Market snapshot: Oct 12, 2026, 3:00 AM UTC. Prices may have changed since this snapshot.

GroupPOLYMARKET

Event GroupPOLYMARKETSports

1. FC Union Berlin vs. SV 07 Elversberg - Total Corners

Name: 1. FC Union Berlin vs. SV 07 Elversberg - Total Corners
End: 2026-10-10T13:30:00.000Z

Total Volume

$40.84K

Events

Markets

Trade on Polymarket

1. FC Union Berlin vs. SV 07 Elversberg - Total Corners

Vol

$40.84K

Events

Markets

Polymarket

Platform StatsQuick Platform Comparison

Polymarket

markets · $40.84K

Kalshi

markets · $0.00

Price Spread

0.0%avg

max 0.0%

Arbitrage

of 0 cross-platform

Volume DistributionTotal: $40.84K

100%

Polymarket leads

Price ChartMatched Markets Price Graph

1 market tracked

1. FC Union Berlin vs. SV 07 Elversberg: Team to Take First Corner

No data available

Single-Platform Markets(1)

Market	Platform	Price
1. FC Union Berlin vs. SV 07 Elversberg: Team to Take First Corner	Poly	50%

AI Analysis

Trader mode: Actionable analysis for identifying opportunities and edge

50%

Top Probability

$40.84K

Volume

Markets

Platforms

About This Event

Total corners markets for the Bundesliga game between 1. FC Union Berlin and SV 07 Elversberg, scheduled for October 10, 2026 at 9:30 AM ET.

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

Custom AI Analysis Prompt

Tap to expand

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%).

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## 1. Event Snapshot
**Event:** 1. FC Union Berlin vs. SV 07 Elversberg - Total Corners
**Category:** Sports | **Status:** active
**Resolution Date:** Oct 10, 2026 (Ended)
**Time Pressure:** Expired

**Description:** Total corners markets for the Bundesliga game between 1. FC Union Berlin and SV 07 Elversberg, scheduled for October 10, 2026 at 9:30 AM ET.

**Resolution Criteria (Sample):**
"In the upcoming Bundesliga game between 1. FC Union Berlin and SV 07 Elversberg, scheduled for October 10, 2026 at 9:30 AM ET: This market will resolve to the team that takes the first corner in the game within the first 90 minutes of regular play plus stoppage time. If no corner is taken within tha"

## 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. 1. FC Union Berlin vs. SV 07 Elversberg: Team to Take First Corner
- **Polymarket** (End: Oct 10, 2026): Vol $0
  DATA: [1. FC Union Berlin: 50.0% (0.0%)] | [SV 07 Elversberg: 50.0% (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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