United Russia seats in the next Russian legislative election?

According to the latest prediction market data for the query “United Russia seats in the next Russian legislative election?”, traders have formed a strong consensus.

Currently, 325–339 is dominating the market with an overwhelming 36% chance of winning. 310–324 follows in second place at 26.6%, while 340–354 sits in third with 21.5%. The betting volume for this specific market has already reached $382.6K, 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

• 325–339 (36%): Currently commanding the highest probability, 325–339 is heavily favored by the order book. Traders looking to back this outcome face a “Buy Yes” contract price of 36¢, signaling a high degree of market conviction. This contract alone has generated $38.1K in volume.

🥈 Tier 2: The Primary Challengers

• 310–324 (26.6%): Positioned as the most viable alternative, 310–324 maintains a 26.6% chance of resolving true. Its “Buy Yes” shares currently trade at 27¢.
• 340–354 (21.5%): Sitting in third place with a 21.5% probability, the market shows measured skepticism toward 340–354, treating it as an outside wildcard unless momentum shifts.

🥉 Tier 3: The Long-Tail Options (Combining for ~15.9%)

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 355+ (7%), 295–309 (5.1%), and <280 (3.3%).
• Speculative Volume: Despite low statistical likelihood, certain long-tail contracts like 280–294 are still attracting notable interest.

Legislative elections are scheduled to be held in Russia in September 2026.

This market will resolve according to the number of seats held by United Russia in the State Duma as a result of the next Russian legislative election.

If results are not known definitively by May 31, 2027, 11:59 PM ET, this market will resolve to the lowest bracket.

This market’s resolution will be based solely on the number of seats won by the named party in the State Duma.

This market will resolve based on the results of this election as indicated by a consensus of credible reporting. If there is ambiguity, this market will resolve based solely on the official results reported by Russian government sources such as the Central Election Commission of the Russian Federation.

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 310–324 currently trades at 26.6%, but our AI places its Fair Value at just 20.3%. This creates a large negative EV Gap of -6.3%, suggesting the crowd may be overhyping this outcome and driving the premium too high.
• The Best Value Play (Highest EV) Our model identifies 295–309 as the premium value opportunity on the board. While the market only assigns it a 5.1% trading probability, our AI’s Fair Value assessment sits at 15% — yielding an impressive +10% EV Gap.
• Under-the-Radar Dark Horses Other notable discrepancies include 280–294 (EV Gap: +0.7%) and 355+ (EV Gap: +0.6%). These long-tail opportunities are heavily discounted by the live order books despite stronger statistical backing from our predictive model.