There is a popular notion in agile estimating that a time series of past performance can be used to forecast the future performance of the project. Here's a clip of that time series.It is conjectured that data like this can be used to make decisions about the future. And certainly data about the past CAN be used and IS used to make decisions (forecast based decisions), but there are serious flaws in the suggested approach.
The first flaw is the Flaw of Averages.
Sam Savage's book The Flaw of Averages: Why We Underestimate Risk in the Face of Uncertainty is a good place to start. From a 2000 newspaper article, this cartoon says it all.
But the Flaw of Averages is deeper (pun intended) than the simple cartoon. Any time series of data, like the first chart can be used to forecast the possible outcomes from past performance. The Autoregressive Integrated Moving Average function in R, can easily shows that a time series like the first chart has a huge swing in possible outcomes. Below is an example, from a much better behaved time series. In this case the Schedule Performance Index of a software intensives system.
When we see Poorman's solutions to estimating future outcomes, be careful not to become that poorman by spending your customers money in ways that bankrupt the project.
Don't fall prey to simple and many times simple minded approaches to managing other peoples money. Math is required when dealing with probabilistic processes found on ALL projects. The future is always uncertain and NEVER the same as the past. This is the case even of production processes found at Toyota. Margin and risk reduction activities are always needed. Knowing how much margin and what risk reduction activities is past of the planning process for any non-trivial project.