| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Over 162.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 141.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 144.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 147.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 150.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 153.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 156.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 159.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 165.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 168.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 171.5 points scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
This market asks traders to predict the combined final score (total points/runs) of the UC Davis at Cal State Fullerton game. Total markets condense expectations about offense, defense, pace, and conditions into a single measurable outcome, making them useful for assessing game-level scoring expectations.
UC Davis and Cal State Fullerton are collegiate programs that meet in sport-specific competitions where scoring patterns can differ widely by season, venue, and matchup. Cal State Fullerton has a strong historical reputation in baseball and often benefits from home-field familiarity; UC Davis can bring different tempo and roster matchups that shift scoring expectations. Traders typically look at recent form, head-to-head results, and venue characteristics when forming views.
Market prices represent the community’s aggregated expectation for the final combined score; higher prices for higher-score outcomes indicate market participants are anticipating a more offense-heavy game, while lower-price outcomes imply expectations of a lower-scoring contest. Use prices together with contextual information (lineups, weather, pitching, tempo) to form a trading view rather than as standalone truth.
Starting pitchers/starters are among the strongest drivers of total-score expectations: a dominant starter tends to lower expected runs scored, while a less experienced or fatigued starter can push expected totals higher. Markets typically react quickly when official lineups or rotation spots are posted, so traders watch team announcements and probables closely.
Home-field effects include familiarity with playing surface, crowd influence, and specific park or arena traits—such as ballpark dimensions or typical wind patterns—that can increase or decrease scoring. Historical scoring splits at the Fullerton venue, combined with current weather forecasts, are important context when assessing the total.
Settlement rules depend on the platform: most settle on the official final score once the game is declared official under the sport’s rules; postponed or suspended games may be settled after a rescheduled completion or can be voided depending on the exchange’s policy. Check the specific KALSHI market rules for this event to confirm how extraordinary interruptions are treated.
Relevant trends include recent head-to-head scores (especially at the same venue), each team’s season scoring and run-allowance trends, performance against similar opponents, and how either team performs early vs. late in games. Use multiple seasons with caution—small samples and roster turnover can limit their predictive value.
In active markets, prices can update within seconds to minutes after an information release; in thinly traded or low-liquidity markets, even a small bet can move prices sharply and changes may be more erratic. Monitor liquidity and order-book depth on the market—when volume is low, expect larger swings from individual trades.