Growing up, I was a major fan of Chelsea. I lived and breathed blue, and to this day I do believe that London will always be blue. But, I have always been wondering why they always had some good years and some bad years. Suddenly, they would have a couple of good seasons then the next year they would be completely off. This made me question, why? What are the factors that can result in my favorite team losing so much and not acting on its high record? This drove me to get to know the specifics, to dive deeper than just watching my favorite football team play, but by understanding how they play and why they perform the way that they do. The English Premier League is one of the most-watched games in the world, with fans from all parts of the world since they are being aired live and it always consists of teams aiming to be the best in the world.
The Premier League is the pinnacle of professional soccer in England and attracts some of the world’s most talented players. It is one of the most renowned sports leagues in the world as a result, and several of its teams participate in the UEFA Champions League. Given the intense rivalry, it is unsurprising that several variables influence a team’s performance. This analysis aims to investigate some of the main variables affecting Premier League clubs’ score goals and identify which are most crucial. We will examine statistics on goals scored, predicted goals, age, progressive carries and progressive passes, possession, and assists in providing an answer to this topic. I will also compare these numbers to find patterns and connections that may help us understand why certain teams are more successful than others.
To visualize the data, I used column graphs. Column graphs are useful for displaying quantitative data over a period of time. The data I analyzed was from the 20 Premier League teams, so I used a separate column graph for each team to show their goals scored, expected goals, age, progressive carries and progressive passes, possession, and assists. This allowed me to identify correlations between each statistic and the team’s goals scored. Column graphs also allowed me to show the relative size of each statistic in comparison to one another. By doing this I could identify which factors had the greatest effect on a team’s number of goals scored and to what degree. By displaying the data in this way, it allowed me to easily identify the differences between teams and draw conclusions about which factors were the most important for a team’s success in scoring goals.
With all that said, through analyzing the data, I have come to see that the most shocking finding is the goals and expected goals data set. With their individual goals and predicted goals displayed on the y-axis, the teams are ordered from left to right along the x-axis. Arsenal, the side with the most goals scored in the league, outperformed expectations by scoring 56 goals as opposed to the projected 50.3. Similarly, Manchester City scored 64 more than their predicted 54.1 goals. Conversely, the Wolves only scored 17 goals, less than their predicted 24.9 goals. Evidently, the well-known teams have seen to reach or have a close result to their expected goals throughout the 2021-2022 English Premier League season. Thus, the ratio for teams such as Tottenham and Manchester City is not comparable to the performance of teams such as Southampton. The graph demonstrates that although some teams have scored more goals than anticipated, others have scored fewer. This could result from several things, including chance, team skill, and strategy. Ultimately, it’s crucial to remember that the data points only represent a moment throughout the season and that the teams’ performances may alter substantially as the campaign progresses.
When seeing the shocking results from the gathered data above, made me question other things too such as progressive carries or possession of the teams. But, within the context of expectancy, this brought up the idea of analyzing the Assists and Expected Goals of the teams too. The graph below reveals that while all teams had fewer assists than expected, Leicester City and Liverpool had the highest rate of assists and expected assisted goals, followed by Manchester City and Arsenal. Aston Villa, Bournemouth, and Crystal Palace have the lowest assist rate to expected assisted goals. The data shows that some teams are advantageous regarding assists and expected assisted goals, while others have disadvantages. This knowledge can be used to improve team tactics and prepare for future matches, and help them with their future in the English Premier League.
I have asked for an expert opinion whilst looking through these datasets and really wanted to know why the numbers appeared as they did. I asked Mohammed Saadoun AlKuwari who is a well-known sports journalist and broadcaster and presenter for beIN Sports. When I asked him about the results, he said that “all the teams involved in the EPL scored less as it can be associated with the effect of the pandemic, which made most of the teams not in a position to practice and play regularly as they used before in the presence of their fans. Some teams, such as the Wolves. But Arsenal could have done better due to their resources advantage over other teams.”
