| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Wright St. wins by over 3.5 Points | 54% | 51¢ | 55¢ | — | $366 | Trade → |
| Detroit Mercy wins by over 3.5 Points | 24% | 24¢ | 30¢ | — | $5 | Trade → |
| Detroit Mercy wins by over 12.5 Points | 0% | 3¢ | 11¢ | — | $0 | Trade → |
| Wright St. wins by over 15.5 Points | 0% | 10¢ | 18¢ | — | $0 | Trade → |
| Wright St. wins by over 6.5 Points | 0% | 39¢ | 43¢ | — | $0 | Trade → |
| Wright St. wins by over 12.5 Points | 0% | 17¢ | 24¢ | — | $0 | Trade → |
| Detroit Mercy wins by over 9.5 Points | 0% | 8¢ | 15¢ | — | $0 | Trade → |
| Detroit Mercy wins by over 6.5 Points | 0% | 14¢ | 22¢ | — | $0 | Trade → |
| Wright St. wins by over 18.5 Points | 0% | 5¢ | 13¢ | — | $0 | Trade → |
| Wright St. wins by over 9.5 Points | 0% | 26¢ | 32¢ | — | $0 | Trade → |
This market lets traders take positions on the point-spread outcome for the college basketball game between Detroit Mercy and Wright State; it matters because spread markets aggregate expectations about margin of victory and game dynamics. The market's ten discrete outcomes partition possible winning margins to reflect different spread ranges.
Detroit Mercy and Wright State are mid-major programs whose matchup outcome depends on recent form, roster availability, and matchup specifics rather than national rankings; Wright State has historically been competitive in its conference while Detroit Mercy has had variable results season to season. Context such as non-conference scheduling, travel distance, and coaching continuity can substantially shift expectations compared to a raw pregame line.
Prediction-market prices on spread outcomes represent the market's current consensus about which margin-range is most likely, and movement in prices reflects new information (injuries, lineup changes, betting flows). Use prices to gauge how traders value particular margin intervals, but always check market liquidity and the platform's stated settlement rules before assuming finality.
The market lists the close as TBD; on most platforms trading closes at the official game start or at the platform-specified close time, so monitor the market page for the exact timestamp and any updates from the exchange.
They represent discrete margin ranges (spread buckets) covering different possible winning margins between the teams; each outcome corresponds to a specific interval of final point differential used for settlement.
Significant late news—such as a starter ruled out—can shift trader beliefs about the likely margin, producing rapid price movement across the spread buckets; larger impacts occur when the absent player is a primary scorer, rebounder, or playmaker.
Total volume traded provides a snapshot of past activity but does not guarantee current liquidity; relatively low total volume suggests tighter depth and potentially larger price impact for sizable orders, so consider using limit orders or smaller trades.
Use head-to-head and recent-season margins as one input alongside adjustments for roster changes, schedule context, and matchup metrics (three-point shooting, turnovers, rebound margins); historical results matter less when rosters or contexts differ substantially.