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.

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Module 3

Module 4

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 – Missing data

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