Visualisations

In part 6 of our match report series, find out how to dig a little deeper in to your data with a simple shooting analysis of the match.

In Part 5 of our match report series, we look back at what we have done so far and also add performance of our team throughout the season. Check it out today!

In Part 4 of our match report series we will show performance over recent matches to help visualise how the team is trending.

In this Python Tutorial I will plot event data from StatsBomb in a few different scenarios. This will include shots and passes from a single match. This data will be called using the StatsBomb python library and reformatted entirely in Python. I hope you enjoy.

In Part 3 of our match report series we add some stats to show the performance of the team in the given match.

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.

In Part 2 of our match report series we add a set-up page to filter our report page. This page will help to filter our reports to a given match for a team.

Hi all, Welcome to a new series of videos I am starting this week, creating a match report using StatsBomb event data. This series will run over a few videos, starting with this first video walking through loading data in to Power Bi.

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.