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AFL Game GPS Stats Analytics Workbook
By Mladen Jovanovic on 14/03/2015Keith 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
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Banister Impulse~Response model in R [part 2]
By Mladen Jovanovic on 18/10/2014In 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…
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Banister Impulse~Response Model in R [Part 1]
By Mladen Jovanovic on 14/10/2014Banister 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...
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What Are Biomotor Abilities?
By Mladen Jovanovic on 01/07/2014What Are Biomotor Abilities? Do you know what are biomotor abilities? How did they ’emerge’? The purpose of this video is to explain 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...
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Stats Playbook: What is Anscombe’s Quartet and why is it important?
By Mladen Jovanovic on 27/05/2014Stats 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...
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Playbook: Exploring decathlon competition data. Part 2
By Mladen Jovanovic on 18/05/2014Playbook: 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),...
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Playbook: Exploring decathlon competition data [Part 1]
By Mladen Jovanovic on 17/05/2014Playbook: 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...
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How to Analyze Movement Screen Tests? [Addendum]
By Mladen Jovanovic on 28/04/2014How 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...
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How to Analyze Movement Screen Tests?
By Mladen Jovanovic on 28/04/2014How 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...
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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...
