With a Return of Only 43 Cents per Dollar, Why Are 87% of Polymarket Players Losing Money?

Compiled by | Odaily (@OdailyChina); Translator | Asher (@Asher_ 0210)

On the Las Vegas Strip, the average return rate of a slot machine is about 93%, meaning for every $1 wagered, you get back an average of $0.93; yet on Polymarket, traders voluntarily accept returns as low as $0.43, using $1 to bet on outcomes with odds even worse than those in a casino.

This is not a metaphor, but based on real data. Researcher Jonathan Becker analyzed all settled markets on Kalshi, covering 72.1 million trades with a total volume of $18.26 billion. The patterns he discovered also apply to Polymarket—the same mechanisms, the same biases, and thus the same opportunities. The data’s conclusion is straightforward: approximately 87% of prediction market wallets end up losing money, but the remaining 13% don’t win by luck; they master a mathematical approach most traders aren’t even aware of.

This article will break down the 5 game theory formulas that separate winners from losers, each accompanied by its mathematical principle, real-world examples, and runnable Python code. Some traders already applying these methods in practice include:

• RN (Polymarket address: https://polymarket.com/profile/%40rn1): A Polymarket algorithmic trading bot that has achieved over $6 million in total profit in sports markets based on the models described here.

• distinct-baguette (Polymarket address: https://polymarket.com/profile/%40distinct-baguette): Turned $560 into $812,000 through market making in UP/DOWN markets.

1. Expected Value: The Core Formula

On Polymarket, every trade is essentially a judgment of expected value. Most traders rely on intuition, while the 13% of winners use mathematics to make decisions. Expected Value (EV) measures not a single outcome, but the average return over many repetitions, used to judge whether a trade is worth making.

Take a real market as an example: “Will Bitcoin reach $150,000 before June 2026?” The current YES price is 12¢, corresponding to a market-implied probability of 12%. If, based on on-chain data, halving cycles, ETF flows, and other factors, you judge the true probability to be about 20%, then this trade has positive expected value. According to this calculation, each contract bought at 12¢ yields, on average over the long term, an 8¢ profit; buying 100 contracts at a cost of $12 yields an expected profit of $8, a return rate of approximately +66.7%.

But data shows most prediction market traders don’t perform such calculations. In a sample covering 72 million trades, takers (market buyers) lost an average of about 1.12% per trade, while makers (limit order placers) profited an average of about 1.12% per trade. The gap between them isn’t about information, but patience—makers wait for positive EV opportunities, while takers are more prone to impulsive trading.

2. Mispricing: The Low-Price Contract Trap

“Longshot bias” is one of the most expensive mistakes in prediction markets. Traders systematically overestimate low-probability events, paying too high a price for seemingly cheap contracts. A contract priced at 5¢ theoretically should have a 5% win rate, but on Kalshi, the actual win rate is only 4.18%, corresponding to a -16.36% pricing bias. In more extreme cases, a 1¢ contract should have a 1% win rate, but for takers, the actual win rate is only 0.43%, a bias as high as -57%.

Looking at the overall distribution, the market is relatively accurate in the middle range (30¢–70¢), but shows clear bias at the extremes: contracts below 20¢ generally have actual win rates lower than their price-implied probability; contracts above 80¢ often have win rates higher than their price-reflected probability.

In other words, market inefficiency is concentrated at the extremes, which are precisely where emotional trading is most concentrated. Specifically, there are two formulas:

Formula 1: Mispricing (δ)

Mispricing measures the deviation between a contract’s actual win rate and its implied probability. Taking the 5¢ contract as an example: across all settled markets, assuming there were 100,000 trades executed at 5¢, of which 4,180 ultimately resulted in YES, the actual win rate is 4.18%, while the price-implied probability is 5.00%. The difference is -0.82 percentage points, a relative bias of about -16.36%. This means that for every 5¢ contract bought, you are essentially paying about a 16.36% premium.

Formula 2: Gross Excess Return per Trade (rᵢ)

If mispricing reflects overall bias, then gross excess return per trade reveals the actual return structure of each individual trade, and it’s here that behavioral bias becomes clearly visible. When buying a 5¢ contract, two outcomes are possible: if the contract hits, the profit can be +1900% (about a 20x return); if it misses, you lose 100%, and the 5¢ invested is completely lost.

