
This project contains all the posts exploring data in different ways. Each post will attempt to build on the last and provide more advanced ways of looking at the data.

Josh Trewin
Data Scientist
I’m a data scientist, learning my way through R / Python and applying to football data from StatsBomb, provided for free through GitHub. Follow my journey on here or Twitter to find out when I add new content.
Related
Posts
A look at attacking and defensive effectiveness
The use of scatter plots can provide a lot of information in a quick snapshot. In this blog post I provide an overview of some uses the scatter plot can have in comparing relational data to see where a team fits compared to their opposition.
The missing piece - Throw-ins
In football, set-piece scenarios are often considered as threatening and given considerable time towards mastering in the hope of positive outcomes. However, one piece teams often under value are throw-ins, particularly when they occur in the final third of the pitch. For example, from a total of 2351 only 54 were score, making it only a 2.3% conversion rate throughout the FA WSL.
When we convert that to a team based summary (Table 1), we find that only Arsenal converted more than 5% of their throws in the final third in to goals.
Exploring Passing Networks
The analysis of passing in elite soccer is common place. Often media shows simple pass counts and pass completion rates but there are much better ways of viewing this type of data. For example, we can create a passing network based on the average position of players when making a pass. We can also show where the number of passes they make between themselves and another player. This could be extremely powerful data to show how the passes between players and from where on the pitch.