| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Over 2.5 goals scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 3.5 goals scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 4.5 goals scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 5.5 goals scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 6.5 goals scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 7.5 goals scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 8.5 goals scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Over 9.5 goals scored | 0% | 0¢ | 0¢ | — | $0 | Trade → |
This market asks which scoring range the combined points by Detroit and Pittsburgh will fall into for their upcoming game. It matters because total-points markets let traders express views on game pace, offensive efficiency, and defensive strength without picking a winner.
Detroit at Pittsburgh is a head-to-head matchup where venue, coaching styles, and recent team form all shape scoring expectations. Historical meetings, each team's offensive identity, and any short-term changes (injuries, lineup changes, or weather) provide context that traders use to form a view. The market's eight outcomes represent discrete total-points ranges that cover plausible final combined scores.
Market prices (odds) reflect the aggregated views of traders about which total-points range will occur; they are signals about consensus expectations, not guarantees. Use them alongside matchup research—injury reports, weather, and play-calling tendencies—to inform decisions.
The event page lists a closing time as TBD; platform practice is to close markets at or shortly before the official kickoff, but final close time depends on Kalshi's event configuration—check the event page for the confirmed close once it is set.
Each of the eight outcomes corresponds to a specific range of combined points (for example, buckets like '0–19', '20–29', etc.); the winning outcome is the bucket that contains the final combined score—read the outcome labels on the event page for the exact ranges.
Injuries to either team’s starting quarterback are the most impactful, followed by losses of key receivers/running backs or major offensive-line absences; on the defensive side, losing a top pass rusher or secondary starter can also meaningfully alter scoring expectations.
Pittsburgh is an open-air venue where wind, cold, or precipitation tend to suppress passing effectiveness and can lower total scoring; check short-range weather forecasts and expected wind/gusts on game day to adjust expectations for passing efficiency and special teams play.
Turnovers and special teams touchdowns can cause large, sudden deviations from expected totals—either reducing scoring by killing drives or increasing combined points via short-field scores—while high penalty counts often extend drives and can inflate totals; these event-level risks make totals inherently more volatile than single-possession predictions.