As my HS chem teacher like to remind us, "to get the right answer, you have to ask the right question."

Various questions in this thread seem to be asked. So each answer is likely correct for the implicit question asked.

My question would be (in its crudest form): what percentage of an estimate do I have to add (or implicitly subtract) from an initial estimate to get an estimate based on past performance? Note that I am specifically not interested in "rating" the developer for a given sprint (the variance for a single data point is by definition unknown).

So my answer would be:

adjustment_percent = (all_actuals - all_estimates) / all_estimates

If my numbers are:

estimate     actual
12                  2
33.5              65

Totals: 45.5   67
adjustment_percent: 47

Then I would average the adjustment _percents. Note that with n large enough, a variance could be found for this number, leading to an estimate of margin of error.

Now, this gets interesting. If I have n large enough, I can look for differentiating factors, such as "big" vs "small", or "greenfield" vs "rewrite", dev platform, etc. With enough data for a single developer I could use differential Multiple Regression analysis to determine the amount of variance my suspect factors contribute. I could also use the dataset from all developers to see how much variance is contributed by which of the same factors and also by which developers. Etc.

All that said, the real answer to the management question is to do as Rands (http://randsinrepose.com/) suggests: meet weekly with each developer and get to know them. Then you don't need the numbers to know the answer to the question, as you will already know.

Hank

>If you're trying to find a deviation within a context, then you'd have to define the "norm" and the "context". In this case, the norm (i.e. mean) would be your estimate number, but what is the context (i.e. range)?
>What if the actual nr of hours was 7 instead of 60? (7 is off by the same number of hours from "estimated" as 60, but in the other direction). Then your formula and his formula would give you the same result.
>His formula is defining the range in which the mean is the amount estimated, the low end is 0 and the high-end is 67 (i.e. 33.5 * 2). With his formula you get the same accuracy for actual 7 and actual 60.
>
>
>>A follow-up....and because I respect you as the "just the facts, Sgt Joe Friday, no nonsense", then maybe you can make sense of this.
>>
>>I found how someone is calculating this....again, for a bad sprint of 60 hours on an original guess of 33.5 hours, I figured the accuracy was 55.83%. But I've got a project manager saying it's about 21%. Here is how the PM is calcing it...this seems more like a factor than an accuracy percentage.
>>
>>1 - abs ( amount estimated - remaining work left - time spent)
>> -----------------------------------------------------------------------------
>> amount estimated
>>
>>
>>so that....
>>

>>
>>1 -   abs (  33.5  - 0 - 60)
>>       ----------------------------
>>              33.5
>>
>>1 -  ( 26.5)
>>      ----------  
>>        33.5
>>
>>1 -  .791
>>
>>
>>accuracy:   21%
>>
>>