Coaching practices for Forecast Accuracy Measurement

Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Forecast Accuracy Measurement, these are the strongest matches in the current practice library.

Does this sound like the set of challenges you might be facing?

  • I make calls about how things will go all the time, but I never write them down or check them after
  • I’m sure about things constantly and I have no idea if that confidence is earned
  • I make calls all the time but I never write them down or check them later, so my confidence is just a feeling that floats free
  • When a prediction of mine blows up, I either spiral into thinking the whole thing is pointless or I just shrug and forget it
  • My guesses never seem to get any better because I make them, find out the real answer, and then just move on

Practices that may help

  1. Score your own past predictions to calibrate your outside-view use
    Keep a forecast log and score it — you cannot improve calibration without feedback on where you were over- or under-confident.
    The Outside View
  2. Practice probabilistic calibration by tracking your predictions
    Assign explicit probability estimates to your predictions and track whether they come true at the right rate.
    Base-Rate Neglect: Why We Ignore the Odds
  3. Maintain a scored prediction log
    Record predictions with explicit probabilities and score them when they resolve.
    Calibration Training
  4. Treat forecasting accuracy as a skill that improves with practice
    Take prediction errors as performance feedback, not as proof that forecasting is futile.
    Superforecasting
  5. Track your estimates and calibrate
    Compare your Fermi estimates to actual figures when you can, and use the gap to improve future estimates.
    Fermi Estimation
  6. Conduct post-project debriefs to contribute to the class data
    Record actual vs. forecast outcomes honestly — this builds the reference class that future forecasts depend on.
    Reference Class Forecasting
  7. Reference Class Forecasting
    Reference class forecasting, developed by Daniel Kahneman and Amos Tversky and formalized by Bent Flyvbjerg, improves forecast accuracy by anchoring on the statistical distribution of outcomes for similar past projects rather than on the details of the current one. The method reliably corrects the optimism bias that inflates cost, time, and benefit estimates in planning — the evidence base here is real and specific.
  8. Forecast a distribution, not a point estimate
    Represent your forecast as a range of likely outcomes, not a single predicted number.
    Reference Class Forecasting
  9. Update your forecast incrementally as new evidence arrives
    Treat your forecast as a probability that should shift with each new piece of evidence, not a commitment that survives contradiction.
    Reference Class Forecasting
  10. Practice probability calibration
    Regularly make probabilistic predictions and track your accuracy across many of them.
    Hindsight Bias: Why Everything Seems Obvious in Retrospect

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