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Miami at Houston: Points

📊 $0 traded 🏦 Source: Kalshi
Total Volume
$0
Open Interest
0
Active Markets
42
Markets
42

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Prices in cents (1¢ = 1%). Trade on Kalshi.

All Outcomes (42)
Outcome Probability Yes Bid Yes Ask 24h Change Volume
Alperen Sengun: 25+ 0%
$0 Trade →
Reed Sheppard: 25+ 0%
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Reed Sheppard: 20+ 0%
$0 Trade →
Davion Mitchell: 15+ 0%
$0 Trade →
Jabari Smith Jr.: 20+ 0%
$0 Trade →
Jabari Smith Jr.: 10+ 0%
$0 Trade →
Kel'el Ware: 20+ 0%
$0 Trade →
Tari Eason: 15+ 0%
$0 Trade →
Alperen Sengun: 30+ 0%
$0 Trade →
Jabari Smith Jr.: 15+ 0%
$0 Trade →
Tyler Herro: 15+ 0%
$0 Trade →
Bam Adebayo: 15+ 0%
$0 Trade →
Amen Thompson: 15+ 0%
$0 Trade →
Alperen Sengun: 10+ 0%
$0 Trade →
Tari Eason: 20+ 0%
$0 Trade →
Bam Adebayo: 20+ 0%
$0 Trade →
Bam Adebayo: 30+ 0%
$0 Trade →
Jabari Smith Jr.: 25+ 0%
$0 Trade →
Bam Adebayo: 25+ 0%
$0 Trade →
Amen Thompson: 30+ 0%
$0 Trade →
Tyler Herro: 20+ 0%
$0 Trade →
Kevin Durant: 25+ 0%
$0 Trade →
Amen Thompson: 20+ 0%
$0 Trade →
Tyler Herro: 25+ 0%
$0 Trade →
Tyler Herro: 30+ 0%
$0 Trade →
Kevin Durant: 20+ 0%
$0 Trade →
Amen Thompson: 25+ 0%
$0 Trade →
Alperen Sengun: 15+ 0%
$0 Trade →
Alperen Sengun: 20+ 0%
$0 Trade →
Davion Mitchell: 20+ 0%
$0 Trade →
Kel'el Ware: 15+ 0%
$0 Trade →
Norman Powell: 15+ 0%
$0 Trade →
Kevin Durant: 35+ 0%
$0 Trade →
Norman Powell: 20+ 0%
$0 Trade →
Reed Sheppard: 10+ 0%
$0 Trade →
Kevin Durant: 30+ 0%
$0 Trade →
Davion Mitchell: 10+ 0%
$0 Trade →
Reed Sheppard: 15+ 0%
$0 Trade →
Norman Powell: 25+ 0%
$0 Trade →
Norman Powell: 30+ 0%
$0 Trade →
Tari Eason: 10+ 0%
$0 Trade →
Kel'el Ware: 10+ 0%
$0 Trade →

About This Market

This prediction market asks how many points will be scored in the Miami at Houston game, broken into many discrete point-related outcomes. It matters because markets synthesize public expectations about scoring and respond quickly to news that affects game tempo and availability.

This is a matchup between the Miami team and the Houston team; the event page lists 42 distinct point outcomes, indicating a granular set of totals or point-range questions rather than a single over/under. Relevant background includes each team’s offensive and defensive styles, recent form, schedule context (rest and travel), and any roster changes that can materially shift scoring dynamics.

Market prices reflect the consensus of traders about likely scoring outcomes and will move as new information (injuries, scratches, weather, minute changes) becomes available. Treat the displayed odds as a real-time snapshot of market expectations rather than a fixed prediction.

Key Factors

Frequently Asked Questions

When does the Miami at Houston: Points market close?

The event page lists the close time as TBD; typically point markets close shortly before game tip or kickoff to lock positions before lineups are finalized. Check the KALSHI market page for the exact, up-to-date close time.

What do the 42 outcomes in this market represent?

The 42 outcomes are distinct point-based results (for example, specific totals, ranges, or discrete bucketed scores) rather than a single binary over/under. Consult the market interface to see the exact outcome labels and which points each one covers.

How do late scratches, injuries, or lineup changes for Miami or Houston affect this points market?

Late availability news can materially shift expected scoring: losing a primary scorer typically lowers projected points for that team and can reduce total points, while losing a key defender can raise opponent scoring. Markets usually react quickly as traders update positions when official injury reports and lineup confirmations are posted.

Does this market settle including overtime, or only regulation?

Settlement rules vary by market; some point markets specify 'regulation only' while others include overtime. Verify the settlement rules on the specific KALSHI market page before trading to know whether extra periods count toward the outcome.

What historical matchup data between Miami and Houston is useful for evaluating the points outcomes?

Look at recent head-to-head scores, each team’s scoring and allowed points over the last several games, pace metrics, and any changes in rosters or coaching since those matchups. Use samples that account for roster continuity and weigh more recent games more heavily for current form.

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