By Mladen Jovanovic on 07/03/2017
I was asked by Rod Whiteley and Nicol van Dyk to contribute to the Aspetar Journal targeted topic issue that just got released off the press. I tried to combine my knowledge of predictive analytics, machine learning, philosophy of science, heuristics and practical experience as coach & sport scientist into one article. Hopefully I managed to create readable narrative.
By Mladen Jovanovic on 19/03/2016
Researchers usually try to randomize correctly, but sometimes the effect is not in the treatment, but maybe in the difference between group (pre-treatment) or during the treatment (for example volume of training) which should be similar/same so we can judge the effects of something else of interest (for example novel periodization). The solutions are to involve covariates in the…
By Mladen Jovanovic on 20/01/2015
Confidence intervals gives us the range of a given statistic when generalizing from a sample to a population. The simplest example could be mean of a sample (e.g. average height) – what we are interested in are the generalizations (or inferences) from this sample to a population (e.g. average height in population). Due the sampling error we are not…
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…
By Mladen Jovanovic on 24/09/2014
Importance of Context in Evaluating Wellness Questionnaires In the previous post I’ve shared the novel idea on how to ‘aggregate’ wellness categories into positive/negative wellness score. In the video below I talk about importance of context when evaluating the wellness data and I also provide couple of ways to quantify context or smooth it. If you are interested in the wellness questionnaires...
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 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.