| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Over 125.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 128.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 131.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 134.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 137.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 140.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 143.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 146.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 149.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 152.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 155.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
This prediction market lets traders take positions on the combined final score (total points) of the Notre Dame at UConn game; it matters because total-points markets aggregate public expectations about how high- or low-scoring the game will be. Traders and observers use the market to track how new information (lineups, injuries, weather) shifts those expectations.
Notre Dame and UConn have met intermittently rather than as an annual rivalry, and each program’s offensive and defensive identity can change substantially from season to season due to coaching, recruiting, and roster turnover. Recent team form, injuries, and matchup-specific advantages (tempo, size, and depth) are the most relevant context for this market, while historical scores between the two teams provide limited but useful perspective.
Market prices on a Total Points market represent the collective assessment of which scoring range or threshold is most likely given available information, and they update as news arrives. Use price movements as signals about how new data (injuries, starting lineups, weather, coaching decisions) are expected to affect the game’s combined scoring.
The event page currently lists the close time as TBD; exchanges commonly close total-points markets at or before game start to prevent trading on live game information, so check the KALSHI interface for the definitive timestamp as the game approaches.
The 11 outcomes correspond to discrete scoring ranges or threshold buckets for the combined final score between Notre Dame and UConn; consult the market’s outcome labels on KALSHI to see the exact point ranges or breakpoints being traded.
Absences of a team’s primary scorer or starting quarterback (or, in basketball, the leading scorer/primary ball-handler) or top defenders/rebounders are most impactful, because they directly alter offensive output and defensive suppression, and late changes to those roles typically move market expectations.
Head-to-head history can provide context about matchup tendencies, but roster turnover and yearly coaching changes mean past scores should be weighted lightly; combine head-to-head data with current-season offensive/defensive metrics and recent form for a more reliable view.
Rapid moves typically reflect traders incorporating new, potentially game-changing information; verify the source and the expected on-court impact (e.g., who replaces an injured starter, how much usage changes), and note that early sharp moves can also be followed by stabilization as the market digests confirmation or counter-evidence.