Archive for March, 2021

  • Always Stay Critical – Papers Review 1

    By on 24/03/2021

    When comparing groups of people in the way how they move, we first must make sure we do it in a way the acquired data can actually tell us something meaningful. Understanding and acknowledging some pitfalls when designing, conducting, and interpreting the data is crucial to NOT conclude something misleading or wrong.

  • Athlete Monitoring: Data Analysis and Visualization – The dorem package

    By Mladen Jovanovic on 12/03/2021

    Athlete Monitoring: Data Analysis and Visualization The dorem package One of the best and the most practical papers I have read is Clarke & Skiba’s “Rationale and resources for teaching the mathematical modeling of athletic training and performance” (DOI: 10.1152/advan.00078.2011). Ever since reading that paper, I was interested in Banister’s Impulse-Response (IR) model and even wrote few R scripts...

  • 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.