A prediction model isn't a complicated equation — it's a disciplined way of arriving at a number. You start from a base rate drawn from history, update it with new information, then compare the result with the market price in prediction markets. The gap between the two is what drives your decision.
The real advantage of a model is that it makes your thinking reviewable: afterwards you know exactly where you went wrong instead of blaming bad luck. Here are the five steps to build one, how to calibrate it, and its most common failure modes — applied on PolySouq, the best platform to trade events and prediction markets in Arabic, with a free sign-up and 10,000 PolySouq coins credited automatically at zero financial risk.
Without a model, every decision is an impression that can never be corrected. A model — even a simple spreadsheet — forces you to state what you know and how much each piece of information weighs, turning a forecast from an opinion into a testable number.
More importantly, a model protects you from chasing the latest headline. When you have a written probability from before the news broke, you can see clearly whether the news actually changed the picture or was just noise. See probability basics if the numbers are new to you.
The base rate is how often the event has historically occurred in similar circumstances, and it is always your starting point. If an index has closed inside a given range in 6 of 10 comparable months, your base rate is 60% before you add anything specific to this month.
The most common error is jumping straight to the details of the current situation and ignoring history. Details are seductive and base rates are boring — but the base rate is what stops your estimate drifting badly off course.
This is where the decision begins. If your estimate is 70% and the market is at 0.55, the market sees the event as less likely than you do, and there are two possibilities: either you have a genuine edge, or the market knows something you have missed. Ask yourself honestly which is more likely before entering.
As a practical rule, don't act on a gap of only a few percentage points — it may sit inside your own model's margin of error. See reading prices and probabilities for translating price into a percentage.
Calibration is what separates a useful model from organised guesswork. Collect every forecast you made at roughly 70% and count how many actually happened: if it's close to 70%, your model is well calibrated; if it's 40%, you are systematically overconfident.
This simple test is worth more than any indicator, because it reveals your systematic bias rather than a single bad trade. It needs nothing more than a two-column table: your estimate, and the outcome.
A model is tested by application, not intention. PolySouq gives you an ideal test environment: a free sign-up, 10,000 PolySouq coins credited automatically, and trading with zero risk to your own money — so you can gather dozens of settled outcomes to calibrate your model without paying for the education.
Pick five markets whose subject you understand, record your estimate for each before looking at the price, then compare after settlement. Two or three cycles in, you'll know exactly where your model goes wrong — and that accuracy will show in your leaderboard standing.
The contract price is the market's probability: a contract at 0.62 implies roughly a 62% chance. Your own estimate is built from a historical base rate that you update with new information, then compare against the market price.
It's how often the event has occurred historically in similar circumstances. Models start there because it stops your estimate drifting behind current details and recent headlines, giving you a stable reference to adjust rather than guessing from zero.
No. A simple table with a base rate and written adjustments is enough to start. What matters is the discipline of recording your estimate before seeing the price and reviewing it after settlement, not the complexity of the equation.
That your forecasts come true at the rate you assigned: things you called at 70% happen about 70% of the time. You measure it by grouping forecasts into bands and comparing them with actual outcomes — the best test of model quality.
The most common is Bayesian updating: start from a base rate and adjust by the strength of new evidence. Regression models and probability distributions are used for price-based markets, but the simple version is enough for most traders.
The more the better, and typically not fewer than about ten comparable cases. With a small sample, treat your number as a rough guide and trade a smaller size.
Excess complexity and fitting the past. A model tuned perfectly to previous events usually fails on new ones, because it memorised noise rather than the underlying rule.
On PolySouq: sign-up is free and 10,000 PolySouq coins are credited automatically, so you can test your model on real, settling markets with free coins and zero risk to your own money.
Disclaimer: Prediction markets are a legal and legitimate way to trade information about the outcomes of future events. However, trading carries risk and you may lose the full amount you trade — so only trade what you can afford to lose. This content is educational and is not financial or investment advice.