Quote:
Originally Posted by formula_2002
lets just look at "calculate_pace_figures.py"
It would be helpful to me if your present your idea in the form of a "conclusion", without using code.
I assume you want our comments on your ideas, not the code.
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Understanding the details of the metrics is not necessary for the current exercise; what I am developing here is an application that can receive any figure that can be expressed as a number and decide whether it has some predictability value or not. A completely useless metric should be easy to spot as it will not affect the performance if removed from the pattern.
To answer the question though, the pace figures implemented in the file you are mentioning, measure how quickly the second call of the leader of the race was ran. The scale used, ranges between 0 -100 following the normal distribution (the figure is a derivative of z-score); speed figures (as stated in the documentation) are calculated using a similar methodology to Beyer’s, (for more details you can read the notebooks).
I do not think that knowledge of programming is necessary to understand the details of this program; I could easily hide most of the implementation details from the notebooks but have placed them for documentation reasons more than anything else.
