How to Analyze Movement Screen Tests? [Addendum] - Complementary Training
How to Analyze Movement Screen Tests? [Addendum]

How to Analyze Movement Screen Tests? [Addendum]

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 (within our group of athletes) or that might  assess the same factor/quality. Not sure how much is this similar to PCA/EFA, so someone please correct me if I am being stupid here.

By the way, all tests used in movement screen are available here »

We need to load and munge the data

# Load the Movement Screen Data
movementScreenData <- read.table("", 
    header = TRUE, sep = ",")

# Remove first 8 columns
movementScreenData <- movementScreenData[, -1:-8]

# Scale the data
movementScreenData <- scale(movementScreenData)
movementScreenData <-

# transpose the data
movementScreenData.matrix <- as.matrix(movementScreenData)
movementScreenData.matrix <- t(movementScreenData.matrix)

We are going do Hierachical Clustering and mark 10 clusters (No clue why I used this number) using both ward and complete method

HCWard <- hclust(d = dist(movementScreenData.matrix), method = "ward")

plot(HCWard, xlab = "", ylab = "", yaxt = "n", main = "Ward Method")
rect.hclust(HCWard, k = 10)

plot of chunk unnamed-chunk-2

HCComplete <- hclust(d = dist(movementScreenData.matrix), method = "complete")

plot(HCComplete, xlab = "", ylab = "", yaxt = "n", main = "Complete Method")
rect.hclust(HCComplete, k = 10)

plot of chunk unnamed-chunk-3

What I miss now is how to combine Testing clusters with Athlete clusters? Anyone having a clude let me know.

I am a physical preparation coach from Belgrade, Serbia, grew up in Pula, Croatia (which I consider my home town). I was involved in physical preparation of professional, amateur and recreational athletes of various ages in sports such as basketball, soccer, volleyball, martial arts and tennis. Read More »