TOTAL VOLUME:
$134.2b
24H VOL:
$130,522,377
24H TRANSACTIONS:
2,388,728,490
OPEN INTEREST:
$1,438,389,636
404,028
Markets across
30,214
events
MATCHED EVENTS:
2,681
PLATFORM COVERAGE:
5
Polymarket:
39%
VS.
Kalshi:
61%
Closed: Sep 26, 4:55 PM EST
Kalshi
This market tracks the total number of 4th down conversions—over or under 0.5—in the UCLA versus Maryland college football game. Currently, the consensus probability of UCLA vs. Maryland: Total 4th Down Conversions O/U 0.5 resolving to ‘Yes’ is 100.0%. This aggregate reflects predictions from Polymarket and Kalshi, and will resolve based on data reported by https://www.ncaa.com/. Keep an eye on the game itself on September 26th, as the outcome will depend on in-game play calling and success rates on 4th down attempts.
In the upcoming college football game between UCLA and Maryland, scheduled for September 26 at 1:30PM ET: This market will resolve to "UCLA" if UCLA win the game. This market will resolve to "Maryland" if Maryland 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.
The market resolves to Yes for the respective team if they win the UCLA vs Maryland college football game scheduled for September 26, 2026. If the game is postponed but starts within 48 hours of the original time, the market stays open and resolves based on the final result. If the game is cancelled or does not start within 48 hours, the market resolves to a fair price. Kalshi is not affiliated with the NCAA, and all trademarks belong to their respective owners.
Prices for this market may vary between Polymarket and Kalshi due to a number of 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 fee structure, all of which can influence price discovery. Differences in market design, such as the types of contracts offered or the liquidity provided, can also contribute to price discrepancies. Furthermore, differing levels of information available to traders on each platform, or varying interpretations of the same information, can lead to divergent predictions. The volume of trading on each platform also plays a role; higher volume generally leads to tighter spreads.