Throughout witnessing the performance of teams in the league, I have noticed that some teams have younger and older players. I have also had a wild guess that if a team has more younger players they would inevitably have a better success rate within the Premier League. This wonderment leads me back to the data set. To see what numbers have to reveal to me. The data set goes into detail about carries and passes between the ages. Carries determine how the ball is being played on the field, whilst passes are just how much possession the player has of the ball and the ability to create chances and pass the ball successfully in the game.
Age and progressive carries data shows that the youngest teams in the Premier League are Arsenal and Leeds United, both of which have an average age of 25. On the other hand, Fulham is the oldest team in the league, with an average age of 29. The graph also shows Manchester City has the highest number of progressive carries with 688, closely followed by Tottenham with 509. On the other hand, Bournemouth has the lowest number of progressive carries, with 313. Overall, the column graph provides a great visual representation of the data set, allowing us to easily compare each team’s age and progressive carries in the Premier League. It is a useful tool for understanding how teams in the Premier League compare to each other in terms of age and progressive carries. The age of players also matters in data analysis of the football game since the players must be both strong and experienced to have good results and compete effectively. From the data, Arsenal has signed a player with a mean age of 25 years but has less progressive carries than Manchester City, whose mean age of their players is 27.6 years. Mr. Al Kuwari commented by stating, “I think there is no direct correlation between the age of the players and the progressive carries” while looking at the data and having the background of information that he does.
Although, on the other hand, the column graph portrays the average age of the Premier League teams and the number of progressive passes they achieved. It is clear from the graph that Arsenal is the youngest team in the league, with an average age of 25, and they had the highest number of progressive passes at 1474. Manchester City, who had the second-highest number of progressive passes in 1483, has an average age of 27.8, slightly higher than the league average. Aston Villa had the lowest progressive passes of 819 and an average age of 27.6, slightly higher than the league average. The data suggests that teams with younger players have more progressive passes than teams with older players. This could mean that younger players are more active and have more energy, which allows them to make more progressive passes. It could also suggest that older players focus more on defending and are less likely to make progressive passes. The data shows that having a younger team is beneficial for a team’s success as it can lead to more progressive passes and better performance in the Premier League. This was the other most shocking result that I found within the data set.
Although to fully understand the performance of passes throughout the season of each team, I decided to analyze the data set that highlights possession and progressive passes without the influence of age as a playing factor. Arsenal, with the highest possession percentage compared to other teams, held 60.2%, while Aston Villa held 48.5%. At the same time, Bournemouth maintained the lowest percentage of possession, with 38.5%. All the other teams maintained a possession percentage of 42-50%. Leeds United had total possession of 49.7%, Liverpool 59.8%, and Crystal Palace had 44.3%. A notable observation shows that the two Manchester-based teams had the highest and second-highest possession, with 64.2% and 52.8%, respectively. The progressive passes portrayed through the column graph also show that Manchester City had the highest pass number, 1483, with Liverpool in the second position, 1382. At the bottom end of the graph, Bournemouth had the least progressive passes with only 622. To summarize, a column graph efficiently communicates the data assets of the possession and progressive passes of the teams. The graph shows that Arsenal holds the highest percentage of possession in the dataset, but Manchester City tops the chart with the highest progressive passes. When I asked Mr. Al Kuwari for his opinion about the possession and progressive carries of the teams provided in the presented graph, he stated “The whole premise of the game is to have more possession and gain more momentum to create an opportunity for those passes, Arsenal did a great job with that. It is very important because, with possession, you gain control and tire the opposing team.”
So, can numbers really determine the fate of the teams?
Short answer, yes. Through dissecting the dataset, it was highlighted the importance of age, progressive carries, and progressive passes in determining a team’s success in the Premier League. Older teams with higher amounts of progressive carries and progressive passes tend to score more goals than expected, and these statistics appear to be the most important factors for a team’s success. Possession and the number of assists also appear to affect goals scored, although to a lesser extent. The whole visualization demonstrates the significance of age, progressive carries, and progressive passes in the Premier League and highlights the value of these statistics in understanding a team’s success.