Over the past few years, prediction markets — such as Polymarket, Kalshi, and others — have been rapidly gaining popularity, leveraging blockchain technology in some form to fundamentally upend the prediction and betting sector. Unlike traditional bookmakers, who hold full centralized control over fixed odds and probabilities, this new type of platform makes betting on outcomes significantly more democratic, allowing participants to freely trade contracts whose prices emerge purely from supply and demand.
As a result, contract prices on prediction markets not only represent a wider and dynamic sentiment of hundreds of thousands of users but can also be — arguably — viewed as a more precise forecast indicator since they lack the rigidness of traditional venues. At the same time, there are several caveats and extra conditions that should be taken into consideration, many of which can make price-based odds and probabilities less reliable.
In this article, we will compare these innovative platforms with their traditional counterparts, which is especially relevant considering the rapid rise of regulated prediction markets for both sports and non-sports events, and the growing practice of using one as a benchmark for the other.
Can Contract Prices Substitute Traditional Odds?
Unlike traditional sportsbooks, which operate based on set fixed odds with built-in margin and manage risk, prediction markets let participants trade binary “Yes/No” contracts whose prices emerge purely from supply and demand. Each of these contracts always comes in two counterparts — “Yes” and “No,” respectively — with different pricing, yet both halves must always amount to $1 combined.
In practice, if “Yes” contracts are priced at $0.65, their “No” equivalents will trade at $0.35. While prices could fluctuate, the $1 parity is usually maintained by markets’ self-regulation mechanisms, such as arbitrage or Polymarket’s smart Conditional Tokens Framework, for example.
When a corresponding event finally occurs, holders of “correct” contracts will receive $1 for each, while people who guessed wrong will lose their investments. So, if a “Yes” outcome occurs, corresponding holders will gain $0.35 in profit per each contract, if it’s a “No” — the profit will be $0.65 per contract for the winning side.
Unlike traditional bookmaking platforms, however, prediction markets’ contracts can be freely traded between participants or cashed out without too much of a penalty, making the whole system significantly more flexible and democratic. As a result, since prices of both “Yes” and “No” contracts are driven by the free market forces, they can also be perceived as a real-time reflection of broader market sentiment — in contrast with somewhat rigid bookmakers’ frameworks.
In essence, if a “Yes” contract’s price is $0.65, it signals a corresponding 65% probability of this outcome occurring, based not only on the community’s sentiment but also on deep analysis and math estimations made by larger and more professional players and entities. But can contract prices on prediction markets be considered more accurate than traditional odds?
Are Contracts Really More Accurate?
First of all, for this system to be as trustworthy as possible, platforms need to have deep liquidity, active participation and narrow bid-ask spreads. As prominent economists like Justin Wolfers and Eric Zitzewitz have been pointing out in their research papers over the years, prediction markets’ prices reflect money-weighted beliefs rather than average sentiments. Because of this, distortions may arise from risk aversion, budget constraints, limited participation, fees, and shallow liquidity.
Additionally, empirical calibrations are the strongest in high-volume markets, i.e. big-scale events like American presidential elections, for example. Conversely, smaller or niche events attract far fewer participants, resulting in smaller liquidity and a more limited range of money-backed opinions, especially near expiry or in thin markets.
The practical takeaway in this is that contract prices should be treated as a useful, real-time, capital-backed probability estimate rather than the “true” probability. While theoretically they can be considered somewhat more accurate because they encompass a wider range of opinions, there’s still a number of caveats that should be taken into account.
Where Do Distortions Arise?
Among those factors are bid-ask spreads and low liquidity, especially on secondary or long-shot markets, that make it harder for a prediction market to self-regulate and offer the most weighted odds possible. Then there are also explicit trading fees — such as per-contract, on winnings, or gas — that can also cause small distortions.
Price-based probabilities can also be somewhat skewed by behavioral biases like favorite-longshot preferences, emotional trading on high-profile events, insurance-demand fluctuations near expiry, and others. Further, big “whales” have a much bigger monetary impact on pricing compared to everyday users, so thin order books can be easily moved by large orders. Cross-market or parlay-style products can also be systematically overpriced.
For instance, a 2026 study of approximately 23 million Kalshi sports moneyline trades found that calibration changed as contracts approached settlement, showing that the market “is not simply biased” but also “the structure of its bias evolves predictably as expiry approaches.” It also found that “cross-game parlays on Kalshi are systematically overpriced relative to the product of their contemporaneous leg prices, with overpricing growing in leg count.” Another study showed that “Kalshi combo execution prices are ask quotes rather than direct probability assessments, so they are systematically upward biased by platform markup.”
Traditional sportsbooks are similarly not insulated from distortions. One of the bigger causes of this is the so-called vig, the built-in commission or fee that they charge for accepting a wager. It typically amounts to 4–6% overround on two-way markets, but may be higher on props or parlays, so raw implied probabilities can sum up to more than 100%.
Since bookmakers have full control over pricing, probabilities are also shaded toward public money or to balance the book’s liability rather than pure probability. Favorite-longshot bias, where longshots often carry disproportionate margins, is also a factor here, in addition to potential account limits, promotions, and risk-management adjustments that move prices away from strictly “fair.”
Meanwhile, both prediction markets and traditional bookmakers also share a number of similar issues, including information asymmetry, reaction speed to news, and calibration errors that grow in low-volume or exotic markets.
Key Differences Between Prediction Markets and Bookmakers
When it comes to structural differences, sportsbooks bake the margin into the quoted odds, thus participants will never see the “fair” line. On the other hand, prediction markets mostly keep prices summing up to nearly 100% and charge fees separately and transparently.
In terms of the effective cost, after de-vigging sportsbook odds and adding prediction markets’ fees and/or spreads, the all-in cost often ends up similar or slightly lower on prediction markets for liquid events — but not always, as favorites can favor sportsbooks while longshots can favor them after per-contract fees. In the end, gaps of 1–3 percentage points are common and run in both directions.
Other key dissimilarities include tradability, where prediction markets allow exits before resolution while sportsbooks generally lock the position and cash-outs are house-controlled. Prediction exchanges generally do not restrict successful traders in the same way sportsbooks may limit individual bettors, although exchanges can impose market- or position-level limits. Additionally, prediction markets offer increased transparency instead of opaque risk management mechanisms employed by traditional venues. Finally, sportsbooks usually dominate match-day props and in-play while prediction markets excel at longer-term or non-sports events.
Conclusion
In liquid markets, closing prices from both prediction platforms and sportsbooks are usually well-calibrated and perform similarly. After removing the vig and accounting for fees and spreads, neither side consistently produces superior prices. The main practical difference is often cost structure and transparency rather than pure forecasting accuracy.
Prediction-market prices remain a cleaner signal of collective probability because they do not embed a house margin and aggregate real capital from a broad range of participants. At the same time, they are not automatically better for finding value and their reliability still depends heavily on liquidity, total fees, market depth, and the specific event.
Overall, neither of these systems is a perfect probability meter. The most useful approach is to treat de-vigged sportsbook odds and mid-market prediction prices as complementary indicators, and if the two diverge meaningfully after adjustments, that divergence itself becomes valuable information worth investigating.
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