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.
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.
Following on from last weeks post on plotting passes, I thought it might be fun to plot the shots and locations of players using the shot freeze frame in StatsBomb data. For those of you that haven’t already, I posted a tutorial around how to extract the freeze frame data included here. So if you need to, take a look at that post before going further.
For this post, I have loaded the following packages to use and colours to be applied to our plots further down:
For this tutorial I am going to show you how to plot all the passes from a match on a pitch. We are going to go through this in a few steps and add some detail as we go. To start, the packages I am using in this tutorial are as follows:
library(tidyverse)
library(ggplot2)
library(ggsoccer)
I always use the entire tidyverse, some of you might like to only use particular packages, but for me this is what I prefer to do.
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.
What a month of Women’s football we had, culminating in a final where the USA were made to earn their fourth World Cup title. The Netherlands however, can be proud of their achievements, making their first final in their second world cup appearance. Let’s summarise the match quickly.
Team Name
Goals
Shots
Shots On Target
xG
Passes
Successful Pass %
Netherlands
0
6
4
0.
The Fifa Women’s World Cup is coming to a close and the race for the golden boot is tight, with the top 3 all still involved in the tournament (Table 1).
Player Name
Team Name
Goals
Shots
xG
Alex Morgan
United States
6
20
2.19
Ellen White
England
6
18
3.
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.