| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Frankfurt wins by over 1.5 goals | 15% | 15¢ | 17¢ | — | $6 | Trade → |
| St. Pauli wins by over 1.5 goals | 14% | 14¢ | 16¢ | — | $6 | Trade → |
| St. Pauli wins by over 2.5 goals | 4% | 4¢ | 6¢ | — | $5 | Trade → |
| Frankfurt wins by over 2.5 goals | 4% | 5¢ | 6¢ | — | $5 | Trade → |
This prediction market asks which spread outcome will occur in the match Frankfurt at St. Pauli; spread outcomes summarize expected margins of victory and matter for traders who want exposure to how close or one-sided the game will be.
Eintracht Frankfurt and FC St. Pauli are professional German clubs whose matches can produce a wide range of scorelines depending on tactics, personnel, and context. Historical meetings, recent form, and venue all shape expectations for margins, while lineup changes and in-game events can shift those expectations quickly. Because this market has four discrete outcomes it parcels the possible margin outcomes into separate settlement bins rather than a single win/loss outcome.
Market prices reflect the crowd’s consensus about which spread bin is most likely to occur at settlement; compare relative prices across the four outcomes to see which margins the market favors. Prices change as new information (lineups, injuries, weather) arrives, so consider both pregame context and late updates when interpreting the market.
The market’s close time is listed as TBD; on most platforms spread markets close shortly before kickoff, but you should monitor the market page for the exact closing timestamp and any platform notices.
The market is divided into four mutually exclusive spread bins that cover different goal-margin ranges; the precise numeric boundaries for each bin are specified on the market page and determine which outcome settles after the match.
Late changes to starting XI—especially the absence or return of a primary scorer or central defender—can materially change expected margins, and those changes are typically reflected in market prices up until close.
Home conditions can affect travel fatigue, crowd pressure, and familiarity with the pitch, all of which tend to influence margins; quantify this by comparing each team’s home vs away goal margins over recent matches.
Head-to-head trends provide context on matchup dynamics and typical scorelines, but their predictive power is limited by changes in squads, managers, and sample size—use them alongside current-season form and lineup information.