UEFA Euro 2024 Team Analysis

This analysis aimed to get a deeper insight into all the team's statistics that have participated in the Euro previously to see which team stands the best chance of winning the upcoming Euro in 2024. This analysis is relevant because we can use previous data to see which country is most equipped to win the Euro by looking at stats such as goals, caps, per position, and market value of the competing team.

Data and methods

The dataset was extracted from a public database called Kaggle, and the files were in CSV form as well. The next step of my project was data transformation, so I cleaned the data in Excel Firstly, I input a filter function to a row within the dataset. The reason for this was to identify any wrong formatting, or null and rename any table irrelevant to the dataset The data quality was good; I did not have to make many changes to the data within the row. However, except for for the Player Name row, it took considerable time to clean that row, I found out that, due to some football players having accents in their names, the data in that row had incorrect text and irrelevant special characters within the Player Name. To resolve this, I had to find the correct spelling of the name on Google. After I believed the data had reached good quality, I decided the best way to visualise the data and be able to perform data analysis was to load the data into Power BI.

Main findings and results

The main finding of this report is that England has the highest-value teams out of all the other teams in the Euro, but compared to the sum of goals, England ranks 10th on the list when it comes to scoring goals. This shows that the most expensive team does not always equal the best team. Also, higher player value does not equal elite player; many factors affect player value, such as age, position and demand of the football market as well. When we compare England's stats to the last Euro winner, Italy, Italy's squad ranked the 3rd lowest when it came to goal score and ranked 7th when it came to team market valuation. So this is a prime example of how high team valuation does not always result in being the best team However, team market value is still an important measure when it comes to winning the euros. If we specifically look at the last euro that Italy won, they won on penalty against England. When it comes to penalties, not always the best team wins as well, and another external factor which could lead Italy to victory could be that an important player was missing from the English team. In addition, when we look at the last four euro finalists, all teams that have competed in the final are national teams ranked 1–5 when it comes to team valuation.

Another significant finding was the lack of left-footed players and the lack of players who were both-footed as well. When drilling down into the pie chart "player preferred foot," only 24.24% of players were left-footed and 4.98 percent of players were both-footed. However, having left left-footed player in the team can be beneficial to team balance and can be a strategic advantage for a specific position. For example, left-footed players who played on the right wing scored a total of 258 goals compared to right-footed players who played on the right wing, who only scored a total of 72 goals. When we compare the difference between these two numbers it comes to a total of 186 goals. This could mean left-footed players are better than right-footed players that play right-wing. However, there is a limitation to this point of analysis, players might have different roles in the team instead of scoring goals. Also the quality of players and quality of the team. If a right-footed player plays for a lower-quality team, he will have fewer scoring opportunities than a left-footed player who plays for a better team. Moreover, the sample size is small, if the sample size is bigger there is more opportunity for statistical tests like ANOVA and t-tests to be conducted.

Reccomadation

A recommendation for this project is to include more statistical tests to discover more in-depth findings. Moreover, there was not enough dimension to measure team performance, and the sum of goals was not enough to measure team performance. Dimensions that could be included to provide a more in-depth analysis regarding team performance could be goals conceded, goals per match average, shot per average, possession and shot fact per match. These are important measures to validate a team's performance. Due to the lack of dimension to measure a team performance. I could not prove a hypothesis for which team would win the euro as the sum of goals is not enough to measure a team's performance.