| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| North Dakota St. wins by over 12.5 Points | 48% | 43¢ | 48¢ | — | $15K | Trade → |
| North Dakota St. wins by over 9.5 Points | 58% | 56¢ | 58¢ | — | $13K | Trade → |
| North Dakota St. wins by over 6.5 Points | 70% | 67¢ | 70¢ | — | $1K | Trade → |
| North Dakota St. wins by over 18.5 Points | 28% | 22¢ | 27¢ | — | $711 | Trade → |
| North Dakota St. wins by over 15.5 Points | 32% | 32¢ | 36¢ | — | $673 | Trade → |
| North Dakota St. wins by over 3.5 Points | 78% | 74¢ | 79¢ | — | $606 | Trade → |
| North Dakota wins by over 6.5 Points | 10% | 3¢ | 9¢ | — | $280 | Trade → |
| North Dakota wins by over 3.5 Points | 14% | 7¢ | 11¢ | — | $160 | Trade → |
| North Dakota St. wins by over 24.5 Points | 13% | 9¢ | 13¢ | — | $128 | Trade → |
| North Dakota St. wins by over 21.5 Points | 0% | 13¢ | 20¢ | — | $0 | Trade → |
This market trades the point-spread outcome for the college football game North Dakota at North Dakota State; it matters because spread markets summarize collective expectations about the margin of victory and let traders express views on how close the game will be. Market prices can move quickly around news that affects either team's projected margin.
North Dakota State and the University of North Dakota meet as regional rivals with a history of competitive matchups; NDSU has been a consistently strong program and home-field advantage in Fargo is often considered important. Rosters, coaching changes, and the specific season schedule shape how each matchup plays out, so context from the current season matters more than older results.
Prediction market prices for a spread market represent the market consensus about which margin bucket is most likely to occur; traders should read prices as a summary of collective expectations that update when new information arrives. Use prices alongside independent assessment of injuries, weather, and matchup details rather than as a sole input.
The market close is listed as TBD; typically spread markets close shortly before kickoff or when the platform sets a final lock time—check the KALSHI platform for the confirmed close time and any last-minute updates.
The market uses discrete margin-of-victory buckets (ranges) that specify which team covers by how many points; consult the platform’s outcome list to see the exact buckets and any special outcomes such as a push or void.
Late injury/illness reports, official starting lineup announcements, significant weather forecast changes, and sudden roster or travel disruptions are the primary drivers of movement in the run-up to kickoff.
Recent head-to-head trends can inform expectations, but roster turnover, scheme changes, and whether the game is home or away make older results less predictive; prioritize recent-season matchups and similar situational games.
Watch starting quarterbacks, the lead running back and offensive line, the defensive front’s ability to stop the run, and special teams (kicking and returns); changes to any of these areas often have an outsized impact on the expected margin.