• # What Are Biomotor Abilities?

By Mladen Jovanovic on 01/07/2014

Do you know what are biomotor abilities? How did they ‘emerge’? The purpose of this video is to explain to you the ontology of biomotor abilities, the certain flaws of their use (The Root problem, buckets, periodization based on those qualities) and also provide statistical analysis in RStudio with simulated data using Factor Analysis and Hierarchical Clustering.

• # Stats Playbook: What is Anscombe’s Quartet and why is it important?

By Mladen Jovanovic on 27/05/2014

Stats Playbook: What is Anscombe’s Quartet and why is it important?   The following paragraph is take from Wikipedia “Anscombe’s quartet comprises four datasets that have nearly identical simple statistical properties, yet appear very different when graphed. Each dataset consists of eleven (x,y) points. They were constructed in 1973 by the statistician Francis Anscombe to demonstrate both the importance...

• # Playbook: Exploring decathlon competition data. Part 2

By Mladen Jovanovic on 18/05/2014

Playbook: Exploring Decathlon Competition Data Click HERE to read part 1   Clustering What we might be interested next is similarities between athletes, or in other words, which athletes have similar profiles. For that purpose we can use Hierarchical Clustering and Principal Component Analysis (PCA) which we are going to cover later HCWard <- hclust(d = dist(decathlon.normal),...

• # Playbook: Exploring decathlon competition data [Part 1]

By Mladen Jovanovic on 17/05/2014

Playbook: Exploring Decathlon Competition Data Data set Decathlon data set comes from FactoMineR package and represents two competitions: Decastar and Olympic Games. For this example we will explore only Olympic Games competition, so we need to subset the data. # Load the needed packages library(FactoMineR) library(ggplot2) library(reshape2) suppressPackageStartupMessages(library(googleVis)) # Load the decathlon data data(decathlon) # Subset the...

• # How to Analyze Movement Screen Tests? [Addendum]

By Mladen Jovanovic on 28/04/2014

How to Analyze Movement Screen Tests? Continuing on the previous post I had an idea to make further analysis. Please note that this is only a playbook. The idea is to transpose the data, and instead of clustering the athletes, we cluster the metrics or tests. What we are looking for is to find tests/metrics that are similar...

• # How to Analyze Movement Screen Tests?

By Mladen Jovanovic on 28/04/2014

How to Analyze Movement Screen Tests? In the recent post I shared the movement screen we designed and implemented. The question now is how to analyze the data and make real life decisions on it? What do we need to get from the analysis in the first place? In my (current) opinion we need these: Identify groups of athletes...

• # How to (Pretend to) Be a Better Coach Using Bad Statistics

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...

• # Analysis of Metabolic Power Data Using Power-Duration Profile in Team Sports

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…

• # Continuing with Statistical Power simulation in R

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).

• # “Power” to detect statistically significant effects based on sample size and magnitudes of effects

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.