Coaching practices for Prediction Tracking

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

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

• When a prediction of mine blows up, I either spiral into thinking the whole thing is pointless or I just shrug and forget it
• I’m sure about things constantly and I have no idea if that confidence is earned
• I make calls about how things will go all the time, but I never write them down or check them after
• New information keeps arriving that should obviously move my estimate, but I just cling to the date I first committed to
• I’m guessing how this goal of mine will play out purely from my own picture of it, and it feels sure to work

Practices that may help

1. Treat forecasting accuracy as a skill that improves with practice
   Take prediction errors as performance feedback, not as proof that forecasting is futile.
   Superforecasting
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. 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
4. Superforecasting
   Philip Tetlock’s forecasting tournament research found that a subset of ordinary people — "superforecasters" — consistently outperform domain experts and intelligence analysts at probabilistic prediction. They share measurable cognitive and behavioral habits: they think in probabilities, update frequently on evidence, and actively seek disconfirming information. These habits are learnable.
5. 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
6. Use reference classes to ground personal estimates in base rates
   Before estimating how your situation will unfold, find similar past situations and check what happened.
   Bayesian Thinking: How to Update Beliefs Rationally
7. Maintain a scored prediction log
   Record predictions with explicit probabilities and score them when they resolve.
   Calibration Training
8. 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.
9. Pre-commit to resolution criteria before making a prediction
   Define exactly what counts as "I was right" before the outcome happens.
   Calibration Training
10. Affect Forecasting: Why You Mispredicted How You’d Feel
    Daniel Gilbert and colleagues showed that people reliably misjudge both the intensity and duration of their future emotional reactions — overestimating how bad bad events will feel and how good good events will feel. The mechanism is "impact bias": we focus on the event and ignore the adaptive processes that will moderate it. This research is well-replicated and has direct implications for how you make decisions, build habits, and manage expectations.

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