| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Jakob Chychrun: 1+ | 46% | 0¢ | 46¢ | — | $69 | Trade → |
| Alex Ovechkin: 1+ | 0% | 0¢ | 41¢ | — | $0 | Trade → |
| Ryan Leonard: 1+ | 0% | 0¢ | 32¢ | — | $0 | Trade → |
| Connor Zary: 1+ | 0% | 0¢ | 28¢ | — | $0 | Trade → |
| Matt Coronato: 2+ | 0% | 0¢ | 7¢ | — | $0 | Trade → |
| Morgan Frost: 2+ | 0% | 0¢ | 7¢ | — | $0 | Trade → |
| Connor McMichael: 2+ | 0% | 0¢ | 8¢ | — | $0 | Trade → |
| Mikael Backlund: 2+ | 0% | 0¢ | 7¢ | — | $0 | Trade → |
| Aliaksei Protas: 2+ | 0% | 0¢ | 8¢ | — | $0 | Trade → |
| Alex Ovechkin: 2+ | 0% | 0¢ | 11¢ | — | $0 | Trade → |
| Tom Wilson: 3+ | 0% | 0¢ | 5¢ | — | $0 | Trade → |
| Dylan Strome: 2+ | 0% | 0¢ | 14¢ | — | $0 | Trade → |
| Pierre-Luc Dubois: 2+ | 0% | 0¢ | 10¢ | — | $0 | Trade → |
| Dylan Strome: 3+ | 0% | 0¢ | 4¢ | — | $0 | Trade → |
| Ryan Leonard: 2+ | 0% | 0¢ | 8¢ | — | $0 | Trade → |
| Jakob Chychrun: 2+ | 0% | 0¢ | 14¢ | — | $0 | Trade → |
| Rasmus Sandin: 2+ | 0% | 0¢ | 8¢ | — | $0 | Trade → |
| Tom Wilson: 2+ | 0% | 0¢ | 13¢ | — | $0 | Trade → |
| Matt Coronato: 1+ | 0% | 0¢ | 29¢ | — | $0 | Trade → |
| Rasmus Sandin: 1+ | 0% | 0¢ | 33¢ | — | $0 | Trade → |
| Dylan Strome: 1+ | 0% | 0¢ | 46¢ | — | $0 | Trade → |
| Joel Farabee: 1+ | 0% | 0¢ | 21¢ | — | $0 | Trade → |
| Aliaksei Protas: 1+ | 0% | 0¢ | 32¢ | — | $0 | Trade → |
| Tom Wilson: 1+ | 0% | 0¢ | 45¢ | — | $0 | Trade → |
| Pierre-Luc Dubois: 1+ | 0% | 0¢ | 37¢ | — | $0 | Trade → |
| Connor McMichael: 1+ | 0% | 0¢ | 31¢ | — | $0 | Trade → |
| Morgan Frost: 1+ | 0% | 0¢ | 30¢ | — | $0 | Trade → |
| Mikael Backlund: 1+ | 0% | 0¢ | 29¢ | — | $0 | Trade → |
| Jakob Chychrun: 3+ | 0% | 0¢ | 5¢ | — | $0 | Trade → |
This market covers the assists recorded in the Calgary Flames at Washington Capitals game on KALSHI; traders buy outcomes tied to individual, team, or total-assist outcomes. It matters because assist outcomes reflect playmaking and special-teams performance, and they drive many player- and team-prop valuations.
Assists are credited when a player helps create a goal (NHL official scorers can award up to two assists per goal), so totals depend on how each team generates offense, power-play opportunities, and who is on the ice. Historical tendencies of each franchise and recent form (line chemistry, injuries, coaching strategy) shape expectations for how many assists will be recorded and who will earn them.
Market prices represent the collective view of traders about which assist outcome will occur and update as new information arrives; use them as a live signal rather than a fixed forecast. Because this market has many mutually exclusive outcomes, relative prices show which assist scenarios the market currently favors.
The market lists mutually exclusive assist-related outcomes for this specific game (examples include team assist totals, player-specific assist buckets, or named outcome ranges). See the platform event page for the full enumerated list of the 29 outcomes and their exact wording.
The event close time is listed as TBD on the page; typically, KALSHI markets for a single game close before the game starts or at a platform-specified deadline. Resolution occurs once the official NHL game statistics are finalized according to the platform's settlement rules—check the event page for the confirmed close and settlement timing.
Settlement follows the NHL's official scorer and boxscore: up to two assists can be awarded per goal and only assists recorded in the official game report count. If the league adjusts scoring after the game (within any platform-defined review window), the platform's stated resolution policy determines the final outcome.
Late roster moves materially affect assist expectations because they change who gets power-play and primary playmaking minutes; monitor team reports and line announcements and be prepared for rapid price movement. The market will reflect new information as traders update positions.
The outcomes are structured as exclusive buckets that together cover the possible assist scenarios for the game (for example, discrete ranges or player-specific outcomes). Traders often compare relative prices across similar buckets, consider correlations (one player's assist can reduce another's chance), and use partial hedges across adjacent outcomes rather than single large positions.