| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Over 2.5 goals scored | 41% | 38¢ | 41¢ | — | $25 | Trade → |
| Over 1.5 goals scored | 68% | 66¢ | 68¢ | — | $7 | Trade → |
| Over 4.5 goals scored | 1% | 5¢ | 12¢ | — | $2 | Trade → |
| Over 3.5 goals scored | 0% | 17¢ | 23¢ | — | $0 | Trade → |
This market asks how many total goals will be scored in the Roma at Bologna match; it matters to traders who want to express views on match tempo and scoring rather than match winner.
Roma and Bologna meetings historically produce varying goal totals depending on tactics and available personnel; both clubs' seasonal form, injuries and fixture congestion influence scoring patterns. Totals markets are popular because they let traders focus on attacking and defensive tendencies without predicting the outright result.
Market prices represent the crowd’s assessment of which total-goals outcome is most likely and will move as new information arrives; use them as a dynamic signal rather than a fixed forecast.
It refers to the combined number of goals scored by both teams in the match; each traded outcome represents a distinct, mutually exclusive range or threshold for total goals.
This event offers four mutually exclusive total-goals outcomes as listed on the trading interface; check the market page to see the exact labels and the goal ranges or thresholds they represent.
The closing time is listed as TBD for now; typically totals markets close at or just before kickoff or at a platform-specified cutoff—monitor the KALSHI interface for the confirmed close time.
Pre-match lineup releases, late injuries or suspensions, significant changes to weather or pitch conditions, major bookmaker odds updates, and any in-game events (early goal or red card) are the fastest triggers for price moves.
Look at recent goals-per-game for each team, home vs. away scoring patterns, reliance on specific goalscorers or set pieces, and whether either side has shown abrupt tactical shifts; use those patterns as inputs but remember that single-match factors can override historical averages.