| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Marist | 50% | 46¢ | 47¢ | — | $83 | Trade → |
| Quinnipiac | 55% | 53¢ | 55¢ | — | $43 | Trade → |
This prediction market lets traders take positions on the winner of the Marist at Quinnipiac game and aggregates real-time market sentiment about which team will prevail. It matters to fans and traders because it summarizes how new information (injuries, lineups, rest) changes expectations before the matchup.
Marist College (Poughkeepsie, NY) and Quinnipiac University (Hamden, CT) are NCAA Division I programs whose meetings reflect seasonal form, roster turnover, and coaching matchups. Quinnipiac is the home team for this game; while past results between the programs provide context, roster changes and current-season performance are usually more relevant to the immediate outcome.
Market odds represent the collective expectation of participants and update as new information arrives; treat them as a dynamic consensus signal rather than a guarantee of the result.
The market typically closes at or just before the scheduled game start; this specific listing shows the close time as TBD, so check the market page for the official close and any last-minute updates.
This is a binary market with the two outcomes being a Marist win or a Quinnipiac win for the specified game; there is no separate market here for point spreads or totals.
Primary influencers are each team's leading scorers and the starting point guard (who controls tempo) as well as any key rebounder or interior defender; monitor official injury reports and lineup announcements for names and status changes.
Home-court is routinely priced in because of travel, crowd effects, and familiar surroundings; the exact impact varies by matchup and is adjusted as injury reports and other information arrive.
Relatively low trading volume indicates limited liquidity and that prices can move substantially on small orders; it also means the market’s consensus may be driven by few participants, so treat signals as less robust than in high-volume markets.