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Sports OPEN

Minnesota at Los Angeles L: Rebounds

📊 $8K traded 🏦 Source: Kalshi
Total Volume
$8K
Open Interest
8,180
Active Markets
35
Markets
35

Trade This Market

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Yes Ask
Last Price
Prev Close
Buy YES → Buy NO

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

All Outcomes (35)
Outcome Probability Yes Bid Yes Ask 24h Change Volume
Rudy Gobert: 12+ 50%
49¢ 50¢ $2K Trade →
Luka Dončić: 8+ 58%
50¢ 58¢ $2K Trade →
Anthony Edwards: 5+ 54%
50¢ 54¢ $812 Trade →
Donte DiVincenzo: 4+ 52%
46¢ 52¢ $671 Trade →
Luka Dončić: 10+ 33%
33¢ 34¢ $547 Trade →
Deandre Ayton: 7+ 49%
45¢ 49¢ $531 Trade →
Deandre Ayton: 10+ 20%
18¢ $359 Trade →
Julius Randle: 6+ 62%
60¢ 61¢ $278 Trade →
Luka Dončić: 6+ 79%
76¢ 82¢ $261 Trade →
Jaden McDaniels: 4+ 62%
52¢ 61¢ $200 Trade →
Deandre Ayton: 8+ 39%
31¢ 38¢ $189 Trade →
Donte DiVincenzo: 6+ 21%
12¢ 20¢ $112 Trade →
Anthony Edwards: 6+ 38%
30¢ 38¢ $100 Trade →
Rudy Gobert: 14+ 29%
26¢ 29¢ $92 Trade →
Deandre Ayton: 6+ 63%
55¢ 62¢ $82 Trade →
Rudy Gobert: 10+ 72%
66¢ 71¢ $68 Trade →
Julius Randle: 10+ 16%
17¢ $11 Trade →
Rudy Gobert: 16+ 15%
16¢ $11 Trade →
Anthony Edwards: 4+ 70%
68¢ 70¢ $8 Trade →
Rudy Gobert: 8+ 86%
76¢ 85¢ $2 Trade →
Julius Randle: 8+ 0%
26¢ 35¢ $0 Trade →
Anthony Edwards: 8+ 0%
15¢ $0 Trade →
Jaden McDaniels: 2+ 0%
84¢ 99¢ $0 Trade →
Donte DiVincenzo: 8+ 0%
$0 Trade →
Donte DiVincenzo: 2+ 0%
83¢ 95¢ $0 Trade →
Julius Randle: 12+ 0%
$0 Trade →
Luka Dončić: 12+ 0%
17¢ $0 Trade →
Deandre Ayton: 12+ 0%
$0 Trade →
Jaden McDaniels: 6+ 0%
16¢ 29¢ $0 Trade →
Julius Randle: 7+ 0%
42¢ 48¢ $0 Trade →
Jaden McDaniels: 8+ 0%
11¢ $0 Trade →
Anthony Edwards: 2+ 0%
90¢ 99¢ $0 Trade →
Jaden McDaniels: 10+ 0%
$0 Trade →
Donte DiVincenzo: 10+ 0%
$0 Trade →
Luka Dončić: 4+ 0%
71¢ 97¢ $0 Trade →

About This Market

This market forecasts which rebounds outcome will occur in the Minnesota at Los Angeles L game; it matters to traders who want to express views on the game’s physical, possession-based dynamics.

The event is tied to a head-to-head basketball matchup between Minnesota and Los Angeles L, where official rebound totals are recorded by the scorers and determine which discrete outcome wins. Historical rebounding patterns between the teams provide context, but roster changes, coaching decisions, and game pace frequently change expectations from one meeting to the next.

Market prices on this platform aggregate trader beliefs about likely rebound totals across the listed outcomes; they are a real‑time signal that can move as new information (lineups, injuries, weather for travel, etc.) arrives.

Key Factors

Frequently Asked Questions

When will this Minnesota at Los Angeles L: Rebounds market close?

The event page lists the closing time as TBD; the platform typically updates the close time ahead of tip‑off or when the officiating/scoring window is finalized, so check the market page for the official closing timestamp.

How do late lineup or rotation changes for Minnesota affect which rebounds outcome wins?

Late changes to starters or minutes shift rebound opportunities — inserting a bigger starter or resting a primary rebounder can materially change the expected distribution of rebounds and prompt price movement across outcomes.

What in‑game developments most commonly move this rebounds market?

In‑game factors such as early foul trouble, injuries, coaching substitution patterns, and unexpected pace swings are the fastest drivers of market updates because they change who is on the floor and how many opportunities for rebounds exist.

How does the fact that there are 35 outcomes affect trading strategy for this market?

Thirty‑five discrete outcomes makes the market highly granular, so liquidity may be dispersed; traders should be aware that thin liquidity can lead to larger spreads between outcomes and that focusing on a small group of adjacent buckets or using limit orders can manage execution risk.

Are past head‑to‑head rebounding numbers between Minnesota and Los Angeles L reliable predictors for this market?

Past head‑to‑head stats are useful background, but their predictive power is limited if either team has had recent roster, coaching, or role changes; combine historical data with up‑to‑date injury, matchup, and pace information for a more robust view.

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