Expected Value Trading on Polymarket
Master the concept of expected value to make consistently profitable decisions on Polymarket prediction markets.
1What Is Expected Value and Why It Matters
Expected value (EV) is the average outcome of a bet if it were repeated many times. It is calculated by multiplying each possible outcome by its probability and summing the results. For a Polymarket trade, EV = (probability of winning * profit if win) - (probability of losing * loss if lose). A positive EV trade is one where, over many repetitions, you expect to make money. A negative EV trade loses money over time.
Expected value is the foundational concept in profitable prediction market trading. Every trade you make should have positive expected value based on your probability estimate. If the market prices an outcome at 60% but you believe the true probability is 70%, buying Yes shares has positive EV because you are paying $0.60 for something worth $0.70 in expectation. The $0.10 difference is your expected profit per share.
The power of EV thinking is that it separates the quality of a decision from its outcome. A positive EV trade can still lose (you can be right that the probability is 70% and still hit the 30% losing scenario), but if you consistently make positive EV trades, the law of large numbers ensures you will be profitable over time. Professional traders focus on making good decisions, not on individual outcomes.
2Calculating Expected Value on Polymarket
The EV calculation for a Polymarket trade is straightforward. If you buy Yes shares at price c and your estimated probability of Yes is p, then: EV per share = p * (1 - c) - (1 - p) * c = p - c. This simplifies to your probability estimate minus the market price. If p > c, the trade has positive EV. The magnitude (p - c) tells you how much edge you have per share.
For example, if the market price is $0.45 and you estimate the true probability at 55%, your EV per share is 0.55 - 0.45 = $0.10, or 10 cents per share. On a $100 position (222 shares at $0.45), your expected profit is $22.20. Of course, the actual outcome will be either a $122 profit (if Yes) or a $100 loss (if No), but across many similar trades, you expect to earn roughly $22 per trade.
Always calculate EV before entering a trade and record it. Over time, comparing your calculated EV to your actual realized returns tells you whether your probability estimates are well-calibrated. If you consistently calculate 10% EV but only realize 5%, your probability estimates may be systematically biased.
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Get Started Free3Finding Positive EV Opportunities
Positive EV opportunities arise when the market price differs from the true probability. Finding these opportunities requires either superior information, superior analysis, or the ability to identify market inefficiencies. Superior information might mean accessing primary sources or expert opinions that other traders have not yet incorporated. Superior analysis might mean applying a more rigorous statistical model than the consensus.
Market inefficiencies often appear in specific patterns. New markets tend to be less efficient than established ones. Markets with low liquidity are less efficient than highly traded ones. Markets experiencing sudden volume spikes due to news events often temporarily overshoot fair value. Markets near extreme prices ($0.05 or $0.95) may have skewed risk-reward due to the binary nature of outcomes.
Build a pipeline of markets to analyze regularly. Rather than waiting for opportunities to find you, systematically review markets in categories where you have expertise. Compare market prices to your independent assessments and flag any where you see at least a 5-10% edge. This systematic approach ensures you capture more opportunities than a passive one.
Pro Tip: Track Your Calibration
Record your probability estimates for every trade and compare them to actual outcomes over time. If events you rate at 70% actually occur 60% of the time, you are overconfident and need to adjust. Good calibration is the key to finding genuine positive EV opportunities.
4EV Thinking Beyond Individual Trades
Apply expected value thinking to your overall trading practice, not just individual trades. Consider the EV of the time you spend researching markets, the EV of different trading strategies, and the EV of using tools like PredictEngine to automate your execution. If spending an extra hour researching a market improves your probability estimate by 2% on a $500 position, that hour of research has an EV of $10.
Think about opportunity cost in EV terms. Capital allocated to one position cannot be used for another. If you have $1,000 and two potential trades, one with 5% EV and one with 15% EV, allocating capital to the lower EV trade costs you the difference. Rank your opportunities by EV and allocate capital to the highest EV trades first.
Long-term profitability on Polymarket comes from consistently finding and sizing positive EV trades. Even small edges compound over time. A trader who makes 50 trades per month with an average 3% edge per trade will be consistently profitable. Focus on the process of finding and executing positive EV trades, and the results will follow.
Frequently Asked Questions
Can a negative EV trade still win?
Yes. A negative EV trade can win on any individual occurrence. However, if you repeatedly make negative EV trades, you will lose money over time. This is why casinos are profitable despite many individual gamblers winning on any given night.
How do I estimate the true probability of an event?
Use multiple information sources, historical data, statistical models, and expert opinions. For well-studied events like elections, academic forecasting models can provide probability estimates. For less studied events, triangulate from related data and base rates.
What edge do I need to be profitable after fees?
Your expected edge must exceed your total trading costs (fees, spreads, slippage). On Polymarket, trading fees are typically 1-2%, so you need at least a 2-3% edge per trade to be profitable after costs. Higher edges provide more buffer for estimation errors.