GroupKALSHI

Event GroupKALSHIElections

North Carolina Supreme Court winner?

Total Volume

$0.00

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Trade on Kalshi

North Carolina Supreme Court winner?

Vol

$0.00

Events

Markets

Kalshi

Platform StatsQuick Platform Comparison

Polymarket

markets · $0.00

Kalshi

markets · $0.00

Price Spread

0.0%avg

max 0.0%

Arbitrage

of 0 cross-platform

Price ChartMatched Markets Price Graph

2 markets tracked

Who will win the Supreme Court race in North Carolina? (Anita Earls)

No data available

Single-Platform Markets(2)

Market	Platform	Price
Who will win the Supreme Court race in North Carolina? (Anita Earls)	Kalshi	77%
Who will win the Supreme Court race in North Carolina? (Sarah Stevens)	Kalshi	24%

AI Analysis

Trader mode: Actionable analysis for identifying opportunities and edge

77%

Top Probability

$0.00

Volume

Markets

Platforms

About This Event

In 2026 If X wins the Supreme Court election in North Carolina in 2026, then the market resolves to Yes. Early close condition: This market will close early following the swearing in of the Supreme Court representative for the seat in question. This market will close early following the swearing in of the Supreme Court representative for the seat in question.

AI-generated analysis based on market data. Not financial advice.

Custom AI Analysis Prompt

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Open in:ChatGPT Claude Gemini Grok DeepSeek

**Role:** Expert Prediction Market Analyst & Trader
**Objective:** Analyze the provided betting market data to deliver actionable insights, identify mispricings, and perform scenario analysis.
**Current Date:** Friday, July 10, 2026
**Context:** Prediction markets (Polymarket/Kalshi) are binary option markets where price (1-99) equals implied probability (1%-99%).

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## 1. Event Snapshot
**Event:** North Carolina Supreme Court winner?
**Category:** Elections | **Status:** active
**Resolution Date:** N/A (Unknown)
**Time Pressure:** Unknown

**Description:** In 2026

If X wins the Supreme Court election in North Carolina in 2026, then the market resolves to Yes. Early close condition: This market will close early following the swearing in of the Supreme Court representative for the seat in question. This market will close early following the swearing in of the Supreme Court representative for the seat in question.

**Resolution Criteria (Sample):**
"If Sarah Stevens wins the Supreme Court election in North Carolina in 2026, then the market resolves to Yes. Early close condition: This market will close early following the swearing in of the Supreme Court representative for the seat in question."

## 2. Market liquidity & Volume
* **Total Volume:** $0
* **Total Liquidity:** $0
* **Activity Level:** Low Activity (Vol/Liq Ratio: 0.00)

## 3. Market Data (Prices & Odds)
### 1. Who will win the Supreme Court race in North Carolina? (Sarah Stevens)
- **Kalshi** (End: Nov 3, 2027): Vol $0
  DATA: [Yes: 23.5% (0.0%)] | [No: 76.5% (0.0%)]

### 2. Who will win the Supreme Court race in North Carolina? (Anita Earls)
- **Kalshi** (End: Nov 3, 2027): Vol $0
  DATA: [Yes: 76.5% (0.0%)] | [No: 23.5% (0.0%)]

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## Analysis Instructions
Please provide a structured analysis using the following template:

### 1. Executive Summary
*   **Verdict:** [Most Likely Outcome]
*   **Confidence:** [0-100]%
*   **Timeframe:** [Immediate / Short-term / Long-term]
*   **Actionable Trade:** [Buy YES / Buy NO / Stay Away] (Include entry price target if applicable)
*   **Rationale:** 2-sentence summary of the core thesis.

### 2. Scenario Analysis
*   **Bull Case (Why YES goes up):** List specific catalysts or data points.
*   **Bear Case (Why YES goes down):** List specifics.
*   **Invalidation Point:** What happens to prove your thesis wrong?

### 3. Market Mechanics & Risks
*   **Liquidity/Volume Analysis:** Is the market efficient? Is it easy to exit?
*   **Cross-Platform Arbitrage:** (If applicable) Is the spread tradeable after fees?
*   **Time Decay:** How does time passing affect this position?

### 4. Signal Scores (0-100)
*   **Momentum:** [Score] (Price trend & velocity)
*   **Conviction:** [Score] (Volume backing the move)
*   **Information Edge:** [Score] (Likelihood of non-public info affecting price)
*   **Risk/Reward:** [Score] (Potential upside vs downside)

**Constraints:**
- Be direct and objective.
- Use bullet points for readability.
- If data is conflicting or insufficient, explicitly state "Low Confidence".

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Educational content is AI-generated and sourced from Wikipedia. It should not be considered financial advice.

KALSHIElections