A talk by David Lowe and James Wyllie delivered at Agile on the Beach, Falmouth, on 3 September 2015. The talk covered a variety of approaches to estimate and forecast, including planning poker, Kanban metrics, calibration, Student's t-statistic and Monte Carlo simulations.

1. Can Guinness help you
estimate?
@jamespwyllie
@bigpinots

2. Story Points & Velocity
Story Points
“They are a unit of measure for expressing the overall size of a user story or feature. They tell us how big a story is, relative to
others, either in terms of size or complexity”
- Mike Cohn

3. Story Points & Velocity
Planning Poker

4. Story Points & Velocity
Benchmark
3 Story Points

5. Story Points & Velocity
Velocity
total story points / no. of sprints
Last 5 sprints completed points were
30, 40, 50, 60, 70 = 250 points / 5 sprints = velocity of 50 points
How big is my project?
Total estimate (points) / velocity = number of sprints
e.g. 500 story points for project / 50 points = 10 sprints
How long will it take?
Number of sprints x length of sprint (weeks)
e.g. 2 weeks sprints = 10 sprints x 2 weeks = 20 weeks
https://www.mountaingoatsoftware.com/tools/velocity-range-calculator

6. Kanban Metrics

7. Kanban Metrics
LEAD TIME
WIP
DELIVERY RATE
https://hakanforss.wordpress.com/

8. Kanban Metrics
Little’s Law
Average Lead Time (ALT) = Average WIP / Average DR
Average WIP (AW) = Average Lead Time x Average DR
Average Delivery Rate (DR) = Average WIP / Average Lead Time
“Slap anyone who uses little’s law to create forecasts”
- Daniel Vacanti
https://leanpub.com/actionableagilemetrics
Basic Forecast
Forecast = (WI(n) / DR) + ALT

