Click the Original, Code and Reconstruction tabs to read about the issues and how they were fixed.

Original


Source: Visual Capitalist (2021).


Objective

The objective of the original visualisation is to show that Esports companies are rapidly growing in market value and may soon challenge the established sports teams in the NFL, NBA, NHL, and MLB for market share.

Target Audience

The target audience of the visualisation is individuals with an interest in Esports, particularly those considering investing their time and money into the sport.

Issues

The three main issues of the chosen visualisation are:

  • Issue 1: The visualisation uses size to depict quantity. For example, NFL is represented by a football, NBA is represented by a basketball, MLB is represented by a baseball, and the larger the y-axis value, the larger the size of the object. This makes it difficult to accurately compare where each observation belongs on the graph. If there was no labelling of each observation, it would be impossible to tell which observation has a higher value, particularly among the observations at the bottom of the graph.
  • Issue 2: The visualisation has no labelled x-axis. Observations seem to be randomly placed along the x-axis, with ‘T1’ on the left and ‘100 Thieves’ on the right, and all observations in between having y-axis values both higher and lower than the two outer observations. This confuses the viewer and makes them question why the visualisation was made this way - is there some variable missing to the naked eye?
  • Issue 3: The visualisation provides a limited data comparison between Esports and the four biggest US sports leagues. Franchises in the NFL, NBA, MLB, and NHL have existed for over 50 years, so have had more time to build a following and become a major presence in the sports financial market. Esports companies were founded only in the 21st century and have not had the time to build up a big following and increase their market value. This negatively impacts the visualisation by making it seem like Esports companies are not valued as highly as the major sports leagues.

Reference

Code

The following code was used to fix the issues identified in the original.

library(ggplot2)
library(readxl)
library(scales)
team <- read_excel("teams.xlsx", sheet = "All")
team$Sport <- factor(team$Sport, levels = c("Esports", "NFL", "MLS", "NHL", "NBA", "MLB", "NASCAR"))

t1 <- ggplot(data = team, aes(x = reorder(Sport,Value,median), y = Value))

t1 <- t1 + geom_boxplot(fill = "#dfeae9",colour = "#c4cfce", outlier.shape = NA) +
  geom_jitter(width = 0.2, alpha = .3) +
  scale_y_continuous(name="Team Value", labels = label_number(scale = 1/1000000000, suffix = " Billion", prefix = "$"), breaks = c(1000000000,2000000000,3000000000,4000000000,5000000000)) +
  xlab("Sports League") +
  ggtitle("Esports Company Values Compared to Established Sports in the US") +
  stat_summary(fun = median, colour = "red", geom = "point", shape = 15) +
  theme_minimal()

Data Reference

Reconstruction

The following plot fixes the main issues in the original.