Posts tagged with ‘Monitoring’

  • Velocity-Based Training: Signal vs. Noise

    By Mladen Jovanovic on 29/04/2015

    This is a R workbook using my older bench press data, in which I want to discuss Signal vs. Noise of Velocity-Based Training (VBT) measurements. This could be used for future reliability studies. The goal is to compare within-individual variations of velocity over load-velocity relationship (noise) with smallest practical velocity difference (in my opinion difference in velocities across nRM,…

  • AFL Game GPS Stats Analytics Workbook

    By Mladen Jovanovic on 14/03/2015

    Keith Lyons shared one game of data for one AFL game across four quarters for the #UCSIA15 course. I took some time to analyze it using R and created interactive and reproducible document (HTML) using knitr and markdown. You can download markdown file and CSV data file HERE

  • Training Stress Balance Workbook Con’t

    By Mladen Jovanovic on 12/03/2015

    In the previous video I was talking about two different methods of calculating Training Stress Balance and underlying assumptions. In this short addendum I will explain even better method of calculating TSB by combining good parts of previous two, discuss differences between calculus of TSB for “daily data” (e.g. training load) versus “occasional data” (e.g. HRV, readiness metrics) and…

  • Training Stress Balance: Two Methods of Calculation and Assumptions

    By Mladen Jovanovic on 07/03/2015

    In this blog post I will share with you the Excel workbook you can play with, and also the screen cast of me explain how to use the TSB method. I am also discussing different pro’s and con’s of two very similar ways of calculating Chronic and Acute Load and their assumptions.

  • How to Easily Make Sense of Your Training Load Data Using TSB

    By Mladen Jovanovic on 02/03/2015

    Training Stress Balance (TSB) is a concept I first heard of in Training and Racing with Power Meter by Allen and Coggan in 2010 and I immediately found it very interesting and tried to implementing couple of times. Learn how to implement it in this article.

  • Great Videos by Fusion Sport on Smartabase and Monitoring

    By Mladen Jovanovic on 15/12/2014

    Fusion sport has posted great videos on training monitoring on their YouTube channel. Some of the videos are from ASCA conference and some are from the latest international SMARTABASE user conference. I highly suggest you spend some time and check these videos out.

  • Training Load Monitoring – Seeing the Big Picture

    By Mladen Jovanovic on 06/12/2014

    I already mentioned it in the introduction, but basically we have three components: training load and reaction to that load, where current state and context moderates the interaction between the two.

  • Making Sense Out of the Session GPS Data

    By Mladen Jovanovic on 06/11/2014

    We collect more and more data and it is becoming increasingly difficult to make meaning out of it. What I would like to do is to present one simple way to make the meaning out of session GPS data using LOF and Clustering. Most GPS units produce multiple features p compared to number of observations n, so we are…

  • Interview With Rob Gathercole on Alternative CMJ Analysis and NMF

    By Mladen Jovanovic on 01/11/2014

    I have recently read two great papers on using alternative metrics when analyzing countermovement jump (CMJ) with the goal of evaluating both acute (neuromuscular fatigue, or NMF) and chronic training effects written by Rob Gathercole et al.

    I was amazed by how much new food for thought have been inside and how great was the novel combination of inferential statistics…

  • Banister Impulse~Response model in R [part 3]

    By Mladen Jovanovic on 22/10/2014

    Here is the another ‘playbook’, but this time on my own data set during high-frequency project I did in 2013. The data set features estimated 1RM using velocity (which I measure during the lifts). I have also measure Peak Velocity and Mean Power in CMJ w/20kg before lower body workouts. Those four are response variables.