This is precisely why “longshot bias” is so attractive—the payoff is extremely high if it hits, making it easy to remember, amplify, and spread. But overall, its actual hit rate is lower than the probability implied by the price, and the asymmetric structure between “total loss” and “extremely high gain” creates negative expected value over many trades, essentially equivalent to buying an overpriced lottery ticket.

Looking at the overall distribution, this bias has a clear price gradient: the lower the price, the worse the return. For example, as a taker, for every $1 invested in 1¢ contracts, you get back only about $0.43 on average; while on 90¢ contracts, for every $1 invested, you get about $1.02 on average. The cheaper the price, the worse the actual trading conditions.

Further breaking down roles reveals an almost mirror relationship: the losses takers incur in low-price ranges (up to -57%) correspond precisely to the profits makers earn in the same ranges; the overall market pricing bias lies between the two. In other words, almost every cent lost by takers is earned by makers.

From a game theory perspective, low-probability contracts are typically systematically overvalued, while high-probability contracts are often undervalued. The real strategy is not to chase longshots, but to sell longshots and buy high-certainty outcomes.

3. Kelly Criterion: How Much to Bet

When you find a trade with positive expected value, the real question just begins: how much should the trader bet? Bet too much, and one loss could wipe out weeks of gains; bet too little, and even with an edge, growth is so slow it’s almost meaningless. Between “all-in” and “betting nothing,” there exists a mathematically optimal bet size—this is the Kelly Criterion.

The Kelly Criterion was proposed by John Kelly Jr. in 1956, originally for optimizing signal-to-noise ratios in communications, later proven to be one of the most effective position sizing methods in gambling, trading, and even prediction markets. Professional poker players, sports betting experts, and Wall Street quant funds almost all use some form of Kelly strategy.

In prediction markets, because contracts are binary (resulting in $1 or $0) and the price itself represents probability, applying the Kelly Criterion is more direct. The key is understanding the odds (b): if you buy a YES contract at 30¢, you are essentially risking $0.30 to win $0.70, corresponding to odds of 0.70 / 0.30 ≈ 2.33; at a price of 50¢, the odds are 1; at 10¢, they are 9; at 80¢, only 0.25. The higher the odds, the larger the bet size Kelly suggests, assuming an edge exists.

But a key principle is not to use full Kelly. While mathematically, full Kelly maximizes long-term capital growth rate, in practice, its volatility is extreme, with drawdowns often exceeding 50%. It might yield the highest returns over a very long period, but the severe volatility along the way makes it difficult for most people to stick with. Therefore, a more common practice is to use fractional Kelly (e.g., 1/2 or 1/4 Kelly). For example, under stable win rate conditions, full Kelly yields the highest final equity curve but with wild swings; 1/4 Kelly grows more smoothly with controllable drawdowns; 1/2 Kelly lies in between.

Essentially, the Kelly Criterion provides a discipline: first, determine if an edge exists (i.e., subjective probability is higher than market-implied probability), and then decide how much capital to allocate. Only when both “whether to bet” and “how much to bet” are constrained by mathematics does trading truly evolve from gambling to strategy.

4. Bayesian Updating: Changing Your Mind Like an Expert

Prediction markets fluctuate because new information constantly enters. The key isn’t whether the initial judgment was correct, but how to adjust beliefs when evidence changes. Most traders either ignore new information or overreact, while Bayesian updating provides a mathematical method for “how much adjustment is reasonable.”

Its core logic can be simply understood as new judgment = (evidence support for original hypothesis × original judgment) ÷ overall probability of that evidence occurring. In practical application, it’s often expanded via the law of total probability into a more calculable form.

Take a typical market: “Will the Fed cut rates at the June meeting?” The current market price is 35¢, corresponding to a 35% probability, serving as the initial judgment. Then non-farm payroll data is released, showing only 120k new jobs (expected 200k), with rising unemployment and slowing wage growth. In this scenario, if the Fed were indeed to cut rates, the probability of seeing weak jobs data is higher, estimated at 70%; if it won’t cut, the probability of such data appearing is lower but still possible, estimated at 25%.

