How to quickly visualise Bundesliga Shooting Data

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1. Introduction

In this R Markdown document, we’ll walk through the quick and easy steps to visualise Bundesliga 22/23 shooting data.

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2. Load packages

We start by loading the required libraries, including tidyverse for data manipulation and worldfootballR for accessing football match data.

library(worldfootballR)

## Warning: package 'worldfootballR' was built under R version 4.2.3

library(tidyverse)

## Warning: package 'tidyverse' was built under R version 4.2.3

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## ✖ dplyr::filter() masks stats::filter()
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

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3. Import Data

We load the Bundesliga 22-23 data, specifying the country, gender, tier, and season-end year.

Bundesliga_Team_Shooting_Data <- fb_season_team_stats(country = "GER", gender = "M", tier = "1st", season_end_year = 2023, stat_type = "shooting")

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4. Filter

We filter the newly created data frame to feature only “team”.

Bundesliga_Team_Shooting_Data <- Bundesliga_Team_Shooting_Data %>% 
  filter(Team_or_Opponent == 'team')

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5. Plot

We can now visualise a basic graph to display this season’s shots and shots on target for each team.

ggplot(data = Bundesliga_Team_Shooting_Data, aes(x = Sh_Standard, y = SoT_Standard )) +
  geom_point( data = Bundesliga_Team_Shooting_Data,
              aes( x = Sh_Standard, y = SoT_Standard)) +
 facet_wrap(~ Squad)

6. Additional Layering by adding expected goals

If you would like to take an extra step to display more key information as a plot, we can manipulate the size of the points within the plot to reveal the expected goals. The larger the point, the more likely the team was to score goals throughout the season.

ggplot(data = Bundesliga_Team_Shooting_Data, aes(x = Sh_Standard, y = SoT_Standard )) +
  geom_point(data = Bundesliga_Team_Shooting_Data,
              aes(x = Sh_Standard, y = SoT_Standard, color = Squad, size = xG_Expected)) +
  theme(legend.position = "right")

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