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 for predicting markets, one of the key findings being: whether many arbitrage strategies can succeed may not be limited by the mathematical formulas of the strategies themselves, but by the liquidity depth of the prediction markets.

Recently, after Polymarket announced the launch of the US real estate prediction market, this phenomenon seems to have become even more apparent. Since going live, the daily trading volume of this series of markets has only been a few hundred dollars, far from the lively scene expected, and much less active than discussions on social media. This seems to be a humorous and abnormal situation, 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 analyzed the historical data of 295,000 markets on Polymarket and derived the following results.

1. Short-term Markets: PVP Battlegrounds Comparable to MEME Coins Among the 295,000 markets, 67,700 have cycles shorter than 1 day, accounting for 22.9%, and 198,000 have cycles shorter than 7 days, accounting for 67.7%.
   Within these ultra-short-term prediction events, 21,848 are currently ongoing markets, with 13,800 having a 24-hour trading volume of zero, accounting for approximately 63.16%. In other words, on Polymarket, there are many short-term markets 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 failed.
   Currently, this state is also being recreated in prediction markets, but compared to MEME coins, the event life cycle in prediction markets is definite, whereas the life cycle of MEME coins is unknown.
   In terms of liquidity, more than half of these short-term events have less than $100 in liquidity.
   Categorically, these short-term markets are almost entirely dominated by sports and crypto market predictions. The main reason is that these events have relatively simple and mature judgment mechanisms, usually involving questions like a token’s 15-minute price change or which team will win. However, perhaps because the liquidity is so poor compared to crypto derivatives, the crypto category is not the hottest “short-term king.”
   Sports events, on the other hand, dominate completely. Analysis shows that the average trading volume for sports-related prediction cycles under 1 day on Polymarket reaches $1.32 million, while crypto-related ones only reach $44,000. This also means 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 of Large Capital Compared to the numerous short-term event contracts, markets with longer time horizons are much fewer.
   On Polymarket, markets with a cycle of 1~7 days total 141,000, while those longer than 30 days only number 28,700. However, these long-term markets have accumulated the most capital. The average liquidity for markets longer than 30 days reaches $450,000, while those within 1 day have only about $10,000. This indicates that large capital prefers to position itself 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 market category is US politics, with an average trading volume of $28.17 million and an average liquidity of $811,000. The “Others” category, which includes pop culture, social topics, etc., also attracts good capital, with an average liquidity of $420,000.
   In crypto prediction markets, funds also tend to favor long-term strategies, such as predicting whether Bitcoin will break $150,000 by the end of the year or whether a token’s price will fall below a certain level within 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, accounting for roughly 40%. However, based on the distribution of trading volume, there are huge differences among sports markets with different cycles. On one hand, ultra-short-term predictions under 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 ultra-long-term markets (over 30 days) reach an average of $16.59 million.
   From these data, participation in sports predictions on Polymarket either seeks “immediate results” or involves “season-long bets,” with mid-term event contracts being less popular.

4. Real Estate Predictions Face “Mismatch” After extensive data analysis, a superficial result is that longer-term prediction events seem to have better liquidity. However, when this logic is applied to specific or more subdivided categories, this trait sometimes fails. For example, the real estate prediction market mentioned earlier is a relatively high-certainty market with a cycle longer than 30 days. Conversely, predictions like the 2028 US presidential election result lead the market in both liquidity and trading volume.
   This may reflect the “cold start dilemma” faced by new asset classes (especially niche and highly specialized categories). Unlike simple event predictions, real estate market participants require higher levels of expertise and cognition. Currently, the market still appears to be in a “strategy calibration period,” with retail participation limited to spectators. Naturally, the low volatility inherent in real estate markets further exacerbates this cold start problem—without frequent event-driven volatility, speculative enthusiasm diminishes. Under these combined factors, such relatively niche markets face an 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 reclassify prediction markets: markets like cryptocurrencies and sports, which are ultra-short-term, can be called short-term markets, while categories like politics, geopolitics, and technology lean more toward 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. Conversely, “accumulation” markets are better suited for those with 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 only having 505 contracts. Markets with trading volumes between $100,000 and $10 million constitute the majority in number, with 156,000 contracts, but only account for 7.54% of total trading volume. For most prediction contracts lacking top-tier narratives, “going live and then zeroing out” is the norm. Liquidity is not evenly distributed like sunlight but concentrates around a few ultra-rare events.

6. The Rise of the “Geopolitics” Sector From the “current active number / historical number” ratio, we can see the growth momentum of a category. Currently, the fastest-growing sector is undoubtedly “geopolitics.” There are only 2,873 historical event contracts in geopolitics, but 854 are active, with an active ratio as high as 29.7%, the highest among all sectors.
   This data indicates that the number of new contracts in the “geopolitics” category is rapidly increasing, making it one of the topics most concerned by prediction market users. This can also be glimpsed from recent frequent insider addresses exposed in several geopolitics-related contracts.

Overall, behind the liquidity analysis of prediction markets, whether it is the “high-frequency casino” sports sector or the “macro hedge” political sector, their ability to capture liquidity depends on either providing immediate dopamine feedback or offering deep macro strategic space. Markets lacking narrative density, with feedback cycles that are too long and lacking 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 seeking the next “100x prediction.” In this arena, only places with abundant liquidity will have value discovered; where liquidity is scarce, traps await.
This may be the greatest truth about prediction markets that data reveals to us.