| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| UC Santa Barbara wins by over 1.5 Points | 49% | 49¢ | 51¢ | — | $3K | Trade → |
| UC Santa Barbara wins by over 7.5 Points | 32% | 28¢ | 30¢ | — | $59 | Trade → |
| UC Santa Barbara wins by over 2.5 Points | 47% | 46¢ | 47¢ | — | $50 | Trade → |
| UC Santa Barbara wins by over 4.5 Points | 41% | 37¢ | 40¢ | — | $23 | Trade → |
| UC Santa Barbara wins by over 10.5 Points | 19% | 19¢ | 22¢ | — | $15 | Trade → |
| UC San Diego wins by over 11.5 Points | 10% | 13¢ | 15¢ | — | $2 | Trade → |
| UC San Diego wins by over 2.5 Points | 38% | 40¢ | 41¢ | — | $1 | Trade → |
| UC San Diego wins by over 1.5 Points | 43% | 43¢ | 45¢ | — | $1 | Trade → |
| UC San Diego wins by over 14.5 Points | 0% | 5¢ | 7¢ | — | $0 | Trade → |
| UC San Diego wins by over 17.5 Points | 0% | 3¢ | 5¢ | — | $0 | Trade → |
| UC Santa Barbara wins by over 13.5 Points | 0% | 12¢ | 15¢ | — | $0 | Trade → |
| UC Santa Barbara wins by over 11.5 Points | 0% | 17¢ | 21¢ | — | $0 | Trade → |
| UC San Diego wins by over 7.5 Points | 0% | 21¢ | 24¢ | — | $0 | Trade → |
| UC San Diego wins by over 10.5 Points | 0% | 15¢ | 18¢ | — | $0 | Trade → |
| UC San Diego wins by over 8.5 Points | 0% | 19¢ | 22¢ | — | $0 | Trade → |
| UC San Diego wins by over 5.5 Points | 0% | 26¢ | 30¢ | — | $0 | Trade → |
| UC San Diego wins by over 13.5 Points | 0% | 6¢ | 10¢ | — | $0 | Trade → |
| UC San Diego wins by over 4.5 Points | 0% | 30¢ | 34¢ | — | $0 | Trade → |
| UC Santa Barbara wins by over 5.5 Points | 0% | 34¢ | 37¢ | — | $0 | Trade → |
| UC Santa Barbara wins by over 8.5 Points | 0% | 26¢ | 29¢ | — | $0 | Trade → |
| UC Santa Barbara wins by over 14.5 Points | 0% | 8¢ | 11¢ | — | $0 | Trade → |
| UC Santa Barbara wins by over 17.5 Points | 0% | 4¢ | 7¢ | — | $0 | Trade → |
This market asks which point-spread outcome will occur in the UC San Diego at UC Santa Barbara game, letting traders speculate on the margin of victory rather than just the winner. Spread markets are useful for assessing how much better one team performs relative to the other on game day.
The matchup pits two California universities that compete in the same conference, so results can matter for conference standings and postseason positioning. Both programs' recent form, roster changes, and home/away dynamics shape expectations; historical rivalry results provide context but individual-game factors often dominate. Because the market is live and closes close to tip-off, news that breaks the day of the game can materially change the market.
Market prices for each spread outcome reflect the collective view of traders about which margin will occur and move as new information appears. Interpret prices as relative confidence in different margin ranges rather than fixed predictions — they update with injuries, starting lineup announcements, travel issues, and other real-time factors.
Exact close time is determined by the platform and often aligned to the game tip-off; because this event lists the close time as 'TBD', check the KALSHI interface or event details for the final closure. Expect the market to close at or immediately before the official start unless the platform specifies otherwise.
Each outcome corresponds to a specific spread margin range (different possible point-differentials) that could occur when the game finishes; only the outcome matching the final margin resolves as correct. The outcomes divide possible margins into discrete bins so traders can express views on how large the victory will be.
Give considerable weight to confirmed injuries, suspensions, or late scratches for starters and key rotation players because they can shift the expected margin substantially. Monitor official team reports, pregame warmups, and reliable beat reporters—markets typically react quickly to those updates.
Head-to-head history provides context on matchup tendencies but is only one input; prioritize recent season matchups, roster continuity, and current-season statistics (home/away splits, scoring margins) since teams evolve year to year and situational factors often drive the spread.
Settlement in those cases follows the platform's event rules—typical outcomes include voiding positions with refunds if the game is not played within a specified reschedule window or waiting to settle when the official result occurs. Check KALSHI's terms and the event's settlement policy for the definitive procedure.