| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Dylan Guenther: 2+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Clayton Keller: 3+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Dylan Guenther: 1+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Clayton Keller: 2+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Clayton Keller: 1+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Wyatt Johnston: 3+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Jason Robertson: 1+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Nick Schmaltz: 1+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Matt Duchene: 3+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Jason Robertson: 3+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Mikhail Sergachev: 1+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Matt Duchene: 2+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Thomas Harley: 1+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Wyatt Johnston: 2+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Mavrik Bourque: 1+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Logan Cooley: 1+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Jason Robertson: 2+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Mikhail Sergachev: 3+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Miro Heiskanen: 3+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Mavrik Bourque: 3+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Mavrik Bourque: 2+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Matt Duchene: 1+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Wyatt Johnston: 1+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Dylan Guenther: 3+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Nick Schmaltz: 2+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Miro Heiskanen: 2+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Mikhail Sergachev: 2+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Logan Cooley: 2+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Thomas Harley: 2+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Thomas Harley: 3+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Miro Heiskanen: 1+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Logan Cooley: 3+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Nick Schmaltz: 3+ | 0% | 0¢ | 0¢ | — | $0 | Trade → |
This market forecasts scoring for the UTA Mammoth at DAL Stars game, letting traders express views on how many points will be scored. It matters for fans and traders who want to hedge or speculate on game-level scoring outcomes.
The market covers a single matchup between the Utah Mammoth and the Dallas Stars, presented as multiple discrete point outcomes so participants can choose granular scoring ranges or totals. Historical scoring patterns between the two clubs, each team’s offensive and defensive identities, and short-term roster changes are the primary contextual inputs that typically inform expectations.
Market prices are an aggregation of participant expectations about which point outcome will occur; movement reflects new information such as injuries or lineup changes. Higher prices indicate outcomes the market views as less likely, while lower prices indicate outcomes the market views as more likely.
The event page lists the close time as TBD; typically the market will close at the operator-specified time (often at or just before game start). Check the Kalshi market page for the definitive closing timestamp.
The 33 outcomes are discrete scoring outcomes (specific point totals or defined ranges) offered for trading; the market page shows exactly which total or range each outcome corresponds to so you can select the scenario you want to trade.
Whether overtime or shootout scoring counts depends on the market’s settlement rules; always read the event’s settlement clause on the Kalshi page to see if only regulation time is counted or if overtime/shootouts are included.
Late roster news can materially alter expected scoring; monitor official team announcements and trusted beat reporters, and be prepared for rapid price movement as the market incorporates the new information.
Head-to-head history can provide useful signals about matchup tendencies, but its relevance depends on sample size and context—changes in rosters, coaching, or season phase can make historical numbers less predictive than recent form and current injuries.