TOTAL VOLUME:
$154.8b
24H VOL:
$260,393,102
24H TRANSACTIONS:
2,984,640,614
OPEN INTEREST:
$1,894,304,386
408,643
Markets across
39,771
events
MATCHED EVENTS:
4,493
PLATFORM COVERAGE:
5
Polymarket:
37%
VS.
Kalshi:
63%
Time left: 02d:12h:04m
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This event group concerns a women's college basketball game between North Texas Mean Green and San Diego State Aztecs, originally scheduled for November 5, 2026. The markets ask who will win the game, with provisions for cancellation or abandonment.
In the upcoming CWBB game between North Texas Mean Green and San Diego State Aztecs, originally scheduled for Thursday, November 5, 2026 at 2:00 PM ET (as of creation of the first market related to the event): If this market's outcome was definitively determined under its rules by the listed resolution sources before abandonment, that outcome will stand. Otherwise, the following no-result provisions apply. If the game is canceled, does not start by Saturday, November 7, 2026, 11:59 PM ET, or is abandoned with no official result by that time, this market will resolve 50-50.
The event determines the winner of the women’s college basketball game between North Texas and San Diego State, scheduled for November 5, 2026. If San Diego State wins the game as originally scheduled, that market resolves to Yes. Conversely, if North Texas wins, its market resolves to Yes. Should the game be postponed but commence within 48 hours of the original start time, both markets remain open and resolve based on the final official result. If the game is cancelled or fails to start within the 48-hour window, both markets resolve to a fair price. Kalshi clarifies that it holds no official affiliation with the NCAA, and all trademarks remain property of their respective owners.
Prediction market odds, like those reflected in this market, often differ from traditional sportsbook odds due to the different incentives and information sources involved. Sportsbooks primarily aim to set lines that balance betting action and ensure profitability, while prediction markets incentivize participants to express their true beliefs about the outcome. This can lead to prediction markets being more efficient at incorporating diverse information and identifying mispricings. While sportsbooks may adjust odds based on public betting patterns, this market reflects a broader range of opinions and analysis. It’s common to see discrepancies, especially before significant events or when new information emerges.
Polymarket and Kalshi can show different implied probabilities for the same outcome because of liquidity, fee structure, participant mix, and how each venue defines the contract. Prices on Polymarket and Kalshi may differ for several reasons. Each platform has its own user base, trading dynamics, and fee structure, all of which can influence price discovery. Polymarket currently favors San Diego St. wins at an implied probability, while Kalshi leans toward North Texas Mean Green winning. Differences in liquidity and trading volume between the two platforms can also contribute to price discrepancies. Additionally, varying levels of information access and analytical approaches among traders on each platform can lead to divergent opinions and, consequently, different prices.
This market resolves around Nov 5, 2026, with the outcome confirmed once the event is verifiable from credible public reporting. The result of the North Texas vs. San Diego State game will be used to determine the winning outcome in this market. Traders will be able to see the verified result and any associated payouts after the event concludes. The platform will rely on widely accepted sources to confirm the final score and declare a winner, ensuring a transparent and reliable resolution process.
Several signals could significantly impact this market before it resolves around Nov 5, 2026. Any news regarding key player injuries or suspensions for either the North Texas Mean Green or the San Diego State Aztecs would likely cause a shift in probabilities. Major coaching changes, significant shifts in team performance during the season, or even unexpected weather conditions could also influence trading activity. Public sentiment, reflected in media coverage and social media discussions, can also play a role, as traders react to new information and adjust their forecasts accordingly. Keep an eye on these factors as they unfold.