cancellations, forfeits, and delays.
Kalshi's resolution logic is logically contradictory (both outcomes map to Yes), making the market fundamentally unresolvable. Polymarket provides a complete, coherent binary resolution framework with explicit edge-case rules and a named primary source.
Hero Tip:
Avoid Kalshi. Use Polymarket exclusively for this event. Confirm match scheduling and monitor gol.gg starting 2 hours before the scheduled 3:00 PM ET start on Feb 26, 2026. If results are not published within 2 hours post-conclusion, watch for credible reporting (video evidence preferred) to anticipate resolution.
Critical Divergence Points:
Kalshi: Both 'Ruddy Esports wins' and 'LEO wins' resolve to Yes. No source specified. No edge-case handling for cancellations, forfeits, or delays. Market structure is logically impossible for a binary outcome.
Polymarket: Resolves to LEO if LEO wins, Ruddy Esports if Ruddy wins. Cancellations, ties, delays beyond 7 days, and pre-match forfeits resolve to 50-50. In-match forfeits resolve to the winning team. Primary source: gol.gg (2-hour window); fallback to credible reporting with video evidence.
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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