Predicted Probability: How to Build a Forecast You Can Trust
What predicted probability means
Predicted probability is your best estimate that an outcome will happen, expressed on a probability scale from 0 to 1. In prediction markets, you will often see a market price that can be interpreted as an implied probability. Your job is to build your own predicted probability first, then compare it to the market.
Step 1: Define the outcome with precision
Bad forecasts start with vague questions. Before you assign a number, define:
• The outcome: what must be true for YES to settle.
• The deadline: when it resolves and what the measurement window is.
• The source of truth: how it will be settled, and whether an oracle or dispute process exists.
If the outcome definition is fuzzy, your probability is not the main risk. The main risk is ambiguous outcome and settlement ambiguity.
Step 2: Start with a base rate
A base rate is the background frequency of the outcome in a relevant reference class. Base rates anchor forecasts in reality and reduce wild swings.
Turn the base rate into a prior probability. If you are unsure, start with a range and use the midpoint as a working prior.
Step 3: Identify evidence and its direction
Evidence matters only if it moves probability in the correct direction and you can defend why. This is where many people confuse confidence vs probability.
When evidence is conditional, write it as conditional probability language, even informally. The goal is to avoid mixing directions like P(A given B) vs P(B given A).
Step 4: Update, ideally in odds form
Bayes style updates are easiest to reason about using odds. The odds form of Bayes theorem is:
• posterior odds = prior odds times likelihood ratio
If you prefer a single numeric scale, you can use log odds or logit. For conversions, use an odds converter calculator on this site.
Step 5: Sanity checks that prevent extreme errors
Before you lock in a number, run these checks:
• No fake certainty: avoid 0 percent or 100 percent unless you can prove it. Extreme numbers are punished by scoring rules like log loss and often reflect overconfidence.
• Range check: ask what evidence would move you meaningfully the other way. If nothing could, you are likely overconfident.
• Calibration mindset: good forecasters track whether their 70 percent calls happen about 70 percent of the time. That is calibration. If you never track outcomes, you cannot know if you are calibrated.
• Underconfidence trap: pushing everything toward 50 percent can look safe but can destroy sharpness and usefulness. See underconfidence.
Worked example: a simple Bayes update
Suppose your base rate suggests a 40 percent chance.
• Prior probability p = 0.40
• Prior odds = p divided by (1 minus p) = 0.40 divided by 0.60 = 0.6667
Now suppose new evidence favors the outcome with a likelihood ratio of 1.5.
• Posterior odds = 0.6667 times 1.5 = 1.0000
• Posterior probability = odds divided by (1 plus odds) = 1.0000 divided by 2.0000 = 0.50
Your predicted probability becomes 50 percent. That does not mean certainty. It means your best estimate after the update is a coin flip.
Step 6: Translate your probability into a trading decision
In prediction markets you compare your predicted probability to market pricing:
• Convert market price into implied probability and confirm the price scale.
• Define fair price from your predicted probability, then compute edge as the gap versus implied probability.
• Require that the edge clears costs. Use break-even probability after fees, trading fee, and spread costs like bid ask spread.
• Execution matters: you do not trade at mid price unless you can actually get filled there. You often pay ask to buy and hit bid to sell.
Quick checklist you can reuse
• Is the outcome definition unambiguous, with clear settlement?
• What is the base rate and why is it the right reference class?
• What evidence matters, and is it directionally correct?
• What probability range is defensible, not just one number?
• Does your probability survive costs and execution reality?
Takeaway
A trusted predicted probability is built, not guessed. Start with a base rate, update with evidence, avoid extreme confidence, and keep calibration in mind. Only then translate the number into fair price, edge, and break-even math in the market. Most mistakes are not about being wrong on the world, they are about building probabilities with weak foundations and then ignoring costs.
Related
• Confidence vs Probability: The Fastest Way to Get Miscalibrated
• Calibration: What Good Probabilities Actually Means
• Bayes for Humans: Updating with Odds and Likelihood Ratios