BiscuitChaserFC

I have more time on my hands so thought it would be good fun to show some steps from creating creating scatters to plotting event data to show those that show up favourably in initial searches. The question that I posed myself was: "How can we go beyond scatters to learn more about a players output?" There has been an increase in scatter plots to show players that excel, however they don't tell the whole story. As always, I'm still learning code myself so I'm sure there is code in here that will upset experienced coders! Sorry! Anyway, a rough idea of how this will look - - download data from fbref.com - plot a scatter using the data - filter the top 9 performers for one parameter - plot the event data - compare the player outputs Lets goooooooooooo Lets fire up: library(StatsBombR) library(tidyverse) library(ggsoccer) library(ggrepel) We could create the P90 data for the WSL ourselves by using the StatsBomb data, however I'm being l

Following my first tutorial loading R and importing StatsBomb data to plot passes in a specific FAWSL match I've had loads of good feedback! ( Tutorial One ) There seems to be a general appetite for a second part how the plots can be progressed and further information added - so lets give it a go! As always, I will caveat that I'm no expert in R and have been self teaching since Christmas 2019 - as such I'm presenting something that works for me but may not be the *entirely* correct way of doing things! Anyway,  below was the final pass plot we ended up with after the first tutorial. Great that we've plotted the passes, but what can we learn from it? What could a practical application be if we wanted coaches/scouts to take away insight? Looking at below, for me, the plot infers a high density of passes on the right wing in the final third with regular crosses (in this specific match!) along side regular passing actions in the Left Centre Back location. This is