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My original request was for the creation of a standalone Python script that would perform Monte Carlo simulations for Agile estimation. I provided the developer with two articles that outline what I am trying to accomplish. [login to view URL] This article describes using Monte Carlo simulation to determine how many days it might take to complete a Scrum product backlog based on story points. In this article the author describes how he groups the run times from a hundred simulations into 10 buckets, each covering one-tenth of the time between the fastest and slowest. This aspect of the script is not working as described here. The calculations in the script are based on grouping things into 11 buckets, which I don’t understand and is not what I asked for. [login to view URL] This article is what the script is based on. It describes using historical sprint velocity as a baseline then applying Monte Carlo simulation to determine the likelihood of completing X number of story points within a 10-day sprint. The script as-is, does not perform the way I expected. Based on using the same methods described in the two articles, I would expect the script to produce similar results, but it's not. When I execute the same scenarios as described in this article, I get very different results. The developer chose to use gaussian distribution to accomplish the task, and it doesn't seem to be producing the results I expected. What I really need is someone who has experience with Monte Carlo simulations in Python to look at the script and recommend what changes need to be made, then make the changes. I'm happy to answer any questions you have. When you execute the script, it will prompt you for values to be entered. Here are some sample values: Sprint velocity: 114, 143, 116, 109, 127, 153, 120 Working days in sprint: 10 Forecasted sprint backlog points: 125 Number of simulations: 1000 For reference, here are the two scenarios in the second article I am trying to reproduce. Scenario #1 Team's velocity for 6 sprints is: 114, 143, 116, 109, 127, 153. Number of story points being forecasted for the sprint is 125. 1000 simulations are ran to assess the likelihood of 125 points actually being completed in their sprint of 10 working days. Result is 543 out of 1000 runs completed in 10.00 days or less, which is a 54.3% chance of the proposed 125-point backlog being completed within the sprint time-box. Scenario #2 Product Owner agrees to drop a 3-point user story from the sprint backlog, so the new forecasted sprint backlog is 122. After 1000 simulations are ran a total of 994 runs now completed in 10 days or less. That results in a 99.4% likelihood of completing the work in the sprint backlog.