| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Seattle wins by over 3.5 runs | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Seattle wins by over 2.5 runs | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Seattle wins by over 1.5 runs | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Cleveland wins by over 1.5 runs | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Cleveland wins by over 2.5 runs | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Cleveland wins by over 3.5 runs | 0% | 0¢ | 0¢ | — | $0 | Trade → |
This market lets traders express expectations about the point spread in the Cleveland vs Seattle game; it matters because spread outcomes summarize market views on which team will outperform the other by how many points.
Cleveland and Seattle have differing offensive and defensive strengths, quarterback play, and coaching styles that shape game dynamics; recent form, roster changes, and situational factors (home/away, rest) all influence how the spread is set. This market offers six distinct spread outcomes and will reflect new information up to its close (currently TBD).
Prices in a spread market represent the market consensus about which side of the point-differential will occur and move as new information arrives; in practice, traders use those price movements to infer how the market updates expectations about this specific matchup.
The market close is listed as TBD; typically KALSHI markets close at or shortly before official kickoff, so monitor the event page for the final close time and any platform announcements.
Each outcome corresponds to a discrete spread bracket or specific point-differential range for the game (e.g., one outcome for Cleveland covering by a certain range, another for Seattle covering); consult the market's outcome labels on the KALSHI page for exact definitions.
Injuries to quarterbacks, primary pass-catchers, or key defensive players change expected scoring margins and typically prompt traders to reprice outcomes, increasing the chance of outcomes favoring the healthier side.
Home-field factors—crowd noise, stadium familiarity, travel fatigue, and local climate—can meaningfully influence game flow and are routinely priced into spread markets, so location is a persistent driver of outcome differences.
Head-to-head history provides context about matchup tendencies, but it should be combined with recent form, injury status, coaching changes, and situational stats (pace, turnovers, red-zone efficiency) because spreads primarily reflect expected point differentials under current conditions.