TOTAL VOLUME:
$134.2b
24H VOL:
$126,590,312
24H TRANSACTIONS:
2,388,728,490
OPEN INTEREST:
$1,439,516,703
404,175
Markets across
30,277
events
MATCHED EVENTS:
2,685
PLATFORM COVERAGE:
5
Polymarket:
39%
VS.
Kalshi:
61%
Closed: Sep 26, 8:27 PM EST
Kalshi
This market tracks which team will lead after the first quarter of a college football game between Texas A&M and LSU. It focuses solely on the scoring dynamics of the opening quarter, ignoring the rest of the game. The outcome depends on whether LSU, Texas A&M, or neither team (in case of a tie) scores more points in that initial period.
If LSU wins the 1st quarter of the Texas A&M vs LSU college football game originally scheduled for Sep 26, 2026, then the market resolves to Yes. If Texas A&M wins the 1st quarter of the Texas A&M vs LSU college football game originally scheduled for Sep 26, 2026, then the market resolves to Yes. If neither team wins the 1st quarter of the Texas A&M vs LSU college football game originally scheduled for Sep 26, 2026, then the market resolves to Yes.
This market resolves around Sep 27, 2026, with the outcome confirmed once the event is verifiable from credible public reporting. The winning team of the first quarter of the Texas A&M vs LSU game will determine the winning contract. The official results from the game will be used to determine the outcome. Traders who correctly predicted the first-quarter winner will receive a payout based on the final market price at resolution. This allows participants to profit from accurately forecasting the game's initial stages.
Several factors could influence this market before resolution. Any news regarding key player injuries for either Texas A&M or LSU would likely cause significant movement. Changes in weather forecasts, particularly if they suggest adverse conditions, could also shift the odds. Unexpected announcements about coaching strategies or team lineups could also impact trader sentiment. Furthermore, public perception and analysis from sports commentators can influence trading activity, leading to price fluctuations in this market. Monitoring these signals will be key to understanding potential shifts in the probabilities.