Goal Probability Forecast (Bayesian)

Formula

Posterior = Prior + (Evidence - Prior) × 0.3 + (Pace - Prior) × 0.2, adjusted by difficulty

Bayesian updating: start with prior probability, adjust based on evidence ratio (success vs failure signals) and pace (progress vs time). Difficulty multiplier adjusts for task complexity.

Worked Examples

Example 1: Fitness Goal On Track

Problem:Goal: Lose 20 lbs in 90 days. Prior: 60% (based on past attempts). Day 30: Lost 8 lbs (40% progress). 4 success signals (workouts consistent, diet adherence), 1 failure (skipped gym week 3).

Solution:Time progress: 30/90 = 33.3% Actual progress: 40% Progress vs time: +6.7% (ahead!) Evidence ratio: 4/(4+1+1) = 67% Pace score: 75 (slightly ahead) Bayesian update: 60% + (67-60)×0.3 + (75-60)×0.2 = 60 + 2.1 + 3 = 65.1% Adjusted (medium difficulty): 65% Projected completion at current pace: 8 lbs in 30 days = 0.27 lbs/day 60 days left × 0.27 = 16.2 lbs more Total: 24.2 lbs (exceeds goal!) Probability: 65% → Likely success

Result:65% success probability (Likely) | On pace | Projected 24 lbs total

Example 2: Startup Revenue Goal Behind

Problem:Goal: $100K MRR in 12 months. Prior: 40%. Month 6: $25K MRR (25% progress). 2 success signals (product-market fit, growth), 3 failure signals (churn, CAC too high).

Solution:Time progress: 6/12 = 50% Actual progress: 25% Progress vs time: -25% (BEHIND) Evidence: 2/(2+3+1) = 33% Pace score: 40 (behind pace) Bayesian update: 40% + (33-40)×0.3 + (40-40)×0.2 = 40 - 2.1 + 0 = 37.9% Projected at current pace: $25K in 6 months = $4.2K/month growth 6 months × $4.2K = $25K more Total: $50K (50% of goal) Probability: 38% → Uncertain Need 2x acceleration to hit target

Result:38% success (Uncertain) | Behind pace | Need 2x acceleration or pivot

Example 3: Book Writing Nearly Done

Problem:Goal: 80K word book in 180 days. Prior: 70%. Day 150: 72K words (90% progress). 5 success signals (flow state, outline solid), 0 failures. Medium difficulty.

Solution:Time progress: 150/180 = 83.3% Actual progress: 90% Progress vs time: +6.7% (ahead!) Evidence: 5/(5+0+1) = 83% Pace score: 90 (ahead) Bayesian update: 70% + (83-70)×0.3 + (90-70)×0.2 = 70 + 3.9 + 4 = 77.9% Adjusted (medium): 78% Projected: 72K in 150 days = 480 words/day 30 days × 480 = 14.4K more Total: 86.4K (exceeds goal!) Probability: 78% → Likely High confidence - goal nearly certain

Result:78% success (Likely) | Ahead of pace | 86K words projected

Frequently Asked Questions

What is Bayesian probability?

Bayesian inference updates prior beliefs with new evidence. Start with initial probability estimate (prior), observe outcomes (evidence), calculate updated probability (posterior). Unlike frequentist methods, it incorporates subjective priors and updates continuously.

How do I set a prior probability?

Base on: historical success rates for similar goals, expert judgment, reference class forecasting, or base rate of comparable attempts. For new goals with no data, 50% (maximum uncertainty) is reasonable.

How accurate are goal probability forecasts?

Well-calibrated forecasters achieve 75-85% accuracy. Common errors: overconfidence (planning fallacy), ignoring base rates, and confirmation bias. Track your predictions and actual outcomes to calibrate.

Should I update probabilities continuously?

Yes, Bayesian updating is iterative. Weekly or milestone-based updates are practical. Significant new evidence (major milestone or setback) warrants immediate update. Track updates to see how predictions evolve.

How does difficulty affect success probability?

Higher difficulty lowers probability through: longer time required, more failure modes, greater skill demands, and higher resource needs. Adjust by 0.6-1.2x based on comparative difficulty assessment.

What if progress isn't linear?

Many goals have S-curves: slow start, acceleration, then plateau. Early progress may overestimate; late progress may underestimate. Adjust for known patterns in your domain (learning curves, network effects).

How do I improve goal achievement?

Strategies: set realistic timelines, build buffer for unexpected, break into smaller milestones, track leading indicators, address obstacles early, and update plans based on evidence rather than optimism.

What are good goal-setting frameworks?

SMART goals (Specific, Measurable, Achievable, Relevant, Time-bound), OKRs (Objectives and Key Results), WOOP (Wish, Outcome, Obstacle, Plan). All benefit from probability forecasting to set realistic targets.

Background & Theory

Goal probability forecasting combines initial estimates (priors) with actual progress (evidence) to produce realistic success probabilities. The key insight: continuously update beliefs based on new data rather than sticking to initial optimism. Progress vs time ratio is a powerful predictor. This approach counteracts planning fallacy and enables mid-course corrections.

History

Bayesian probability was developed by Thomas Bayes (1763). Its application to forecasting emerged in the 20th century. The Good Judgment Project (2011-2015) showed ordinary people could achieve 30% better accuracy than intelligence analysts using Bayesian updating. Modern prediction markets and forecasting platforms automate these calculations.