Deep Dive into 290,000 Market Data Points: Revealing 6 Truths About Polymarket Liquidity

Author: Frank, PANews

Previously, PANews conducted in-depth research on strategies in prediction markets, one of the key findings being: whether many arbitrage strategies can succeed is perhaps less about the mathematical formulas of the strategies themselves and more about the liquidity depth of the prediction markets.

Recently, after Polymarket announced the launch of the U.S. real estate prediction market, this phenomenon seems to have become even more apparent. Since going live, the daily trading volume of these markets has only been a few hundred dollars, far from the expected lively activity. The actual market activity is much lower than the discussions on social media. This seems amusing and abnormal, so it may be necessary to conduct a comprehensive investigation into the liquidity of prediction markets to reveal some truths about liquidity within these markets.

PANews retrieved historical data from 295,000 markets on Polymarket and derived the following results.

1. Short-term markets: a PVP battleground comparable to MEME coins

Among the 295,000 markets, 67,700 have a cycle of less than 1 day, accounting for 22.9%, and 198,000 have a cycle of less than 7 days, accounting for 67.7%.

Within these ultra-short-term prediction events, 21,848 are currently ongoing markets, of which 13,800 have a 24-hour trading volume of zero, accounting for about 63.16%. In other words, on Polymarket, a large number of short-term markets are in a state of no liquidity.

Does this situation seem familiar?

During the peak of MEME coin frenzy, thousands of MEME coins were issued on the Solana chain, most of which were ignored or quickly faded away.

Currently, this state is also being recreated in prediction markets, but compared to MEME coins, the event lifecycle in prediction markets is certain, whereas the lifecycle of MEME coins is unknown.

In terms of liquidity, more than half of these short-term events have less than $100 in liquidity.

In categories, these short-term markets are almost entirely dominated by sports and crypto predictions. The main reason is that these events have relatively simple and mature judgment mechanisms, such as a token’s 15-minute price change or a team’s victory. However, perhaps due to the poor liquidity compared to crypto derivatives, crypto categories are not the hottest “short-term kings.”

Sports events, on the other hand, dominate completely. Analysis shows that the average trading volume for sports events with a cycle of less than 1 day on Polymarket reaches $1.32 million, while crypto events average only $44,000. This also indicates that if you hope to profit by predicting short-term movements of cryptocurrencies in prediction markets, there may not be enough liquidity to support it.

2. Long-term markets: a pool for big funds

Compared to the numerous short-term event contracts, markets with longer durations are much fewer.

On Polymarket, markets with a cycle of 1 to 7 days number 141,000, while those longer than 30 days only total 28,700. However, these long-term markets accumulate the most funds. The average liquidity for markets longer than 30 days reaches $450,000, while those within 1 day have only about $10,000 in liquidity. This indicates that larger funds prefer to position themselves in long-term predictions rather than participate in short-term battles.

In long-term markets (over 30 days), aside from sports, other categories show higher average trading volumes and liquidity. The most popular category for funds is U.S. politics, with an average trading volume of $28.17 million and an average liquidity of $811,000. The “Others” category also attracts significant capital, with an average liquidity of $420,000 (covering topics like pop culture, social issues, etc.).

In crypto prediction markets, funds also tend to favor long-term bets, such as whether Bitcoin will surpass $150,000 by year-end or if a certain token’s price will fall below a specific level in a few months. In prediction markets, crypto predictions are more like simple options hedging tools rather than short-term speculative instruments.

3. Polarization in sports markets

Sports predictions are currently one of the main sources of daily active users on Polymarket, with about 8,698 active markets, roughly 40%. However, the trading volume distribution shows a huge gap across different cycles. On one hand, ultra-short-term predictions of less than 1 day have an average trading volume of $1.32 million; on the other hand, mid-term markets (7–30 days) average only $400,000, while long-term markets (over 30 days) reach an average of $16.59 million.

From these data, it appears that users participating in sports predictions on Polymarket are either seeking “immediate results” or engaging in “season-long bets,” with mid-term event contracts being less popular.

4. Real estate predictions face “adaption issues”

After extensive data analysis, a superficial conclusion is that longer-term prediction events seem to have better liquidity. However, when this logic is applied to specific or more niche categories, the trait sometimes fails. For example, the real estate prediction market, which is a relatively high-certainty, over-30-day cycle market, is an example. Conversely, predictions like the 2028 U.S. presidential election outperform in liquidity and trading volume across the entire market.

This may reflect the “cold start dilemma” faced by new asset classes (especially niche, highly specialized categories). Unlike simple event predictions, real estate market participation requires higher professionalism and cognitive effort. Currently, the market still appears to be in a “strategy refinement period,” with retail participation mainly watching from the sidelines. Moreover, the inherently low volatility of real estate markets further exacerbates this cold start problem—without frequent event-driven fluctuations, speculative interest diminishes. Under these combined factors, such relatively niche markets face the awkward situation where professional players have no counterparties, and amateurs dare not enter.

5. “Short-term” or “Long-term accumulation”?

Based on the above analysis, we can categorize prediction markets into two types: markets like cryptocurrencies and sports, which are ultra-short-term, can be called short-term markets; while categories like politics, geopolitics, and technology tend to be more long-term, accumulation markets.

Behind these two types of markets are different investor groups. Short-term markets are more suitable for small capital or those needing higher capital turnover. “Accumulation” markets are better suited for large capital and higher certainty.

However, when dividing markets by trading volume, markets with the capacity for capital accumulation (over $10 million) account for 47% of total trading volume, despite having the fewest contracts—only 505. Conversely, markets with trading volumes between $100,000 and $1 million constitute the majority in number, with 156,000 contracts, but only account for 7.54% of total volume. For most prediction contracts lacking top-tier narrative power, “going live and then zeroing out” is the norm. Liquidity is not evenly distributed; it concentrates around a few super-events like spotlights.

6. The “Geopolitics” sector is rising

From the “current active / historical total” ratio, we can see the growth momentum of a category. The fastest-growing sector is undoubtedly “geopolitics”: there are only 2,873 historical event contracts, but 854 are currently active, with an active ratio of 29.7%, the highest among all sectors.

This data indicates that the number of new “geopolitical” contracts is rapidly increasing, making it one of the most concerned topics among prediction market users. This is also reflected in recent frequent insider address disclosures related to “geopolitical” contracts.

Overall, behind the liquidity analysis of prediction markets, whether it’s the “high-frequency casino” sports sector or the “macro hedge” political sector, the core of capturing liquidity lies in either providing instant dopamine feedback or offering deep macro strategic space. Markets lacking narrative density, with feedback cycles that are too long and no volatility, are destined to struggle to survive in a decentralized order book.

For participants, Polymarket is evolving from a “predict everything” utopia into an extremely professional financial tool. Recognizing this is more important than blindly searching for the next “100x prediction.” In this arena, only places with abundant liquidity will have value discovered; where liquidity is scarce, only traps remain.

This may be the greatest truth about prediction markets that data reveals.