There's a saying: "Estimates are always wrong."
True. But you can make them less wrong.
I'll share practical tips to improve estimation accuracy found by analyzing hundreds of projects.
Tip 1: Automatic Buffer by Task Type
Not all tasks have the same uncertainty.
buffer_rates = {
"New Development": 1.3, # 30% buffer
"Bug Fix": 1.5, # 50% buffer
"Refactoring": 1.4, # 40% buffer
"External API Integration": 2.0, # 100% buffer (2x)
"Performance Optimization": 1.8, # 80% buffer
"Documentation": 1.2 # 20% buffer
}
def smart_estimate(hours, task_type):
return hours * buffer_rates.get(task_type, 1.3)
# Example
print(smart_estimate(10, "External API Integration")) # 20 hours
print(smart_estimate(10, "Documentation")) # 12 hours
Key: Increase buffer as external dependencies increase.
Tip 2: Standardize Estimation Units
Standardizing time units reduces misconceptions.
class StandardEstimate:
"""Standard estimation units"""
UNITS = {
"XS": 2, # 2 hours (one coffee)
"S": 4, # 4 hours (half day)
"M": 8, # 8 hours (one day)
"L": 16, # 16 hours (two days)
"XL": 40, # 40 hours (one week)
"XXL": 80 # 80 hours (2 weeks)
}
@staticmethod
def estimate(size):
if size not in StandardEstimate.UNITS:
return "Reconsider the size"
return StandardEstimate.UNITS[size]
# Instead of ambiguous estimates like "3.5 days"
task_size = "L" # Clearly "two-day task"
Tip 3: Checklist-Based Estimation
Manage easily missed tasks with checklists.
## API Development Checklist
- [ ] Design Review (2h)
- [ ] Schema Definition (1h)
- [ ] Implementation (4h)
- [ ] Unit Tests (2h)
- [ ] Integration Tests (2h)
- [ ] Documentation (1h)
- [ ] Code Review (1h)
- [ ] Deployment Prep (1h)
Total: 14 hours (Initial estimate: 6 hours)
Effect: Discover average 40% missing work
Tip 4: Team Velocity Tracking
import numpy as np
class TeamVelocity:
def __init__(self):
self.history = []
def add_sprint(self, estimated, actual):
accuracy = actual / estimated
self.history.append(accuracy)
def get_adjustment_factor(self):
if len(self.history) < 3:
return 1.5 # Default 50% buffer if insufficient data
# Average of recent 10 sprints
recent = self.history[-10:]
return np.mean(recent)
def predict(self, estimate):
factor = self.get_adjustment_factor()
return {
"raw": estimate,
"adjusted": estimate * factor,
"confidence": self.get_confidence()
}
def get_confidence(self):
if len(self.history) < 5:
return "Low"
std = np.std(self.history[-10:])
if std < 0.2:
return "High"
elif std < 0.4:
return "Medium"
return "Low"
Tip 5: Estimation Postmortem
Analyze estimation accuracy after each sprint.
## Sprint 12 Estimation Analysis
### Accurate Estimates
- UI Component (8h estimated → 7h actual) ✅
- Reason: Lots of similar task experience
### Inaccurate Estimates
- Payment Integration (8h estimated → 20h actual) ❌
- Cause: PG company documentation poor
- Lesson: External APIs need 2x buffer mandatory
### Improvement Actions
1. External integration tasks must estimate 2x
2. New technology use requires separate learning time
3. Testing tasks allocate 40%+ of development time
Tip 6: Visualize Uncertainty
def visualize_uncertainty(tasks):
"""Visually express uncertainty"""
for task in tasks:
certainty = task['certainty']
bars = '█' * certainty + '░' * (10 - certainty)
print(f"{task['name']:20} {bars} {task['hours']}h")
# Example
tasks = [
{"name": "Login UI", "hours": 4, "certainty": 9},
{"name": "OAuth Integration", "hours": 8, "certainty": 5},
{"name": "Security Audit", "hours": 12, "certainty": 3}
]
visualize_uncertainty(tasks)
# Login UI █████████░ 4h
# OAuth Integration █████░░░░░ 8h
# Security Audit ███░░░░░░░ 12h
Start with high-uncertainty tasks first.
Tip 7: Pair Estimation
Don't estimate alone.
def pair_estimation(dev1_estimate, dev2_estimate):
"""Two people estimate together"""
diff_ratio = abs(dev1_estimate - dev2_estimate) / min(dev1_estimate, dev2_estimate)
if diff_ratio > 0.5: # 50%+ difference
print("🚨 Big difference! Discussion needed")
print(f"Developer 1: {dev1_estimate}h")
print(f"Developer 2: {dev2_estimate}h")
return "Re-estimation needed"
# Weighted average (weight to more experienced developer)
weighted_avg = (dev1_estimate * 0.6 + dev2_estimate * 0.4)
return weighted_avg
Tip 8: Reflect Time-Based Productivity
productivity_by_time = {
"Monday Morning": 0.7, # 70% efficiency
"Monday Afternoon": 0.9,
"Tue-Thu Morning": 1.0, # 100% efficiency
"Tue-Thu Afternoon": 0.9,
"Friday Morning": 0.8,
"Friday Afternoon": 0.6 # 60% efficiency
}
def realistic_schedule(task_hours):
"""Schedule reflecting actual productivity"""
available_slots = [
("Monday Morning", 4, 0.7),
("Monday Afternoon", 4, 0.9),
# ...
]
actual_hours_needed = 0
for slot_name, slot_hours, efficiency in available_slots:
effective_hours = slot_hours * efficiency
actual_hours_needed += slot_hours
task_hours -= effective_hours
if task_hours <= 0:
break
return actual_hours_needed
Practical Checklist
Check before estimation:
- [ ] Did you check similar past task data?
- [ ] Did you break tasks to 8 hours or less?
- [ ] Did you identify external dependencies?
- [ ] Did you include test/document/deploy time?
- [ ] Did you cross-validate with team members?
- [ ] Did you mark uncertainty level?
- [ ] Did you consider day/time-based productivity?
Conclusion: Perfect Estimation Doesn't Exist
Estimation is prediction, not promise.
What's important is continuous improvement.
Estimate, record, analyze, and improve each time.
You can evolve from "always takes 2x" to "mostly accurate."
Remember:
- Estimation is a range (not a single value)
- Acknowledge uncertainty
- Accumulate data
- Estimate with the team
Need accurate schedule estimation and project management? Check out Plexo.
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