| Student's name | SCIPER |
|---|---|
| Ali Ridha Mrad | 314529 |
| Mohammed Charfi | 311171 |
| Mehdi Bouchoucha | 314843 |
Milestone 1 • Milestone 2 • Milestone 3
10% of the final grade
This is a preliminary milestone to let you set up goals for your final project and assess the feasibility of your ideas. Please, fill the following sections about your project.
(max. 2000 characters per section)
Find a dataset (or multiple) that you will explore. Assess the quality of the data it contains and how much preprocessing / data-cleaning it will require before tackling visualization. We recommend using a standard dataset as this course is not about scraping nor data processing.
Hint: some good pointers for finding quality publicly available datasets (Google dataset search, Kaggle, OpenSwissData, SNAP and FiveThirtyEight), you could use also the DataSets proposed by the ENAC (see the Announcements section on Zulip).
In this project, we will combine two datasets: the first one is 2022-2023 Football Team Stats and the second one is 2022-2023 Football Player Stats. These two datasets contain stats for players and teams in the 2022-2023 season.
The selected datasets appear to be rich in content, providing comprehensive statistics on player and team performances throughout the 2022-2023 season. Initial examination suggests that the data is well-structured, with variables covering various aspects such as goals, assists, passes, shots, tackles, and more. It will still require some preprocessing before visualization. This may involve:
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Standardization: While raw stats provide valuable insights, they often lack consistency due to variations in recording methods across matches or teams. By standardizing data, such as converting all measurements to a common unit or adjusting for playing time, we can ensure a level playing field for comparison. This process facilitates easier analysis and interpretation of player performances across different matches and teams.
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Feature Engineering: Raw statistics offer a snapshot of player performance but may not capture the full spectrum of skills and contributions. Through feature engineering, we can derive new metrics or transform existing ones to better represent player capabilities. For instance, creating composite indices that weigh different statistical categories based on their importance in determining overall player effectiveness can provide a more comprehensive view of performance. Additionally, incorporating contextual factors such as match importance, opponent strength, or positional roles can enrich our understanding of player contributions beyond the numbers.
Frame the general topic of your visualization and the main axis that you want to develop.
- What am I trying to show with my visualization?
- Think of an overview for the project, your motivation, and the target audience.
In the dynamic world of football, clubs like Brentford FC and Brighton & Hove Albion have emerged as pioneers in data-driven player recruitment, transitioning from traditional scouting methods to a more analytical and statistical approach. This significant shift away from relying on scouts' subjective evaluations and intuition marks the start of a new era in football management. The strategic application of statistical analysis in recruitment has demonstrated its potential to revolutionize teams, especially for clubs with limited resources, and has played a crucial role in optimizing team performance and gameplay strategies.
Advanced statistics and performance metrics have provided deep insights, fundamentally enhancing our understanding of the game. This underscores the critical role of data analytics in shaping the future of the sport.
Our project aims to discover the strategies that lead to success for players and teams in modern football. We are committed to present players and teams performances through diverse visualizations, with the aim of popularizing these practices. By demonstrating to football fans the power of analysis, statistics, and visualizations, we aim to tackle various challenges such as the following:
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How can we leverage player and team statistics to objectively determine the best team of the season, contrasting it with the subjective choices made by journalists?
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How can we devise an effective method for attributing scores to player statistics and characteristics, thereby creating a standardized rating system? Furthermore, how can we implement this rating system into an interactive tool, resembling FIFA cards, to allow users to compare player statistics and performance across different teams and leagues?
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What are the defining characteristics of top-performing teams across different leagues, positions, and nationalities? Can we create an interactive dashboard that allows users to filter visualizations based on these parameters, providing a focused analysis of specific datasets for both players and teams?
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What are the distinct characteristics exhibited by players based on their positions, ages, and nationalities? How do these factors influence a player's performance, playing style, and overall contribution to the team?
By exploring these questions, we want to offer insights into the sophisticated world of football analytics, making it accessible and understandable to fans worldwide. Our aim is to show that with the right analysis and interpretation of data, complex football questions and problems can be approached in a visual manner.
Pre-processing of the data set you chose
- Show some basic statistics and get insights about the data
Dataset Overview
The datasets provide a comprehensive array of statistics pertaining to both teams and players, offering valuable insights into their performances. Below is a concise summary of the contents of each dataset.
Player Statistics (player_stats.csv) This dataset furnishes intricate performance metrics for individual players:
- Basic Information: Details such as player name, nationality, position, squad affiliation, league, age, and birth year.
- Playing Time: Information on matches played, starts, and total minutes played.
- Offensive Statistics: Metrics including goals scored, total shots taken, shots on target, and pass completion rates.
- Defensive Metrics: Data on tackles made, interceptions, and clearances.
- Passing: Comprehensive statistics covering total passes attempted and completed, passing distances, and types of passes executed.
- Advanced Metrics: Insights into shot-creating actions, goal-creating actions, dribbles completed, and carries made.
Team Statistics (team_stats.csv) This dataset provides an overview of team performances:
- Ranking and Results: Team ranking, along with statistics on wins, draws, losses, goals scored, goals conceded, and total points accumulated.