Applying Bayesian updating, the new probability becomes approximately 60.1%, meaning a one-time upward revision from 35% to 60.1%, an increase of about 25 percentage points. This shows that one key piece of information can significantly alter market judgment.

In practice, you don’t need to calculate the full formula every time. A more common method is the “likelihood ratio.” The same piece of information (e.g., LR = 3) has different impacts depending on the initial judgment: starting from 10%, it might increase to about 25%; from 50%, to 75%; from 90%, only to about 96%. The higher the uncertainty, the greater the impact of information.

Traders who consistently outperform prediction markets in the long run aren’t necessarily those with the “most accurate” initial judgments, but those who can adjust their judgments the fastest and most reasonably when new evidence appears. The Bayesian method essentially provides the scale for this “speed of adjustment.”

5. Nash Equilibrium: The “Poker Formula” in Prediction Markets

In poker, bluffing is never a gut decision but a strategy that can be precisely calculated. Theoretically, there exists an optimal bluffing frequency; deviating from it allows skilled opponents to exploit you. The same logic applies to prediction markets. On Polymarket, “bluffing” corresponds to contrarian trading—taking a position against the majority when market pricing shows bias; while “folding” is akin to being a passive taker, continuously paying a premium for market sentiment.

On Polymarket, makers and takers form a similar adversarial relationship. Contrarian trading (against market consensus) is akin to “bluffing,” while trend-following trading (following mainstream judgment) is akin to “value betting.” From an equilibrium perspective, the market should make marginal participants indifferent between “being a maker” and “being a taker,” a state corresponding to the Nash equilibrium in prediction markets.

But this equilibrium isn’t fixed; it adjusts dynamically with changes in participant structure. Data shows different market categories correspond to different optimal strategies: in areas with more rational information and efficient pricing (e.g., financial markets), contrarian opportunities are smaller; in areas with stronger emotions and more concentrated irrationality (e.g., entertainment, sports), markets are more prone to pricing bias, thus providing opportunities for contrarian trading.

More importantly, this equilibrium has also changed significantly over time. In the early days (2021–2023), takers were actually the profitable group, and the optimal strategy leaned towards active trading. However, after the trading volume explosion in Q4 2024, with the influx of professional market makers, the market structure changed, and the equilibrium strategy shifted towards being a maker (about 65%–70%). This is a classic game theory outcome: when participant structure changes, the optimal strategy evolves accordingly. Strategies effective in a “novice environment” may quickly become ineffective against “professional opponents,” and thus the market’s “playbook” constantly iterates.

Summary

87% of prediction market wallets end up losing money, not because the market is manipulated, but because these traders never truly do the math. They buy longshot contracts at worse prices than slot machines, decide position sizes by feel, ignore new information changes, and pay for “optimistic sentiment” with every market order.

The 13% of participants who consistently profit aren’t luckier; they use these 5 formulas as a complete methodology, forming a full process from judgment to execution, with each step built upon 72.1 million real trades of data.

This window won’t last forever. As professional market makers enter, market spreads are rapidly compressing. In 2022, takers had about a +2.0% edge; now it has turned into -1.12%.

The only question is: will you evolve with the market, or continue buying $1 lottery tickets for a $0.43 return?

This article is sourced from the internet: With a Return of Only 43 Cents per Dollar, Why Are 87% of Polymarket Players Losing Money?

Related: a16z: DeFi Struggles to Support Real Financial Markets

Original Authors: @PGarimidi, @jneu_net, @MaxResnick, a16z Original Compilation: Peggy, BlockBeats Editor’s Note: As blockchain performance continues to improve, the industry’s focus is shifting from “how many transactions can be processed” to “when will transactions be confirmed.” For a long time, performance has been simplified to throughput (TPS), but for financial systems, the more critical aspect is the predictability of transaction inclusion—that is, once a transaction is submitted, whether it can be timely and deterministically included in a block. This article points out that most blockchains currently only provide a guarantee of “eventual inclusion on-chain,” but the timing is uncontrollable. This second-level uncertainty is acceptable in payment scenarios, but in financial transactions requiring millisecond-level responses, it directly leads to price degradation, arbitrage opportunities, and unfairness, undermining the competitiveness of on-chain markets.…