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02/08/2015
Workbook: Change Point Analysis of HRV Data
By Mladen Jovanovic on 02/08/2015What is Change Point Analysis and why and how can you use it to help you out to make sense of your monitoring data? Find out more….
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15/07/2015
“Novel” Metric to Compare Athletes Using Their Load-Velocity Curve
By Mladen Jovanovic on 15/07/2015The question is simple: “What could be used to compare athletes across time and among themselves, taking into account the full load-velocity continuum, but taking into account their strength levels and body weight?”. You can find my answer here
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09/07/2015
R Playbook: Introduction to Multilevel/Hierarchical Models
By Mladen Jovanovic on 09/07/2015I wrote about mixed-level models before and I want to expand on it here. I sort-of finished Andrew Gelman’s Data Analysis Using Regression and Multilevel/Hierarchical Models and will continue to play with it. As far as I know Andrew uses the term multilevel models and avoids the terms fixed and random effect. This is a great book to have.
My…
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28/06/2015
Velocity Based Strength Training Workshop
By Mladen Jovanovic on 28/06/2015I knew I was preaching to the choir of experienced coaches, so I wanted to cover practical applications of the VBT as one “novel” way of prescribing and controlling training. I thought live testing would be much more appreciated than fancy graphs and theory, as it would also show the problems of “when the rubber meets the road” issues...
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29/04/2015
Velocity-Based Training: Signal vs. Noise
By Mladen Jovanovic on 29/04/2015This 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,…
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09/04/2015
Non-responders: are they really?
By Mladen Jovanovic on 09/04/2015Latest obsession of the researches is individual variation in training responses. The motivation behind this approach (known and emphasized in theory of training as individualization principle) is the creation of personalized medicine or personalized training.
Unfortunately, sometimes we see these individual differences (in treatment reaction), although they are artefacts of within-individual typical variation/error of measurement and regression to the mean.
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24/03/2015
R Playbook: Introduction to Mixed-Models
By Mladen Jovanovic on 24/03/2015I just started reading more about mixed-models (multilevel/hierarchical) and will use this as a playbook. Mostly because I learn the best by experimenting with the data and I suggest everyone to try to do the same. So please note that this is just a published playbook – if you find it useful, great, if you find some errors, please…
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14/03/2015
AFL Game GPS Stats Analytics Workbook
By Mladen Jovanovic on 14/03/2015Keith 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
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12/03/2015
Training Stress Balance Workbook Con’t
By Mladen Jovanovic on 12/03/2015In 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…
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07/03/2015
Training Stress Balance: Two Methods of Calculation and Assumptions
By Mladen Jovanovic on 07/03/2015In 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.