Gitential inherently focuses on Agile which aims to be adaptive to ongoing changes, even when they come late during development. This does not preclude the use of predictive analytics. Agile software projects are subject to a range of parameters defined by software specifications and your development team.
Specifications will eventually define what the software needs to do, what functions it needs to have, along with how it must perform. Decisions will be made as to which programming languages, technologies, and resources will power it. Ostensibly, these should match your developer’s skills and experience.
Any change in your team will most likely have an impact on delivery, sprint planning, code pairing and mentoring, and so forth. Some of this can be reasonably predicted.
If a senior developer leaves mid-project, bottlenecks in the workflow can be expected, possibly an uptick in defects, those picking up the slack are likely to be less efficient as they’ll need to multi-task more, etc.
Adding lots of new/junior developers? Expect efficiency to drop hard, code complexity and defect rates to increase, and a slew of other issues.
The question is though… How much will the impact be? And then, there’s the question of how quickly you can help the team improve performance. Benchmarking helps to set realistic goals. There’s no danger in making comparisons between teams and projects, only when setting expectations, goals, and being able to factor them into release planning.