Archive for 2021

  • Athlete Monitoring: Data Analysis and Visualization – Shiny AthleteSR Dashboard

    By Mladen Jovanovic on 12/03/2021

    Athlete Monitoring: Data Analysis and Visualization Shiny AthleteSR Dashboard In this video, I am walking you through the Shiny AthleteSR Dashboard. As explained in this and the previous video, this course and the athletemonitoring package were the result of my attempt to build this mockup dashboard for the AthleteSR developers. Everything covered in this course is implemented in this...

  • Athlete Monitoring: Data Analysis and Visualization – AthleteSR

    By Mladen Jovanovic on 12/03/2021

    Athlete Monitoring: Data Analysis and Visualization AthleteSR The idea for this course started when I developed an athlete monitoring Shiny dashboard for the AthleteSR, which is our app for athlete management (FREE for the Complementary Training members). The goal was to have a formalized way to analyze and visualize the data for our developers, so they can implement it...

  • Athlete Monitoring: Data Analysis and Visualization – Nominal Analysis

    By Mladen Jovanovic on 12/03/2021

    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

    By Mladen Jovanovic on 12/03/2021

    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

    By Mladen Jovanovic on 12/03/2021

    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.

  • Sports Vacancies in March 2021

    By on 28/02/2021

    Here 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!

  • Validity and Repeatability of the YoYoIR1 and 1000TT: Re-analysis of the Clancy et al. paper

    By Mladen Jovanovic on 25/02/2021

    In 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.

  • Athlete Monitoring: Data Analysis and Visualization – Basic Visualizations

    By Mladen Jovanovic on 19/02/2021

    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

    By Mladen Jovanovic on 19/02/2021

    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...