| Outcome | Probability | Yes Bid | Yes Ask | 24h Change | Volume | |
|---|---|---|---|---|---|---|
| Carlos Alcaraz -1.5 games | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Daniil Medvedev -3.5 games | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Daniil Medvedev -1.5 games | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Daniil Medvedev -7.5 games | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Carlos Alcaraz -7.5 games | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Carlos Alcaraz -3.5 games | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Daniil Medvedev -5.5 games | 0% | 0¢ | 0¢ | — | $0 | Trade → |
| Carlos Alcaraz -5.5 games | 0% | 0¢ | 0¢ | — | $0 | Trade → |
This market covers the game-spread outcome between Carlos Alcaraz and Daniil Medvedev, showing how the match is expected to play out in total games and margin of victory; it matters for traders who want exposure to match competitiveness rather than just winner/loser.
Alcaraz and Medvedev are top-level players with contrasting styles—Alcaraz’s aggressive all-court play versus Medvedev’s defensive, flat baseline game—so their meetings often produce distinctive scorelines and varied game totals. Surface, tournament round, and recent form shape how long sets run and which player is likely to win games more consistently, making historical context and current conditions both important.
Market prices reflect the consensus view of traders about likely game differentials and update as new information arrives; interpret them as a snapshot of market expectations that change with news, starting lineups, and in-play developments.
The eight outcomes correspond to the market's predefined game-differential buckets or spread ranges; each outcome pays if the final match game margin falls into that specific bucket—see the market page for the exact bucket boundaries.
The precise close time is set by Kalshi and often aligns with the official match start; because this market shows 'Closes: TBD', check the Kalshi market page and the tournament's official schedule for the confirmed closure.
Settlement follows Kalshi’s event rules for this market; some exchanges count the score at the moment of retirement toward total games, but you should consult Kalshi’s posted settlement policy for this specific event for the definitive procedure.
Use head-to-head scorelines to gauge typical set lengths and tactical outcomes between them, but place greater weight on current surface, recent matches, and fitness because prior meetings may have occurred under different conditions.
Key in-play signals include break-point opportunities and conversions, serve effectiveness (aces and double faults), visible signs of fatigue or medical treatment, momentum shifts and how close sets are on the scoreboard—these indicate whether the match is trending toward many short sets or long, tightly contested sets.