- Expected Goals (xG): Key metrics such as expected goals (xG), expected goals against (xGA), and expected goal difference (xGD).
- Attendance: Average attendance per game, particularly for home matches.
- Key Players: Identification of top team scorers and the most frequently utilized goalkeeper.
In our data visualization project, we delve into key aspects of team performance and fan engagement in the realm of sports. Through a series of visualizations, we offer insights into various facets of team dynamics and fan behavior, allowing viewers to gain a deeper understanding of the competitive landscape and the fervor surrounding sporting events.
The first visualization presents a comparative overview of goals scored and conceded by each team, shedding light on their offensive prowess and defensive resilience throughout the season.
The second visualization highlights the top-performing teams based on league points, providing valuable insights into the most successful teams throughout the season and their standings within the league.
Lastly, our third visualization showcases the teams with the highest attendance rates, offering a glimpse into the clubs that attract the largest crowds and underlining their popularity and support among fans.
Together, these visualizations offer a comprehensive perspective on team performance and fan engagement, enriching our understanding of the dynamic world of sports. You can explore how we plotted this data in our project's repository.
- What others have already done with the data?
- Why is your approach original?
- What source of inspiration do you take? Visualizations that you found on other websites or magazines (might be unrelated to your data).
- In case you are using a dataset that you have already explored in another context (ML or ADA course, semester project...), you are required to share the report of that work to outline the differences with the submission for this class.
Many projects have explored football player and team statistics, not only to understand performance but also for prediction purposes. Football clubs increasingly rely on data analytics to optimize player recruitment, improve team performance, and refine gameplay strategies. However, accessing comprehensive visualizations and data can be challenging. While official match presentations offer insights, they often lack the interactivity and depth desired by users.
Our project seeks to fill this gap by using player and team statistics from the 2022-2023 football season into an interactive visualization tool. Unlike others that may focus solely on player or team data, we aim to integrate both datasets, providing users with an immersive dashboard to explore various aspects of performance. Additionally, we aim to overcome specific challenges in football analytics, such as developing a standardized rating system for players and identifying key characteristics of top-performing teams across different leagues, positions, and nationalities.
Our inspiration comes from diverse sources, including existing data visualization projects and sports analytics websites. We aspire to create a visually captivating and informative tool that draws on best practices in data visualization and sports analytics, while also considering the engaging and meaningful visualizations seen in football TV shows, official match presentations, and football games.
The Football Dashboard is a comprehensive web application that provides insightful visualizations and comparisons of football players and teams. This interactive platform allows users to explore various statistics, view top players and teams, and understand the game through detailed analytics.
- Home Page: An introduction to the website and the importance of football statistics.
- Compare Players: Compare the performance of different players.
- Compare Teams: Compare the performance of different teams.
- Best Team: View the team of the season per league.
- Best Players: View statistics and information about the best players.
- Contact: Contact info of developers.
- Best Player per League and Category: Horizontal bar chart showcasing top players in different leagues for each skill category (pace, shooting, passing, dribbling, defense, physicality, and overall performance).
- Number of Players per Country: Interactive world map displaying the number of players from each country.
- Percentage of Nationalities per Team: Pie chart showing the distribution of nationalities within teams.
- Players in Real-Life Numbers: Visualization showing the number of goals, market value, and weekly wage of top players.
- Club's Biggest Goalscorers: Interactive pie chart displaying top goalscorers for each club.
- Shots per Goal Metric: Chart showing the goals and shots taken by each player to evaluate their effectiveness.
- Best Teams: Interactive visualization of the best team per league.
index.html: Home page of the dashboard.searchPlayer.html: Page for comparing players.searchTeam.html: Page for comparing teams.bestTeam.html: Page for viewing the best teams.bestPlayer.html: Page for viewing the best players.contact.html: Contact page.playerBarChart.html: Horizontal bar chart for top players.playersWorld_map.html: World map for the number of players per country.countryChart.html: Pie chart for the percentage of nationalities per team.goalscorerBarChart.html: Visualization for goals, market value, and weekly wage of top players.pieChart.html: Pie chart for club's biggest goalscorers.shotsXgoals.html: Chart for shots per goal metric.bestTeamVizRaw.html: Visualization for the best team per league.css/style.css: Stylesheet for the dashboard.data/finalPlayerDf.json: JSON data file containing player statistics.data/blank_card_special.png: Image file for blank card background.data/players_images/: Directory containing player images.data/flags/: Directory containing flag images.
- Clone the repository.
- Open
index.htmlin a web browser to access the home page. - Navigate through the sidebar to access different features and visualizations.
- Interact with the charts and graphs to explore football statistics.
- Font Awesome: For icons used in the navigation bar.
- D3.js: For creating interactive visualizations.
- External Stylesheets and Scripts: Required for charts and graphs.
- Add more detailed player and team statistics.
- Implement user authentication for personalized dashboards.
- Enhance the interactivity of the visualizations.
- Add more visualizations and statistical analyses.
For any queries or feedback, please reach out to us through the contact information on the Contact page.
- < 24h: 80% of the grade for the milestone
- < 48h: 70% of the grade for the milestone