TOTAL VOLUME:
$61.7b
24H VOL:
$229,178,007
24H TRANSACTIONS:
600,069,390
OPEN INTEREST:
$1,336,753,738
577,196
Markets across
14,485
events
MATCHED EVENTS:
4,077
PLATFORM COVERAGE:
4
Polymarket:
50%
VS.
Kalshi:
50%
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers a women's college basketball game between Siena Saints and Rider Broncs scheduled for February 26, 2026 at 6:00 PM ET. Markets on Polymarket and Kalshi are tracking the binary outcome of which team wins the game.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi's resolution criteria contain a logical contradiction: both a Rider win and a Siena win are stated to resolve to Yes, making the market fundamentally unresolvable as a binary outcome market. Polymarket provides coherent binary logic.Hero Tip:
Treat Kalshi as unresolvable in its current form. Trade only on Polymarket, which has clear, mutually exclusive resolution paths. Request clarification from Kalshi on whether this should be a Yes/No on Rider winning, or if the rule is a documentation error.Critical Divergence Points:
Polymarket:
Clean binary logic: Siena win resolves to Siena Saints; Rider win resolves to Rider Broncs. Postponement keeps market open; cancellation without makeup resolves 50-50. Resolution based on final score including overtime.Kalshi:
Contradictory resolution rule: states both Rider win and Siena win resolve to Yes. This creates logical impossibility where two mutually exclusive outcomes map to identical resolution state.
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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