Data Exploration

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.

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.

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.