Lowest temperature in Hong Kong on July 30?

According to the latest prediction market data for the query “Lowest temperature in Hong Kong on July 30?”, traders have formed a strong consensus.

Currently, 25°C is dominating the market with an overwhelming 46% chance of winning. 24°C follows in second place at 44.1%, while 23°C sits in third with 9.4%. The betting volume for this specific market has already reached $59.5K, 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

• 25°C (46%): Currently commanding the highest probability, 25°C is heavily favored by the order book. Traders looking to back this outcome face a “Buy Yes” contract price of 46¢, signaling a high degree of market conviction. This contract alone has generated $15.5K in volume.

🥈 Tier 2: The Primary Challengers

• 24°C (44.1%): Positioned as the most viable alternative, 24°C maintains a 44.1% chance of resolving true. Its “Buy Yes” shares currently trade at 44¢.
• 23°C (9.4%): Sitting in third place with a 9.4% probability, the market shows measured skepticism toward 23°C, treating it as an outside wildcard unless momentum shifts.

🥉 Tier 3: The Long-Tail Options (Combining for ~0.6%)

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 22°C (0.2%), 21°C or below (0.1%), and 26°C (0.1%).
• Speculative Volume: Despite low statistical likelihood, certain long-tail contracts like 27°C are still attracting notable interest.

This market will resolve to the temperature range that contains the lowest temperature recorded by the Hong Kong Observatory in degrees Celsius on 30 Jul '26.

The resolution source for this market will be information from the Hong Kong Observatory, specifically the "Absolute Daily Min (deg. C)" the specified date once information is finalized in the relevant "Daily Extract", available here: https://www.weather.gov.hk/en/cis/climat.htm

This market can not resolve until data for this date has been published.

The resolution source for this market measures temperatures in Celsius to one decimal place (eg, 9.1°C). Thus, this is the level of precision that will be used when resolving the market.

Any revisions to temperatures recorded after data is initially published for this market's timeframe will not be considered for this market's resolution.

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 24°C currently trades at 44.1%, but our AI places its Fair Value at just 4.4%. This creates a large negative EV Gap of -39.7%, suggesting the crowd may be overhyping this outcome and driving the premium too high.
• The Best Value Play (Highest EV) Our model identifies 26°C as the premium value opportunity on the board. While the market only assigns it a 0.1% trading probability, our AI’s Fair Value assessment sits at 53.1% — yielding an impressive +53% EV Gap.
• Under-the-Radar Dark Horses Other notable discrepancies include 30°C (EV Gap: +14.2%) and 27°C (EV Gap: +13.9%). These long-tail opportunities are heavily discounted by the live order books despite stronger statistical backing from our predictive model.