Market snapshot: Oct 12, 2026, 12:00 AM UTC. Prices may have changed since this snapshot.

GroupPOLYMARKET

Event GroupPOLYMARKETSports

W50 Lexington, SC: Kaitlyn Carnicella vs Lucciana Perez Alarcon

Name: W50 Lexington, SC: Kaitlyn Carnicella vs Lucciana Perez Alarcon
End: 2026-10-16T14:30:00.000Z

Total Volume

$60.48K

Events

Markets

Trade on Polymarket

W50 Lexington, SC: Kaitlyn Carnicella vs Lucciana Perez Alarcon

Vol

$60.48K

Events

Markets

Polymarket

Platform StatsQuick Platform Comparison

Polymarket

markets · $60.48K

Kalshi

markets · $0.00

Price Spread

0.0%avg

max 0.0%

Arbitrage

of 0 cross-platform

Volume DistributionTotal: $60.48K

100%

Polymarket leads

Price ChartMatched Markets Price Graph

1 market tracked

Set Handicap: Kaitlyn Carnicella (-1.5) vs Lucciana Perez Alarcon (+1.5)

No data available

Single-Platform Markets(1)

Market	Platform	Price
Set Handicap: Kaitlyn Carnicella (-1.5) vs Lucciana Perez Alarcon (+1.5)	Poly	35%

AI Analysis

Trader mode: Actionable analysis for identifying opportunities and edge

35%

Top Probability

$60.48K

Volume

Markets

Platforms

About This Event

This market refers to the tennis match between Kaitlyn Carnicella and Lucciana Perez Alarcon in the W50 Lexington, SC, Main Draw, originally scheduled for October 9, 2026 at 10:30AM ET. This market will resolve to 'Kaitlyn Carnicella' if Kaitlyn Carnicella advances against Lucciana Perez Alarcon. This market will resolve to 'Lucciana Perez Alarcon' if Lucciana Perez Alarcon advances against Kaitlyn Carnicella. If the match is canceled (not played at all), ends in a tie, or a winner has not be

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:** Monday, October 12, 2026
**Context:** Prediction markets (Polymarket/Kalshi) are binary option markets where price (1-99) equals implied probability (1%-99%).

---

## 1. Event Snapshot
**Event:** W50 Lexington, SC: Kaitlyn Carnicella vs Lucciana Perez Alarcon
**Category:** Sports | **Status:** active
**Resolution Date:** Oct 16, 2026 (5 days left)
**Time Pressure:** High

**Description:** This market refers to the tennis match between Kaitlyn Carnicella and Lucciana Perez Alarcon in the W50 Lexington, SC, Main Draw, originally scheduled for October 9, 2026 at 10:30AM ET.

This market will resolve to 'Kaitlyn Carnicella' if Kaitlyn Carnicella advances against Lucciana Perez Alarcon.

This market will resolve to 'Lucciana Perez Alarcon' if Lucciana Perez Alarcon advances against Kaitlyn Carnicella.

If the match is canceled (not played at all), ends in a tie, or a winner has not be

**Resolution Criteria (Sample):**
"This market refers to the tennis match between Kaitlyn Carnicella and Lucciana Perez Alarcon in the W50 Lexington, SC, originally scheduled for October 9, 2026 at 10:30AM ET.
This market will resolve to "Kaitlyn Carnicella" if Kaitlyn Carnicella wins by 2 or more sets than Lucciana Perez Alarcon, ba"

## 2. Market liquidity & Volume
* **Total Volume:** $0
* **Total Liquidity:** $410
* **Activity Level:** Low Activity (Vol/Liq Ratio: 0.00)

## 3. Market Data (Prices & Odds)
### 1. Set Handicap: Kaitlyn Carnicella (-1.5) vs Lucciana Perez Alarcon (+1.5)
- **Polymarket** (End: Oct 16, 2026): Vol $0
  DATA: [Kaitlyn Carnicella: 34.5% (0.0%)] | [Lucciana Perez Alarcon: 65.5% (0.0%)]

---

## 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.

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