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%
Closed: Oct 10, 5:24 PM EST
Kalshi
This market tracks the likelihood of various outcomes in the Miami (OH) vs. UMass college football game on October 10th, aggregating data from Polymarket and Kalshi. Currently, the consensus probability of Miami (OH) making 40+ yard field goals – over/under 0.5 – is 100.0%, with Miami (OH) vs. UMass: Total 4th Down Conversions O/U 0.5 also at 100.0%. Resolution will be based on data reported at https://www.ncaa.com/. Keep an eye on the game itself, scheduled for October 10th, as the final score and in-game statistics will determine the outcomes.
In the upcoming college football game between Miami (OH) and UMass, scheduled for October 10 at 2:00PM ET: This market will resolve to "Miami (OH)" if Miami (OH) win the game. This market will resolve to "UMass" if UMass 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 specified team if they win the college football game originally scheduled for Oct 10, 2026. If the game is postponed but begins within 48 hours of its originally scheduled start time, the market will remain open and resolve based on the official final result. If the game is cancelled or not started within 48 hours of its originally scheduled start, the market will resolve to a fair price.
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. Different platforms attract different types of traders, leading to variations in price. Polymarket may have a user base with specialized knowledge of college football, while Kalshi might be more influenced by general public opinion. Furthermore, each platform has its own fee structure and liquidity, which can impact trading behavior and price formation. The differing market structures and user demographics contribute to the observed price discrepancies, even though both are attempting to forecast the same event.