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Athlete Monitoring: Data Analysis and Visualization – Introducing athletemonitoring R package
By Mladen Jovanovic on 12/03/2021In 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.
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Strength Training In Soccer
By Aleksa Bošković on 05/03/2021Strength 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.
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Sports Vacancies in March 2021
By Mihailo Tomic on 28/02/2021Here are the best sports jobs in the market. During March, we will continuously update the list. Please share, and let’s get the word out!
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Validity and Repeatability of the YoYoIR1 and 1000TT: Re-analysis of the Clancy et al. paper
By Mladen Jovanovic on 25/02/2021In the following two videos and a PDF report, I am going to re-analyze the paper by Clancy et al. using bootstrap magnitude-based approach (using bmbstats package) and mixed-effects model in R language.
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Athlete Monitoring: Data Analysis and Visualization – Basic Visualizations
By Mladen Jovanovic on 19/02/2021Athlete 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...
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Athlete Monitoring: Data Analysis and Visualization – Data Wrangling in R – Part 2
By Mladen Jovanovic on 19/02/2021Athlete 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...
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Athlete Monitoring: Data Analysis and Visualization – Data Wrangling in R – Part 1
By Mladen Jovanovic on 19/02/2021Athlete 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...
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Athlete Monitoring: Data Analysis and Visualization – Hello World in R
By Mladen Jovanovic on 19/02/2021Athlete 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...
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Athlete Monitoring: Data Analysis and Visualization – Aggregating data in Excel
By Mladen Jovanovic on 19/02/2021Athlete Monitoring: Data Analysis and Visualization Aggregating data in Excel We are going to use R and R-Studio thorough the rest of this course (as well as a little bit of Microsoft Excel), so in this lecture, I will show you how to install them on your computer. About Mladen Jovanovic Mladen Jovanovic is a physical preparation coach from...
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Athlete Monitoring: Data Analysis and Visualization – Module 3
By Mladen Jovanovic on 19/02/2021We are going to use R and R-Studio thorough the rest of this course (as well as little bit of Microsoft Excel), so in this lecture I will show you how to install them on you computer.