Which states will Donald Trump visit in 2026?

According to the latest prediction market data for the query “Which states will Donald Trump visit in 2026?”, traders have formed a strong consensus.

Currently, Minnesota is dominating the market with an overwhelming 70.5% chance of winning. Louisiana follows in second place at 53.5%, while Utah sits in third with 51.5%. The betting volume for this specific market has already reached $338.8K, 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

• Minnesota (70.5%): Currently commanding the highest probability, Minnesota is heavily favored by the order book. Traders looking to back this outcome face a “Buy Yes” contract price of 71¢, signaling a high degree of market conviction. This contract alone has generated $3.0K in volume.

🥈 Tier 2: The Primary Challengers

• Louisiana (53.5%): Positioned as the most viable alternative, Louisiana maintains a 53.5% chance of resolving true. Its “Buy Yes” shares currently trade at 54¢.
• Utah (51.5%): Sitting in third place with a 51.5% probability, the market shows measured skepticism toward Utah, 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 Indiana (51%), New Mexico (43%), and Washington (42%).
• Speculative Volume: Despite low statistical likelihood, certain long-tail contracts like Missouri are still attracting notable interest.

If U.S. President Donald Trump visits the listed state between January 1 and December 31, 2026, 11:59 PM ET, this market will resolve to "Yes". Otherwise, this market will resolve to "No".

For the purpose of this market, a "visit" is defined as Trump physically entering the terrestrial territory of the listed state. Whether or not Trump enters the state’s airspace during the timeframe of this market will have no bearing on a positive resolution.

The primary resolution source for this information will be official information from the government of the United States of America, official information from Trump, or released by his verified social media accounts (e.g., https://twitter.com/POTUS); however, a consensus of credible reporting will also be used.

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 Minnesota currently trades at 70.5%, but our AI places its Fair Value at just 39.2%. This creates a large negative EV Gap of -31.3%, suggesting the crowd may be overhyping this outcome and driving the premium too high.
• The Best Value Play (Highest EV) Our model identifies Missouri as the premium value opportunity on the board. While the market only assigns it a 40.5% trading probability, our AI’s Fair Value assessment sits at 70.1% — yielding an impressive +29.6% EV Gap.
• Under-the-Radar Dark Horses Other notable discrepancies include Massachusetts (EV Gap: +16.8%) and Hawaii (EV Gap: +12.2%). These long-tail opportunities are heavily discounted by the live order books despite stronger statistical backing from our predictive model.