GroupPOLYMARKET

Event GroupPOLYMARKETPolitics

Oakland Mayoral Election

Name: Oakland Mayoral Election
End: 2026-11-03T23:59:00.000Z

Total Volume

$1.29K

Events

Markets

Trade on Polymarket

Oakland Mayoral Election

Vol

$1.29K

Events

Markets

Polymarket

Platform StatsQuick Platform Comparison

Polymarket

markets · $1.29K

Kalshi

markets · $0.00

Price Spread

0.0%avg

max 0.0%

Arbitrage

of 0 cross-platform

Volume DistributionTotal: $1.29K

100%

Polymarket leads

Price ChartMatched Markets Price Graph

2 markets tracked

Will Barbara Lee win the Oakland Mayor election?

No data available

Single-Platform Markets(2)

Market	Platform	Price
Will Barbara Lee win the Oakland Mayor election?	Poly	90%
Will Brenda Grisham win the Oakland Mayor election?	Poly	7%

AI Analysis

Trader mode: Actionable analysis for identifying opportunities and edge

90%

Top Probability

$1.29K

Volume

Markets

Platforms

About This Event

The 2026 Oakland mayoral election will be held on November 3, 2026, to elect the mayor of Oakland, California. This market will resolve according to the candidate that wins the election. The primary resolution source for this market will be a consensus of credible reporting; however, if there is any ambiguity in the results, this market will resolve according to official information from the City of Oakland.

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:** Saturday, September 5, 2026
**Context:** Prediction markets (Polymarket/Kalshi) are binary option markets where price (1-99) equals implied probability (1%-99%).

---

## 1. Event Snapshot
**Event:** Oakland Mayoral Election
**Category:** Politics | **Status:** active
**Resolution Date:** Nov 3, 2026 (60 days left)
**Time Pressure:** Low

**Description:** The 2026 Oakland mayoral election will be held on November 3, 2026, to elect the mayor of Oakland, California. 

This market will resolve according to the candidate that wins the election.

The primary resolution source for this market will be a consensus of credible reporting; however, if there is any ambiguity in the results, this market will resolve according to official information from the City of Oakland.

**Resolution Criteria (Sample):**
"The 2026 Oakland mayoral election will be held on November 3, 2026, to elect the mayor of Oakland, California. 

This market will resolve according to the candidate that wins the election.

The primary resolution source for this market will be a consensus of credible reporting; however, if there is "

## 2. Market liquidity & Volume
* **Total Volume:** $1.3K
* **Total Liquidity:** $18.8K
* **Activity Level:** Low Activity (Vol/Liq Ratio: 0.07)

## 3. Market Data (Prices & Odds)
### 1. Will Brenda Grisham win the Oakland Mayor election?
- **Polymarket** (End: Nov 3, 2026): Vol $144
  DATA: [Yes: 7.0% (0.0%)] | [No: 93.0% (0.0%)]

### 2. Will Barbara Lee win the Oakland Mayor election?
- **Polymarket** (End: Nov 3, 2026): Vol $1.1K
  DATA: [Yes: 89.5% (0.0%)] | [No: 10.5% (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.

POLYMARKETPolitics