Statistics - Complementary Training - Page 5

• # How to Easily Make Sense of Your Training Load Data Using TSB

By Mladen Jovanovic on 02/03/2015

Training Stress Balance (TSB) is a concept I first heard of in Training and Racing with Power Meter by Allen and Coggan in 2010 and I immediately found it very interesting and tried to implementing couple of times. Learn how to implement it in this article.

• # Effect of Typical Variation of a Test on Confidence Interval

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…

• # Making Sense Out of the Session GPS Data

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…

• # Interview With Rob Gathercole on Alternative CMJ Analysis and NMF

By Mladen Jovanovic on 01/11/2014

I have recently read two great papers on using alternative metrics when analyzing countermovement jump (CMJ) with the goal of evaluating both acute (neuromuscular fatigue, or NMF) and chronic training effects written by Rob Gathercole et al.

I was amazed by how much new food for thought have been inside and how great was the novel combination of inferential statistics…

• # Banister Impulse~Response model in R [part 3]

By Mladen Jovanovic on 22/10/2014

Here is the another ‘playbook’, but this time on my own data set during high-frequency project I did in 2013. The data set features estimated 1RM using velocity (which I measure during the lifts). I have also measure Peak Velocity and Mean Power in CMJ w/20kg before lower body workouts. Those four are response variables.

• # Banister Impulse~Response model in R [part 2]

By Mladen Jovanovic on 18/10/2014

In the previous part I’ve introduced multivariate modeling of impulse and response using Banister model. In this part I will continue with exploration of this model, mainly visualizing reaction predicted by the model on standardize impulse (load) and compare prediction using multiple impulses. I will use same data sets: one by Skiba and one randomly generated as in first…

• # Banister Impulse~Response Model in R [Part 1]

By Mladen Jovanovic on 14/10/2014

Banister Impulse~Response Model in R Before you start reading this post, please read EXCELLENT paper by Clark and Skiba, especially on the topic of Banister impulse-response model. I decided to write code in R, but also allow for multivariate analysis (where impulse can be multiple variables, as is the case in sports) which can speed the thing...

• # Where to start with Machine Learning and Data Mining?

By Mladen Jovanovic on 28/09/2014

I have just started reading about machine learning and predictive analytics in general, so I wanted to share my planned journey for those who wish to start the same.

• # Importance of Context in Evaluating Wellness Questionnaires

By Mladen Jovanovic on 24/09/2014

In the previous post I’ve shared the novel idea on how to ‘aggregate’ wellness categories into positive/negative wellness score.

• # New Way to Calculate Wellness From Questionnaires

By Mladen Jovanovic on 16/09/2014

In this video, I go over the usual problems of calculating total Wellness from different categories and I present a new way to calculate total wellness score that is more sensitive.