Polymarket Fair Value & Trading Bot Probability Model

This article explains how to build a probability model for a Polymarket trading bot that estimates an event’s fair value and identifies mispriced markets. You’ll learn the core components—data collection, probability aggregation, and signal generation—and how to structure a bot that acts on those signals. The goal is a clear, factual framework you can implement, not a get-rich-quick scheme.

Formula

The fair value of a binary Polymarket share is simply your estimated probability that the event occurs, expressed as a price between $0 and $1. The formula is:

Fair Value (FV) = P(event occurs)

Where P(event occurs) is your model’s probability estimate, a number from 0 to 1. The market price is what other traders are paying. Your edge is the difference:

Edge = FV – Market Price

If your edge is positive, the market is underpriced relative to your model. If negative, it’s overpriced. The expected value per share bought at the market price is:

EV = (FV × $1) – Market Price

Because a winning share pays $1, your profit is the difference between the payout and what you paid, multiplied by your probability of winning.

Worked Example

Using the defaults from the calculator above: time to expiry is 7 days, current market price is $0.45, and your model estimates a 60% probability.

1. Convert your probability to a price: 60% → $0.60.
2. Fair value = $0.60.
3. Edge = $0.60 – $0.45 = $0.15.
4. Expected value per share = (0.60 × $1) – $0.45 = $0.15.

So the market is underpriced by $0.15 per share according to your model. If you buy at $0.45, your expected profit per share is $0.15, assuming your probability estimate is accurate.

Why Build a Probability Model for Polymarket?

Polymarket is a decentralized prediction market where users buy and sell shares in binary outcomes (e.g., “Will X happen by date Y?”). Each share pays $1 if the event occurs, $0 otherwise. The market price reflects the crowd’s consensus probability. But that consensus is often wrong—due to biases, slow information diffusion, or manipulation. A probability model gives you an independent estimate, letting you spot mispriced shares.

For a trading bot, the model is the brain. Without it, you’re guessing. With it, you can automate decisions: buy when the market price is below your fair value by a threshold, sell when above. The model also helps you size positions and manage risk, since not all probabilities are equally confident.

Core Components of a Probability Model

A robust model typically combines several data sources and methods. Here are the key building blocks:

• Base rate data: Historical frequencies of similar events. For example, if the event is “Will it rain in London on July 15?”, the base rate is the historical proportion of days with rain on that date.
• Current information: Real-time data like polls, betting odds, weather forecasts, or economic indicators. For sports, that might be team statistics; for politics, polling averages.
• Market-derived signals: Other prediction markets, betting exchanges, or even Polymarket’s own prices from other related markets. These can be blended with your independent estimates.
• Model type: You can use a simple weighted average, a logistic regression, a Bayesian update, or a full machine learning model. The complexity depends on the event type and your data.

The output is always a probability between 0 and 1. The challenge is choosing the right inputs and weighting them correctly.

How to Build the Model Step by Step

Here’s a practical workflow for creating a probability model that a bot can use:

1. Define the event clearly: The event must be binary and unambiguous. For example, “Will the Federal Reserve raise rates in March?” is clear. “Will the economy improve?” is not.
2. Gather historical data: Find past events with similar characteristics. For political races, use historical election results. For sports, use team win rates. For economic events, use past central bank decisions.
3. Identify current predictors: These are variables that correlate with the outcome. For elections, that’s polling margin, incumbency, economic growth. For sports, that’s team strength ratings, injuries, home advantage.
4. Choose a model structure: Start simple. A logistic regression with 2-3 predictors is often enough. For time-series events, a Bayesian update that starts with a base rate and adjusts with new information works well.
5. Calibrate and test: Backtest your model on historical events where you know the outcome. Check that when your model says 70%, the event happens about 70% of the time. Adjust if overconfident.
6. Output a live probability: The model should accept current inputs and produce a fresh probability. This is what your bot compares to the market price.

Remember, the model is only as good as its inputs. Garbage in, garbage out.

