Athlete Monitoring: Data Analysis and Visualization – Rolling Functions - Complementary Training
Athlete Monitoring: Data Analysis and Visualization – Rolling Functions

# Athlete Monitoring: Data Analysis and Visualization

## Rolling Functions

Rolling functions are useful in describing trends over time. These can be any type of descriptive functions, like means, medians, standard deviations, and so forth.

In sports science, we usually use rolling windows of short duration (i.e., acute; 3-14 days ) and long (i.e., chronic; 28-42 days). This allows us additional descriptive metrics like Training Stress Balance (TSB; the difference between acute and chronic mean), acute to chronic ratios (ACR; the ratio between acute and chronic mean), and so forth.

There have given a bad rep recently due to their bad use in predicting overuse injuries due to bad load management. But, if we approach them as simple descriptors, they are useful visualization techniques that can add extra information about trends without giving them predictive powers.

In this lesson, besides discussing rolling functions, I mention the issues of having an unbroken chain of observations (i.e., without missing days) and the problem of nominal variables, which is covered in the next lecture.

#### Athlete Monitoring Course

Take advantage of our promotional offer

### Learn How to Analyse All Your Training, Performance and Testing Data

OR

\$350 per year

I am a physical preparation coach from Belgrade, Serbia, grew up in Pula, Croatia (which I consider my home town). I was involved in physical preparation of professional, amateur and recreational athletes of various ages in sports such as basketball, soccer, volleyball, martial arts and tennis. Read More »

Welcome to Complementary Training Community! Forums Athlete Monitoring: Data Analysis and Visualization – Rolling Functions

Tagged:

This topic contains 0 replies, has 1 voice, and was last updated by  Mladen Jovanovic 3 years, 4 months ago.

You must be logged in to reply to this topic.