9. Monte Carlo simulators
(5.99 x 127,583) - 312,270 = 451,952
(5.99 x 127,583) - 312,270 = 451,952
(5.49 x 26,715) - 305,923 = -159,255
(5.99 x 127,583) - 312,270 = 451,952
(5.49 x 26,715) - 305,923 = -159,255
(8.49 x 45,202) - 228,423 = 155,341
(5.99 x 127,583) - 312,270 = 451,952
(5.49 x 26,715) - 305,923 = -159,255
(8.49 x 45,202) - 228,423 = 155,341
(6.99 x 117,371) - 245,352 = 575,073
(5.99 x 127,583) - 312,270 = 451,952
(5.49 x 26,715) - 305,923 = -159,255
(8.49 x 45,202) - 228,423 = 155,341
(6.99 x 117,371) - 245,352 = 575,073
(8.00 x 14,054) - 329,609 = -217,180
(5.99 x 127,583) - 312,270 = 451,952
(5.49 x 26,715) - 305,923 = -159,255
(8.49 x 45,202) - 228,423 = 155,341
(6.99 x 117,371) - 245,352 = 575,073
(8.00 x 14,054) - 329,609 = -217,180
(7.49 x 73,265) - 369,820 = 178,935
(5.99 x 127,583) - 312,270 = 451,952
(5.49 x 26,715) - 305,923 = -159,255
(8.49 x 45,202) - 228,423 = 155,341
(6.99 x 117,371) - 245,352 = 575,073
(8.00 x 14,054) - 329,609 = -217,180
(7.49 x 73,265) - 369,820 = 178,935
(8.49 x 7,904) - 207,013 = -139,911
(5.49 x 26,715) - 305,923 = -159,255
(8.49 x 45,202) - 228,423 = 155,341
(6.99 x 117,371) - 245,352 = 575,073
(8.00 x 14,054) - 329,609 = -217,180
(7.49 x 73,265) - 369,820 = 178,935
(8.49 x 7,904) - 207,013 = -139,911
(5.99 x 148,909) - 206,799 = 685,166
(8.49 x 45,202) - 228,423 = 155,341
(6.99 x 117,371) - 245,352 = 575,073
(8.00 x 14,054) - 329,609 = -217,180
(7.49 x 73,265) - 369,820 = 178,935
(8.49 x 7,904) - 207,013 = -139,911
(5.99 x 148,909) - 206,799 = 685,166
(5.99 x 40,272) - 300,293 = -59,064
(6.99 x 117,371) - 245,352 = 575,073
(8.00 x 14,054) - 329,609 = -217,180
(7.49 x 73,265) - 369,820 = 178,935
(8.49 x 7,904) - 207,013 = -139,911
(5.99 x 148,909) - 206,799 = 685,166
(5.99 x 40,272) - 300,293 = -59,064
(5.99 x 151,186) - 228,727 = 676,880
(8.00 x 14,054) - 329,609 = -217,180
(7.49 x 73,265) - 369,820 = 178,935
(8.49 x 7,904) - 207,013 = -139,911
(5.99 x 148,909) - 206,799 = 685,166
(5.99 x 40,272) - 300,293 = -59,064
(5.99 x 151,186) - 228,727 = 676,880
(8.99 x 35,272) - 333,723 = -16,618
(7.49 x 73,265) - 369,820 = 178,935
(8.49 x 7,904) - 207,013 = -139,911
(5.99 x 148,909) - 206,799 = 685,166
(5.99 x 40,272) - 300,293 = -59,064
(5.99 x 151,186) - 228,727 = 676,880
(8.99 x 35,272) - 333,723 = -16,618
(7.99 x 124,891) - 368,288 = 629,595
(8.49 x 7,904) - 207,013 = -139,911
(5.99 x 148,909) - 206,799 = 685,166
(5.99 x 40,272) - 300,293 = -59,064
(5.99 x 151,186) - 228,727 = 676,880
(8.99 x 35,272) - 333,723 = -16,618
(7.99 x 124,891) - 368,288 = 629,595
(8.99 x 76,506) - 388,655 = 299,138
(5.99 x 148,909) - 206,799 = 685,166
(5.99 x 40,272) - 300,293 = -59,064
(5.99 x 151,186) - 228,727 = 676,880
(8.99 x 35,272) - 333,723 = -16,618
(7.99 x 124,891) - 368,288 = 629,595
(8.99 x 76,506) - 388,655 = 299,138
(7.99 x 23,805) - 375,232 = -185,034
(5.99 x 40,272) - 300,293 = -59,064
(5.99 x 151,186) - 228,727 = 676,880
(8.99 x 35,272) - 333,723 = -16,618
(7.99 x 124,891) - 368,288 = 629,595
(8.99 x 76,506) - 388,655 = 299,138
(7.99 x 23,805) - 375,232 = -185,034
(5.99 x 42,630) - 253,040 = 2,314
(5.99 x 151,186) - 228,727 = 676,880
(8.99 x 35,272) - 333,723 = -16,618
(7.99 x 124,891) - 368,288 = 629,595
(8.99 x 76,506) - 388,655 = 299,138
(7.99 x 23,805) - 375,232 = -185,034
(5.99 x 42,630) - 253,040 = 2,314
… and on …
(5.99 x 151,186) - 228,727 = 676,880
(8.99 x 35,272) - 333,723 = -16,618
(7.99 x 124,891) - 368,288 = 629,595
(8.99 x 76,506) - 388,655 = 299,138
(7.99 x 23,805) - 375,232 = -185,034
(5.99 x 42,630) - 253,040 = 2,314
… and on … and on …
(5.99 x 151,186) - 228,727 = 676,880
(8.99 x 35,272) - 333,723 = -16,618
(7.99 x 124,891) - 368,288 = 629,595
(8.99 x 76,506) - 388,655 = 299,138
(7.99 x 23,805) - 375,232 = -185,034
(5.99 x 42,630) - 253,040 = 2,314
… and on … and on … and on …

10. “the future of forecasting in knowledge work”
~ Dan Vacanti

11. Calibration
62mm
Estimate the
average
rainfall in
September

12. Calibration I’m 80% confident
it will be between
0mm and 100mm
Estimate the
average
rainfall in
September

14. Student’s t-statistic
William Sealy Gosset (13 June 1876 – 16 October 1937)
https://upload.wikimedia.org/wikipedia/commons/thumb/4/42/William_Sealy_Gosset.jpg/766px-William_Sealy_Gosset.jpg
A small amount of data can be
used to forecast the future … if
you take the appropriate level of
error into account

15. Conclusions
Measurement
“A quantitatively expressed reduction of uncertainty based on one or more observations”
- Douglas Hubbard
4 useful measurement assumptions:
● Your problem is not as unique as you think
● You have more data than you think
● An adequate amount of data is more accessible than you think
● You need less data than you think

16. Conclusions

17. Conclusions
“It is better to be roughly right than precisely wrong.”
~ John Maynard Keynes, British Economist
Can Guinness help you estimate?

18. Conclusions
“It is better to be roughly right than precisely wrong.”
~ John Maynard Keynes, British Economist
Can Guinness help you estimate?
forecast?
@jamespwyllie & @bigpinots