Analyzing the Beautiful Game: The Goal-Getter's Guide to the 2022 World Cup

The objectives of this project were:

1. To examine how host countries have performed in World Cups over time.
2. To examine historical team performance and determine which teams underperformed at the 2022 World Cup.
3. To compare the 2022 winners, Argentina to previous winners.

The Dataset

This dataset was downloaded from the Maven Analytics Digital Playground. It contains seven csv files:

• 2022_world_cup_groups contains data on 2022 World Cup teams, their respective groups and FIFA rankings.
• 2022_world_cup_matches, which is basically just a 2022 WC fixture list. It contains dates and match pairings.
• 2022_world_cup_squads contains data on individual players playing for qualified WC teams.
• data_dictionary contains definitions for all fields used in this dataset.
• international_matches contains data on international matches from 1872 to 2022.
• world_cup_matches contains data on every World Cup match since 1930 to 2018.
• world_cups is a condensed list of World Cup winners, hosts, goals, qualified teams and matches played.

Steps

Step 1: To start off, I asked myself, who would be the consumer of the FIFA World Cup dashboard? The answer, casual football fans who would want to know interesting facts about the World Cup, and ardent football fans who would want to settle debates using statistics.

Step 2: I mapped a strategy for analyzing a data. I wanted to tell the story of the FIFA World Cup from the very beginning while interacting with interesting facts of the WC along the way, and finishing it with a story about the 2022 World Cup.

Step 3: I used the Excel Query editor to clean the 2022 World Cup data and create a data model. I had to optimize some player names as they were displaying non-user-friendly characters e.g ?lkay Gündo?an to llkay Gündogan I then used a Power Pivot to explore different facts about the teams and players at the World Cup.

Step 4: After taking a look at the world_cup_matches and world_cups data, I started prepping the data using Excel formulas. I also designed a points system that would help every World Cup team by its performances since 1930.

The points system awarded points as follows:

Appearance: 1

Win: 3

Draw: 1

Loss: 0

Goals Scored: 2.2

Goals Conceded: -1.1

Step 5: I then used Excel formulas to create dynamic tables that would help create dynamic charts that would go on to the dashboard.

Step 6: Finally, I designed my dashboard to tell a story of the World Cup to both casual and ardent football fans.

Insights

1. Countries are more likely to perform well when they host the World Cup.

2. Top performers at World Cup tournaments do not necessarily win the World Cup. Brazil in 1950, 'The Magical Magyars' of Hungary in 1954, and Portugal in 1966 are the most glaring examples.

3. Countries such as Brazil, Germany, Argentina, France, and Italy have amassed so much capital in previous World Cups that they go into every World Cup as favorites to win.