Common Mistakes and Pitfalls

Even a well-built model can fail if you fall into these traps:

• Overfitting: A model that fits historical data perfectly will likely fail on new data. Keep it simple and use out-of-sample testing.
• Ignoring market efficiency: The market price already incorporates a lot of information. Your edge must come from information or analysis that the market hasn’t priced in. If your model just repeats public polls, you’ll have no edge.
• Misinterpreting probability: A 60% probability does not mean the event will happen 60% of the time in a single trial. It means that over many similar events, 60% should occur. This is crucial for position sizing.
• Neglecting time decay: As the event approaches, the probability should move toward 0 or 1. Your model must account for time remaining, not just current data.
• Ignoring fees and slippage: Polymarket has no trading fees, but you may face spread and slippage. A small edge can be wiped out by execution costs.

Also, beware of confirmation bias: if you want a certain outcome, you might unconsciously weight inputs to favor it. Stick to your model’s output, not your gut.

How to Choose the Right Model for Your Bot

Not all models fit all markets. Here’s a rough guide:

• Sports events: Use Elo ratings or similar power scores, combined with home advantage and injuries. A simple logistic regression on these variables works well.
• Political events: Polling averages, economic indicators, and historical swing models. Bayesian updating is effective because polls come out over time.
• Economic events (e.g., Fed decisions): Futures market prices (like Fed Funds futures) are strong predictors. Your model can blend those with economic data.
• Pop culture or novelty events: These are harder. You might rely on social media sentiment or betting odds from other platforms, but the data is noisy.

Start with the simplest model that captures the main drivers. Complexity adds maintenance burden and overfitting risk. Only add complexity if it improves out-of-sample accuracy.

Costs, Safety, and Alternatives

Costs: Polymarket itself charges no trading fees, but you need to pay gas fees for transactions on Polygon (the network Polymarket uses). These are typically a few cents, but can spike during congestion. You also need to convert fiat to USDC, which may incur exchange fees.

Safety: Trading bots on Polymarket are legal in many jurisdictions, but not all. Check your local laws. Also, be aware of market manipulation: some events may be influenced by whales or coordinated groups. Never risk money you can’t afford to lose. Use a dedicated wallet with limited funds.

Alternatives: If you don’t want to build your own model, you can use existing prediction market aggregators (like PredictIt or Betfair) as signals, or use Polymarket’s own API to fetch market data and apply a simple moving average or momentum strategy. Some traders use a “fade the public” approach, betting against overhyped markets. But these are strategies, not probability models.

Frequently Asked Questions

Q: Do I need a machine learning model to be profitable?
A: No. Many profitable traders use simple heuristics or a single well-chosen predictor. The key is having an edge, not complexity. A simple model that correctly identifies mispriced markets is better than a complex one that overfits.

Q: How do I handle events with no historical data?
A: For novel events, use analogous situations. For example, if the event is “Will a specific celebrity get married this year?”, look at similar celebrity weddings and base rates. Also, you can use the market’s own price as a prior and adjust with any new information.

Q: What is the best way to backtest my model?
A: Use historical Polymarket data (available via their API) or other prediction market archives. Simulate your model’s signals on past events and compare your hypothetical trades to actual outcomes. Ensure you account for the bid-ask spread and slippage.

Q: How do I set a threshold for buying or selling?
A: A common approach is to trade only when your edge exceeds a threshold, like 5% or 10%. This avoids wasting money on tiny edges that might be due to noise. The threshold depends on your risk tolerance and transaction costs.

Q: Can I use Polymarket’s own price as a predictor in my model?
A: Yes, but be careful. If you include the market price as an input, your model becomes a blend of your independent estimate and the market’s. That can reduce your edge, since you’re partially agreeing with the crowd. It’s often better to use other markets or sources.

Q: What if my model says the probability is 50% but the market is at 60%?
A: That means the market is overpriced. You might consider selling (going short) if Polymarket allows it, or simply avoiding the market. Never force a trade when there’s no edge.

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in t | Time to expiry | days | 7 | 0
in p | Current market price | $ | 0.45 | 0
in v | Your estimated probability | % | 60 | 0
out fair | Fair value (price) | $ | v / 100 | 2
out edge | Edge vs market | $ | (v / 100) - p | 2
out ev | Expected value per share | $ | (v / 100) * 1 - p | 2