Hey guys,
This week I started to make my way through the Workout Wednesday for Power Bi challenges. This is an awesome extension of the Tableau community to Power Bi, providing challenges weekly to help extend your capabilities in the given software.
Hi guys,
In this weeks tutorial, we are expanding on the shot plot we created in R by adding some extra information to easily understand the plot and some context around it.
Hi all,
In this video I am going to show you an easy way of ranking players within a given context. For this I will create a measure that defines the percentile of players within the positional and season context of your choosing.
Hi all,
In my next video, I build upon the shot plot we created a couple of videos ago using R. This plot was built using ggplot2 and ggsoccer, which allows us to more accurately plot our data.
Hi all,
I thought I would follow on from my calculated column a few days ago and present a method for creating this as a measure as well. This measure uses the same DAX with a few minor differences due to how measures work and what they require in Power Bi.
Hi all,
Today I am going to focus more on a calculation rather than the visual. For this video, I will focus on a rolling average across matches, rather than days.
Hi all, last time we created a nice shot plot using the built-in scatter plot for Power Bi. Today we will take things a step further, using the R Custom Visual in Power Bi.
Hi all, following on from my last tutorial, I have now created something more football specific using the built-in scatter plot in Power Bi. This tutorial will take you through building a basic shot plot with shots coloured by outcome.
This post is aimed at walking through one method of comparing players upon specific metrics within a league setting. For this example I will use a couple of different metrics from the free StatsBomb FA WSL dataset.