# Membership Content

• ### Athlete Monitoring: Data Analysis and Visualization – Nominal Analysis

Athlete Monitoring: Data Analysis and Visualization Nominal Analysis To remind ourselves, nominal analysis is the same as the continuous (i.e., ratio scale) analysis, but with dummy coding, where each level is represented as an additional variable with 0 or 1. Thus, (rolling) proportions and counts are the methods used to analyze and visualize the nominal variables. In this video,...

• ### Athlete Monitoring: Data Analysis and Visualization – Continuous Analysis

Athlete Monitoring: Data Analysis and Visualization Continuous Analysis In this video, I will explain how to use the prepare() function from the athletemonitoring package. This function/package allows us to implement everything we have covered so far in this course: Dealing with missing entries Dealing with missing days Acute and Chronic rolling windows for smoothing and trends Rolling functions Group...

• ### Athlete Monitoring: Data Analysis and Visualization – Introducing athletemonitoring R package

In this module, I am going to introduce athletemonitoring package by demonstrating how easy is to perform common data analysis and deal with issues explained in the previous modules. I will also demonstrate the AthleteSR software, how to extract/sync the monitoring data from it and how to analyze it.

• ### Strength Training In Soccer

By on 05/03/2021

Strength training in soccer can often be the most challenging part of a strength and conditioning job. We always think about how to design proper load during the preparatory period and in-season period in soccer. Different scenarios, set and reps schemes, methods with load monitoring for each example are something that you will find in this article.

• ### Athlete Monitoring: Data Analysis and Visualization – Basic Visualizations

Athlete Monitoring: Data Analysis and Visualization Basic Visualizations In this video I will show you the basics of visualizations using ggplot2 package in R. GGplot2 package is the implementation of the Grammar of Graphics – a general scheme for data visualization which breaks up graphs into semantic components such as scales and layers. I am demonstrating the very basic...

• ### Athlete Monitoring: Data Analysis and Visualization – Data Wrangling in R – Part 2

Athlete Monitoring: Data Analysis and Visualization Data Wrangling in R – Part 2 In this video I am continuing the data wrangling in R using tidyverse (actually the dplyr) package. With few basic functions (i.e.,group_by(), mutate(), summarize(), filter(), select()), you can pretty much do 80% of data wrangling for the athlete monitoring purposes. For more informations, please refer to...

• ### Athlete Monitoring: Data Analysis and Visualization – Data Wrangling in R – Part 1

Athlete Monitoring: Data Analysis and Visualization Data Wrangling in R – Part 1 In this video we are beginning to do some data wrangling in R. I am explaining how to convert wide format to long format using pivot_longer() and vice versa using pivot_wider(). These data wrangling techniques are very important, but very hard to do in Excel (although...

• ### Athlete Monitoring: Data Analysis and Visualization – Hello World in R

Athlete Monitoring: Data Analysis and Visualization Hello World in R As the tradition demands, when learning a new programming language, one needs to create the Hello World program. In this version of the Hello World, I am explaining the atomic vectors classes in R, as well some of the specific of the language (such as vectorized variables, vectors recycling...