Greater Manchester Mayoral Election Winner

According to the latest prediction market data for the query “Greater Manchester Mayoral Election Winner”, traders have formed a strong consensus.

Currently, Bev Craig is dominating the market with an overwhelming 91.5% chance of winning. Geraldine Coggins follows in second place at 21.5%, while Dan Barker sits in third with 4.1%. The betting volume for this specific market has already reached $390.7K, reflecting intense industry interest.

To better assess where each potential outcome stands, the market can be segmented into three distinct trading tiers based on implied probability and contract pricing:

🥇 Tier 1: The Dominant Leader

• Bev Craig (91.5%): Currently commanding the highest probability, Bev Craig is heavily favored by the order book. Traders looking to back this outcome face a “Buy Yes” contract price of 92¢, signaling a high degree of market conviction. This contract alone has generated $223.8K in volume.

🥈 Tier 2: The Primary Challengers

• Geraldine Coggins (21.5%): Positioned as the most viable alternative, Geraldine Coggins maintains a 21.5% chance of resolving true. Its “Buy Yes” shares currently trade at 22¢.
• Dan Barker (4.1%): Sitting in third place with a 4.1% probability, the market shows measured skepticism toward Dan Barker, treating it as an outside wildcard unless momentum shifts.

🥉 Tier 3: The Long-Tail Options (Combining for ~0%)

Beyond the top three choices, a wide field of macro variables and long-shot outcomes are being tracked. While their individual probabilities hover low, they represent crucial hedges for speculative traders:

• Alternative Options: This includes Laura Evans (1.7%), Kate Green (1.6%), and Jake Austin (0.6%).
• Speculative Volume: Despite low statistical likelihood, certain long-tail contracts like Paul Dennett are still attracting notable interest.

The 2026 Greater Manchester mayoral election is currently scheduled to be held on July 30, 2026.

This market will resolve according to the candidate who becomes the next Mayor of Greater Manchester as a result of this election.

Temporary, interim, or placeholder mayors appointed before the election will not be considered.

If the result of this election isn't known by June 30, 2027, 11:59 PM ET, the market will resolve to "Other".

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 Greater Manchester Combined Authority (https://www.gmelects.org.uk/).

While human consensus and speculative volume shape the broader prediction market, our quantitative algorithms offer a data-driven counter-perspective. By analyzing fundamental signals, underlying trends and historical distributions, our AI Valuation model calculates an independent “Fair Value” probability for each outcome.

Comparing this Fair Value against the current Trade Value uncovers major disparities — known as the Expected Value (EV) Gap. Contracts with a positive EV Gap represent statistically underpriced outcomes, whereas a negative EV Gap flags a potential market overreaction.

Top AI Alpha & Mispriced Arbitrage Opportunities

Based on the latest data model run, several key contracts stand out with significant deviations:

• The Most Overvalued Outcome Bev Craig currently trades at 91.5%, but our AI places its Fair Value at just 73.5%. This creates a large negative EV Gap of -18%, suggesting the crowd may be overhyping this outcome and driving the premium too high.
• The Best Value Play (Highest EV) Our model identifies Hannah Spencer as the premium value opportunity on the board. While the market only assigns it a 0.4% trading probability, our AI’s Fair Value assessment sits at 50.1% — yielding an impressive +49.7% EV Gap.
• Under-the-Radar Dark Horses Other notable discrepancies include Nick Buckley (EV Gap: +49.4%) and Paul Dennett (EV Gap: +43.7%). These long-tail opportunities are heavily discounted by the live order books despite stronger statistical backing from our predictive model.