TOTAL VOLUME:
$54.6b
24H VOL:
$283,259,638
OPEN INTEREST:
$1,235,549,422
505,521
Markets across
13,661
events
MATCHED EVENTS:
1,892
PLATFORM COVERAGE:
4
Polymarket:
51%
VS.
Kalshi:
49%
Little Rock Trojans vs. Lindenwood Lions
Volume:
$329,422
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
A men's college basketball game between the Little Rock Trojans and Lindenwood Lions scheduled for March 4, 2026 at 9:30 PM ET. Markets cover the moneyline winner, total points over/under 148.5, and multiple spread variations (-2.5, -3.5, -4.5 for Lindenwood).
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi's moneyline market contains a logical contradiction: both possible outcomes (Lindenwood win and Little Rock win) are mapped to the same resolution (Yes), making the market fundamentally unresolvable and unhedgeable.Hero Tip:
Do not trade the Kalshi moneyline. It is broken. Trade Polymarket's winner, spread, and total markets instead—they all use consistent final-score resolution logic and are resolvable.Critical Divergence Points:
- Kalshi: Moneyline resolves to Yes for either team winning. Logic flaw: both outcomes map to Yes, creating an unresolvable market. Quote: If Lindenwood wins...Yes. If Little Rock wins...Yes.
- Polymarket: Moneyline resolves to the winner's name (Little Rock Trojans or Lindenwood Lions). Standard binary resolution. Winner determined by final score including overtime. Postponement keeps market open; cancellation with no makeup resolves 50-50.
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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