Betting on the right team

[yudhisteer]

Continuing from my previous post on extracting nitty-gritty insights from the FIFA World Cup data, I would now like to make some analysis on which team I would bet my money on.

Compared to a boxplot, the violin plot allows us to see the distribution of our data more clearly. For example, we can see we have bimodal distributions for the Away Team Goals in 1934, 1962, 1966, and 1978. Observe that we have long-tail distributions after the third quartile for the Home Team, but this reduces after 1982. Thereafter, the distribution for both teams seems to be similar.

Then, with a stacked bar chart we observe the frequency of countries who won the World Cup but also those countries who missed by a little. From the graph, Brazil is the most successful World Cup team with five titles. But what is more interesting is to see how many times Germany has been 1st runner-up and 2nd runner-up.

In the previous post, we observed that a German holds the record for most goals scored by a single player. Now, we see that Germany reached the finals and semi-finals the greatest number of times. This is clearly an indicator of the strength of the team.

I acknowledge that no statistical inferences have been done on the players and the teams over the years to demonstrate mathematically which team to bet on, but from the analysis we observe Germany to be a ferocious team. Hence, I will act on my “hunch” and bet on Germany for the 2022 World Cup.

This is #day2 of my #100dataviz projects on data science and storytelling with data. I welcome feedback of any kind or ideas on any topics which you would want me to explore. Thank you for reading!