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. This will include cards which total up shots and are coloured based on the if this game was above or below an average for the season. We will also have a matrix at the bottom, highlighting how the shots were created such as from a corner or open play.
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.
As always, subscribe here for more videos and to help you Power Performance Through Data.
Enjoy the video and if you have any questions, please leave a comment below.
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.
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.