Athlete Monitoring: Data Analysis and Visualization – Missing data

Athlete Monitoring: Data Analysis and Visualization

Missing Data

In real-life, missing data are the norm. Unfortunately, not all missing data should be treated the same. It is of utmost importance to understand the types of missing data, but more importantly to understand the process behind the data collection as well as context.

In this lecture, I will explain the three major types of missing values:

  • Missing completely at random (MCAR)
  • Missing at random (MAR)
  • Missing not at random (MAR)

And I will explain how these types manifest themselves in the most common athlete monitoring data.

Athlete Monitoring Course

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Description

Module 1

Module 2:

Module 3

Module 4

Appendix

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 »
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Welcome to Complementary Training Community! Forums Athlete Monitoring: Data Analysis and Visualization – Missing data

This topic contains 1 reply, has 2 voices, and was last updated by  Bojan Makivic 6 months, 2 weeks ago.

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  • 12/03/2021 at 00:00 #31503

    Great overview in terms of missing values and how to deal with different types of missing data. Mladen again is using practical real lie examples in order to explain the topic in more intuitive way.

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