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How to (Pretend to) Be a Better Coach Using Bad Statistics
By Mladen Jovanovic on 25/03/2014How 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...
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Analysis of Metabolic Power Data Using Power-Duration Profile in Team Sports
By Mladen Jovanovic on 06/03/2014This 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…
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Continuing with Statistical Power simulation in R
By Mladen Jovanovic on 13/02/2014In 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).
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“Power” to detect statistically significant effects based on sample size and magnitudes of effects
By Mladen Jovanovic on 12/02/2014I 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.
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Playbook: Understanding MODERATION Through Simulation
By Mladen Jovanovic on 31/12/2013Playbook: 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...
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How to Visualize Test Change Scores for Coaches [Part 2]
By Mladen Jovanovic on 26/12/2013To 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.
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Understanding Inferential Statistics Using Correlation Example
By Mladen Jovanovic on 19/11/2013In 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.
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Quick correction in simulating Typical Error
By Mladen Jovanovic on 12/11/2013I 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.
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How to Visualize Test Change Scores for Coaches
By Mladen Jovanovic on 09/11/2013Coaches 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.
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Real-Time Fatigue Monitoring using Metabolic Power and CP/W’
By Mladen Jovanovic on 26/10/2013Over 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…
