| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Over 143.5 points scored | 53% | 51¢ | 53¢ | — | $4K | Trade → |
| Over 146.5 points scored | 43% | 43¢ | 46¢ | — | $4K | Trade → |
| Over 140.5 points scored | 61% | 59¢ | 62¢ | — | $682 | Trade → |
| Over 137.5 points scored | 66% | 66¢ | 69¢ | — | $180 | Trade → |
| Over 131.5 points scored | 75% | 75¢ | 81¢ | — | $57 | Trade → |
| Over 155.5 points scored | 21% | 21¢ | 28¢ | — | $49 | Trade → |
| Over 149.5 points scored | 33% | 34¢ | 39¢ | — | $7 | Trade → |
| Over 134.5 points scored | 73% | 70¢ | 75¢ | — | $4 | Trade → |
| Over 158.5 points scored | 0% | 16¢ | 23¢ | — | $0 | Trade → |
| Over 128.5 points scored | 0% | 80¢ | 86¢ | — | $0 | Trade → |
| Over 152.5 points scored | 0% | 28¢ | 30¢ | — | $0 | Trade → |
This market asks how many total points will be scored in the Nebraska at UCLA game, aggregating buyer expectations across multiple discrete outcomes. It matters because total-points markets capture collective views on pace, scoring efficiency, and game script.
Nebraska and UCLA bring different offensive and defensive profiles that influence scoring expectations: one team may emphasize tempo and the other more conservative play-calling, and the matchup is affected by travel and the home-field environment. College football totals are driven by matchup-specific elements (quarterback play, turnovers, special teams) as well as situational factors like weather and kickoff time.
Market prices represent the community’s relative support for each total-points bucket; movements reflect new information and changing expectations rather than fixed predictions. Use prices as a real-time signal of how participants are weighting likely game scripts and scoring outcomes.
The event page lists the close time as TBD; typically the market will close according to KALSHI’s rules (usually before kickoff) or at a specified cutoff on the platform, so check the market page for the finalized close time.
Each outcome corresponds to a discrete total-points bucket or exact total defined by the market creator (for example ranges or single-point totals); outcome labels on the market page describe the exact scoring intervals so traders know which range they are buying or selling.
Significant injury news typically shifts expectations for scoring and causes rapid price movement across outcomes — reduced offensive continuity or a backup QB tends to lower expected scoring, while a surprise full-strength lineup can lift it; markets adjust as participants react to verified reports.
Price moves reflect new information (injuries, weather, depth chart announcements, public betting flows) and changing consensus on likely game scripts; large, sustained moves often follow authoritative news, while small fluctuations can reflect routine liquidity and trader opinion shifts.
Home-field can influence scoring via crowd impact, travel fatigue for the visitor, and situational familiarity; incorporate venue effects alongside matchup specifics (offensive schemes and recent scoring trends) rather than assuming a uniform boost or suppression of points.