Confidence vs Probability: The Fastest Way to Get Miscalibrated
The core mistake
Confidence vs probability is the gap between how sure you feel and how likely something actually is. Confidence is internal. Probability is a statement about the world that can be tested over many events.
If you treat confidence as probability, you become miscalibrated. That usually shows up as overconfidence, and sometimes as underconfidence.
Why this matters in prediction markets
Prediction markets turn probability mistakes into money mistakes. When you confuse confidence with probability, you tend to:
• Trade too aggressively on thin evidence.
• Overpay for contracts because you do not compare to implied probability.
• Ignore costs such as trading fee and bid ask spread, then wonder why correct calls lose money.
Two failure modes
1) Overconfidence
Overconfidence is when your probabilities are too extreme. You say 90 percent when the true rate is closer to 60 percent.
Symptoms:
• Many 80 to 95 percent calls that fail more often than expected.
• Big swings based on small updates.
• You often feel surprised by losses, because your numbers implied they should not happen.
2) Underconfidence
Underconfidence is when you avoid strong probabilities even when evidence is strong. Everything drifts toward 50 percent.
Symptoms:
• You rarely make calls above 70 percent or below 30 percent.
• Your forecasts are safe but not useful.
• You lose potential edge because you never commit to a difference large enough to clear costs.
A practical way to assign numbers
Instead of asking, "How confident am I", ask, "Out of 100 similar cases, how many happen". That forces you to think in frequencies and helps calibration.
Step 1: Anchor with a base rate
Start from a base rate. This is your baseline. It prevents emotional forecasting and reduces extreme guesses.
If you do not know the base rate, do not pretend you do. Use a wide range and pick a midpoint as a working prior probability.
Step 2: Update, but demand evidence strength
Updates should be proportional to evidence. If you cannot explain why evidence is strong, the update should be small. This is where Bayes thinking helps, even informally. See Bayes for Humans.
Step 3: Use a range, then pick a point
Write a range first: "I think it is 45 to 60 percent." Then pick a single predicted probability for decisions: predicted probability = 0.52, for example.
Ranges reduce false precision. The point estimate is what you compare to the market.
How to test whether you are confusing confidence with probability
Use scoring and tracking. If you do not measure, you cannot improve.
Track calibration and scoring
• Use Brier score for binary events.
• Understand that log loss punishes extreme probabilities when you are wrong.
• Check calibration and calibration error over time.
A simple self test: take all your 70 percent calls. Do about 70 percent of them occur? If not, you are miscalibrated.
Look for these patterns
• Too many "locks": you label events as certain, then reality disagrees.
• Narrative dominance: one story makes you ignore base rates.
• Outcome bias: one win makes you feel more skilled than your process supports.
Translation to trading: when a number becomes a trade
Even a good predicted probability is not enough. You must translate it into pricing and cost thresholds:
• Convert market market price to implied probability and confirm price scale.
• Compute edge versus implied probability.
• Require that edge clears break even probability after fees and spread.
• If you execute with a market order, assume you cross the bid ask spread and increase costs.
Quick fixes that work immediately
• Replace "I am very confident" with a number and a reason.
• Always write the base rate first, even if it is rough.
• Avoid 0 percent and 100 percent. Treat them as reserved for proofs.
• Separate your forecast from your trade. Forecast is probability. Trade is edge after cost.
Takeaway
Confidence is not probability. If you want forecasts that hold up, anchor in base rates, update in proportion to evidence, and measure calibration. In markets, force your number through implied probability, fair price, and break even cost. That single discipline removes most "I was right but lost money" mistakes.
Related
• Predicted Probability: How to Build a Forecast You Can Trust