How To Do Football Data Analysis – Creating Passing Sonars in Python

The use of data is not new to the field of sports.

Originating from the American sports of baseball, “Sabermetrics” slowly spread to the other sports in the mainland – football, basketball, and hockey.

While soccer was a little wary of embracing the change so easily, it can’t be denied that analytics has finally permeated the traditional sport as well.

As competition grows, teams are looking for the competitive advantage that they can gain over others – especially with the globalisation of the sport and the advent of new minds and ideas to the game.

Today, the amount of data being collected is huge and almost every top division team has a dedicated team who are tasked with making sense of this data in a manner which helps the team.

As the application of data analytics has increased, so has the supply of the requisite data.

With companies such as Statsbomb providing event-level data for free to encourage greater participation, the casual viewer is privy to the same data that the scientists working in the top clubs of Europe are.

However, data doesnt speak on its own and the way humans are wired we naturally prefer to look at a picture rather than a spreadsheet full of numbers.

Data visualisation is an art and one you can learn very easily.

Keeping that in mind, in this data analysis piece, we’re going to use the Statsbomb data and create a very simple and popular visualisation known as passing sonar.

What is a passing sonar?

Passing sonars are the brainchild of Eliot McKinley, from americansocceranalysis.com.

It is essentially a bar graph on a polar axis and it can be used to give a very good idea of a particular player’s passing.

As can be seen above, it has a few different features – namely, the bars, the angles, and colours.

Normally, the bar length indicates the average length of the pass, the angle denotes the angle of said pass.