The mystery of prediction market pricing discrepancies: Kalshi 42% vs Polymarket 23%, the truth lies not in users but in the rules

Uniswap founder Hayden Adams recently posted on social media, providing an in-depth analysis of the pricing differences in the prediction market for the “U.S. acquisition of Greenland” event. The Kalshi platform estimates the probability at approximately 42%, while Polymarket’s estimate is only 15%-23%. This significant price discrepancy has attracted market attention. Adams’ analysis points out that the root cause of this difference is not due to variations in user groups, but rather differences in event definitions, rule conditions, and other multi-dimensional factors.

The Truth Behind the Price Discrepancy

Why isn’t it a user structure issue

On the surface, the price difference of up to 20 percentage points for the same event on two platforms might suggest it is due to different trader demographics. However, Adams indicates that this logic is flawed. If it were solely due to user structure differences, arbitrageurs who can access both platforms simultaneously could quickly eliminate this price gap. Such risk-free arbitrage opportunities would not persist long-term. Since the price difference continues, the fundamental issue lies not with the users but with the event itself.

The key difference in event definition timeframes

The core difference revealed by Adams is in the time frame. Polymarket’s probability refers to “the likelihood of the event occurring within 2026” (currently about 23%), whereas Kalshi’s refers to “the probability of Trump’s entire term” (currently about 45%). This seemingly subtle difference in time scope actually changes the entire approach to calculating the event’s probability.

An event that occurs within 2026 naturally has a lower probability than one that occurs over an entire term (which may span multiple years). This is not a matter of different pricing for the same event on two platforms, but rather pricing two fundamentally different events.

Multi-dimensional rule differences

In addition to the time frame difference, Adams also points out other possible sources of discrepancy:

• Question phrasing: The specific wording of the event description may differ between platforms
• Settlement conditions: Standards for determining whether the event has “occurred” may vary
• Oracle design: The mechanisms used to verify the event may differ
• Risk pricing: The logic used by each platform to price market risk may differ

These seemingly technical differences can have a significant impact on pricing in prediction markets.

Implications for Prediction Markets

This case reveals that prediction markets, while seemingly simple, are actually quite complex. The same real-world event priced differently across platforms is often not a sign of market inefficiency, but a rational response by market participants to different contract rules.

Adams’ analysis highlights the importance of market design. The contract’s time frame, settlement conditions, oracle selection, and other seemingly minor design choices can fundamentally influence price formation. This provides valuable insight for both users and designers of prediction markets: when comparing prices across platforms, it is essential to understand each platform’s specific rule definitions rather than assuming that price differences automatically indicate arbitrage opportunities.

Summary

The pricing differences between Kalshi and Polymarket regarding the Greenland event do not reflect market inefficiency or user group differences. Instead, they stem from fundamental differences in how each platform defines the event. Polymarket focuses on the shorter-term probability of occurrence within 2026, while Kalshi considers the probability over Trump’s entire term, leading to entirely different pricing logic. This case reminds us that understanding prediction markets depends not only on observing prices but also on understanding the rules—rules determine the meaning of prices.