TOTAL VOLUME:
$154.4b
24H VOL:
$235,918,089
24H TRANSACTIONS:
2,984,028,936
OPEN INTEREST:
$1,884,820,873
413,109
Markets across
40,428
events
MATCHED EVENTS:
4,364
PLATFORM COVERAGE:
5
Polymarket:
37%
VS.
Kalshi:
63%
Time left: 04d:21h:45m
$
This group resolves to the outcome of the North Carolina vs. UConn college football game scheduled for November 7th. Markets exist for the winner of the game, point spreads, over/under totals, and various statistical achievements during the game.
In the upcoming college football game between North Carolina and UConn, scheduled for November 7 at 12:00PM ET: This market will resolve to "North Carolina" if North Carolina win the game. This market will resolve to "UConn" if UConn win the game. Overtime is included if played. If the game ends in a tie, this market will resolve 50-50. If the game is postponed, this market will remain open until the game has been completed. If the game is canceled entirely, with no make-up game, this market will resolve 50-50.
Markets resolve based on the winning team of the college football game originally scheduled for November 7, 2026. If the game is postponed but begins within 48 hours of the original scheduled start time, markets remain open and resolve according to the official result. If the game is cancelled or does not start within 48 hours of the original time, markets resolve to a fair price. Kalshi explicitly states it is not affiliated with the NCAA, and all trademarks belong to their respective owners.
In the upcoming college football game between North Carolina and UConn, scheduled for November 7 at 12:00PM ET: This market will resolve to "North Carolina" if North Carolina win the game. This market will resolve to "UConn" if UConn win the game. Overtime is included if played. If the game ends in a tie, this market will resolve 50-50. If the game is postponed, this market will remain open until the game has been completed. If the game is canceled entirely, with no make-up game, this market will resolve 50-50.
Prediction market odds, like those found in this market, often reflect the wisdom of the crowd, potentially offering a different perspective than traditional sportsbooks. Sportsbooks set lines based on their own models and aim to balance action on both sides, while prediction markets allow traders to freely express their beliefs. This can lead to discrepancies, as prediction markets may incorporate information not fully captured by sportsbook algorithms. However, both aim to accurately forecast the outcome of the North Carolina vs. UConn game, and both can be valuable tools for informed decision-making.
Prices on Polymarket and Kalshi may differ due to several factors. 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. Each platform has its own user base, trading rules, and liquidity, which can influence price discovery. Additionally, the specific markets offered on each platform might vary slightly, focusing on different aspects of the North Carolina vs. UConn game. For example, one platform might focus on the point spread, while the other concentrates on the outright winner. These differences in market structure and participation can lead to divergent pricing, even for the same underlying event.
This market resolves around Nov 7, 2026, with the outcome confirmed once the event is verifiable from credible public reporting. The result of the North Carolina vs. UConn game, as reported by official sources, will determine the winning outcome in this market. The platform will verify the final score and declare the appropriate outcome based on the established market conditions. Traders who correctly predicted the result will be rewarded based on the odds at the time of their trades.
Several factors could influence the trading activity and price movement in this market. Any news regarding injuries to key players on either the North Carolina or UConn teams would likely have a significant impact. Changes in coaching strategies or team dynamics could also shift expectations. Furthermore, public sentiment and media coverage surrounding the game can influence trader behavior. Unexpected upsets in other related games or broader basketball news could also indirectly affect this market, as traders adjust their predictions based on the evolving landscape.