TOTAL VOLUME:
$54.3b
24H VOL:
$158,613,015
OPEN INTEREST:
$1,206,641,323
499,459
Markets across
13,676
events
MATCHED EVENTS:
1,789
PLATFORM COVERAGE:
4
Polymarket:
51%
VS.
Kalshi:
49%
New Mexico Lobos vs. Fresno State Bulldogs
Volume:
$1,196,229
Outcome
Chance %
Price
Liquidity
Volume
24h
7d
Open Interest
Ends in
Result
Trade
Description
This event group covers a men's college basketball game between New Mexico Lobos and Fresno State Bulldogs scheduled for February 21, 2026 at 8:00 PM ET. Markets include moneyline (winner), point spreads at -8.5 and -9.5, and total points over/under at 155.5 and 156.5.
PredictionHero - Resolution Divergence Alerts (RDA)
Divergence Detected
Issue:
Kalshi moneyline market contains a logical contradiction: both New Mexico win and Fresno State win are specified to resolve to Yes, making the market unresolvable. Polymarket markets are logically consistent.Hero Tip:
Avoid trading the Kalshi moneyline until the specification is corrected. Polymarket's suite of markets (moneyline, spreads, totals) are all internally consistent and can be traded safely.Critical Divergence Points:
- Polymarket: Moneyline, spread, and total markets all use standard binary logic: one outcome resolves affirmatively, the other negatively. Postponement keeps markets open; cancellation with no makeup resolves 50-50. All markets reference final score including overtime.
- Kalshi: Moneyline market specifies both New Mexico win and Fresno State win resolve to Yes, creating a logical impossibility. No specification provided for postponement or cancellation scenarios.
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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