By Mladen Jovanovic on 25/03/2014
How to (Pretend to) Be a Better Coach Using Bad Statistics Here is a simple scenario from practice: Coach A uses YOYOIRL1 test and Coach B uses 30-15IFT (for more info see paper by Martin Buchheit, which also stimulated me to write this blog) to gauge improvements in endurance. Coach A: We have improved distance covered in YOYOIRL1 test...
By Mladen Jovanovic on 06/03/2014
This is the idea I got from the Training and Racing with a Powermeter book by Hunter Allen and Andrew Coggan. It is an excellent and must read book on cycling, but also great book about endurance training in general…
By Mladen Jovanovic on 13/02/2014
In the last blog post I created a simple simulation of statistical power (probability to identify effects when they are really there) calulation depending on the sample size and effect size (Cohen’s D using Will Hopkins effect levels).
By Mladen Jovanovic on 12/02/2014
I was going through magnitude-based inferences materials by Will Hopkins and I am playing with R simulations. I wanted to see how many times I am able to detect statistically significant effects (p<0.05) depending on magnitude of effects (expressed as Cohen's D, and using Will Hopkins levels) and sample sizes.
By Mladen Jovanovic on 31/12/2013
Playbook: Understanding MODERATION Through Simulation Introduction I recently spoke with my college professor regarding the understanding of statistics, and I remarked that I learn the statistical concept the best (and with full comprehension) through simulation. He remarked that I might be in the minority of students (p<0.01 – see what I did here?). Not sure if this is true...
By Mladen Jovanovic on 26/12/2013
To continue the previous How to visualize test change scores for coaches blogpost, here is another way to visualize individual change scores without falling for group averages. This way we can easily see individual ranks in both pre- and post- test, along with change score (which is also color coded). Quite easy to identify the outliers.
By Mladen Jovanovic on 19/11/2013
In the following R and knitr experiment/blog post I will be documenting my play with correlation and inferences. I am just reading Discovering Statistics Using R by Andy Field and I am trying to code some staff from the book, plus experiment and see how inferential statistics work.
By Mladen Jovanovic on 12/11/2013
I am awaiting review and opinions/critiques from statistics wizard Will Hopkins for my How to visualize test change scores for coaches, but even on his first look he pointed to one small error I did in simulating typical error for vertical jump. It is not huge error, but it is an error in representing typical error in measurement.
By Mladen Jovanovic on 09/11/2013
Coaches are not interested in making inferences to a population, statistical significance, nor mean values (P<0.01). What they are interested are individual athletes, their reactions to training and what are practically significant and meaningful changes.
By Mladen Jovanovic on 26/10/2013
Over the years there were couple of tries to monitor fatigue real-time in team sports, such as soccer. The most simple one was to use heart rate (HR) data and see if the players were getting tired by checking if the HR was getting higher and higher. Long story short – this doesn’t work and I have no clue…