TOTAL VOLUME:
$154.4b
24H VOL:
$290,393,159
24H TRANSACTIONS:
2,984,640,614
OPEN INTEREST:
$1,944,100,805
420,493
Markets across
40,511
events
MATCHED EVENTS:
4,612
PLATFORM COVERAGE:
5
Polymarket:
37%
VS.
Kalshi:
63%
Closed: Oct 31, 6:50 PM EST
Kalshi
This market tracks the likelihood of a pick-six being scored in the New Mexico vs. San Jose State college football game. Currently, the consensus probability of a pick-six occurring is 100.0%, as aggregated from Polymarket and Kalshi. The resolution source for this market is the official NCAA website. Keep an eye on the game itself, scheduled for October 31st, as the outcome will depend on defensive and offensive performance during the contest.
In the upcoming college football game between New Mexico and San Jose State, scheduled for October 31 at 4:00PM ET: This market will resolve to "New Mexico" if New Mexico win the game. This market will resolve to "San Jose State" if San Jose State 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.
If San Jose St. wins the New Mexico vs San Jose St. college football game originally scheduled for Oct 31, 2026, then the market resolves to Yes. If New Mexico wins the New Mexico vs San Jose St. college football game originally scheduled for Oct 31, 2026, then the market resolves to Yes.
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 for the New Mexico-San Jose State game can vary between Polymarket and Kalshi due to several factors. Each platform has a different user base, trading volume, and liquidity, which can influence price formation. Furthermore, the specific contract designs – for example, whether it’s a binary outcome market or an over/under – can attract different types of traders and lead to differing valuations. Regulatory frameworks and fee structures also play a role. While both platforms aim to predict the same event, these distinctions contribute to the observed price discrepancies in this market.