Performance Analysis - Complementary Training - Page 6

• # AFL Game GPS Stats Analytics Workbook

Keith Lyons shared one game of data for one AFL game across four quarters for the #UCSIA15 course. I took some time to analyze it using R and created interactive and reproducible document (HTML) using knitr and markdown. You can download markdown file and CSV data file HERE

• # Training Stress Balance Workbook Con’t

In the previous video I was talking about two different methods of calculating Training Stress Balance and underlying assumptions. In this short addendum I will explain even better method of calculating TSB by combining good parts of previous two, discuss differences between calculus of TSB for “daily data” (e.g. training load) versus “occasional data” (e.g. HRV, readiness metrics) and…

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

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.

• # Making Sense Out of the Session GPS Data

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

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]

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]

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]

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...