One of the main questions about Story Points is what is their accuracy. Has any research or survey been performed to analyse their precision?

For example what is the accuracy of the completion date estimation based on Story Points.

  • 1
    To answer this question we just need to say yes (giving at least one example) or no (explaining how we searched)? Commented Jan 26, 2020 at 10:44
  • Links to researches would be appreciated!) Commented Jan 26, 2020 at 10:51
  • By 'precision', do you mean 'accuracy'? Because I can answer your Question as-is with two words - 'Fibonacci, usually'.
    – Sarov
    Commented Jan 27, 2020 at 14:12
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    Interesting question. I'd be willing to bet that the cost of calibration vs the return on calibration is better for story points than for estimation by hours. I'm also willing to bet that even poor quality story point estimation by the team is superior to estimates developed by the PM in isolation. I think your question is good - but I think it has to be in the context of some questions that are much more important.
    – MCW
    Commented Jan 28, 2020 at 16:51
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    I've created simple application that reads JIRA tickets, calculates their cycle time and compares it to estimated story points. When I run it on my current projects, I find minimal relationship between the two. I would bet majority of projects using story points would be same. You can try it here : github.com/Euphoric/JiraFlowCharts/wiki
    – Euphoric
    Commented Jan 29, 2020 at 14:04

2 Answers 2


I know of at least two


Effort Estimation in Agile Software Development using StoryPoints gives a good overview of the story points accuracy: The cone of uncertainty is inherent to estimation.

For improving the estimation accuracy, various optimization techniques have been proposed. The Support Vector Regression (SVR) is one of these techniques that helps in getting optimal estimated values and the following article explains the theory in a thorough manner.


Also, to improve the accuracy of effort estimation, this article proposes a model for agile software development project prediction using Bayesian networks.

Mike Cohn has invested a lot of time researching the subject of Story points. You may also look into the following articles:

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