| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Over 143.5 points scored | 48% | 45¢ | 47¢ | — | $402 | Trade → |
| Over 137.5 points scored | 0% | 2¢ | 98¢ | — | $0 | Trade → |
| Over 131.5 points scored | 0% | 2¢ | 98¢ | — | $0 | Trade → |
| Over 134.5 points scored | 0% | 2¢ | 98¢ | — | $0 | Trade → |
| Over 128.5 points scored | 0% | 3¢ | 98¢ | — | $0 | Trade → |
| Over 158.5 points scored | 0% | 2¢ | 97¢ | — | $0 | Trade → |
| Over 149.5 points scored | 0% | 2¢ | 97¢ | — | $0 | Trade → |
| Over 146.5 points scored | 0% | 2¢ | 97¢ | — | $0 | Trade → |
| Over 152.5 points scored | 0% | 2¢ | 98¢ | — | $0 | Trade → |
| Over 155.5 points scored | 0% | 2¢ | 98¢ | — | $0 | Trade → |
| Over 140.5 points scored | 0% | 53¢ | 55¢ | — | $0 | Trade → |
This market asks which total-points range the combined score of the St. Bonaventure at George Mason game will fall into; it matters because it aggregates trader expectations about how high- or low-scoring the game will be.
This is a college basketball matchup between two NCAA Division I programs; both teams' season form, conference placement, and recent scheduling all influence scoring expectations. Historical meeting results, each team's style of play, and any roster or coaching changes provide useful background for forecasting total points.
Market odds represent the collective view of traders about which scoring range is most likely and will update as new information arrives; use them as a real-time consensus forecast rather than a certainty.
The close time is listed as TBD for this market; check the KALSHI platform for the specific closing time and note that markets typically close shortly before the scheduled tip-off or per the platform's rules.
Total Points refers to the combined points scored by both teams in the listed game; verify the market rules on KALSHI to confirm whether overtime points are included or excluded for this specific contract.
Key drivers include last-minute injury or lineup news, announced starting lineups, tempo and matchup analytics released before tip-off, late-breaking weather or travel reports affecting the away team, and significant new information about coaching strategies or rotations.
Head-to-head and season averages are useful context but should be balanced with recent form, the location of the game, current injuries, and pace-of-play metrics; recent multi-game trends and same-venue splits often provide more timely signals.
Overtime increases the combined score and can push totals into higher ranges; whether overtime counts depends on the contract rules—confirm on KALSHI—so traders should account for the probability of overtime only if the market includes OT scoring.