Dahel Consultant and Techies Internship: This is a project that entails the analysis of the world football game (The picture below is gotten from Architectural Digest India Website).
Disclaimer: This is not a real company as we know this is a dataset compiled by Dahel Consultant Techies for Internship purposes.
This is a world football game analysis. It is done by analyzing data from the world football table which comprises of date, home_team, away_team, and winner fields. I used Excel to get an overview of the dataset before importing it into Tableau where the actual analysis was carried out.
The goal of this analysis is to know:
- The highest away team wins
- The teams with the highest win or success rate
- The trend of matches over time
- The top 10 home teams in total matches
- The stability in annual match counts
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Tableau concepts like:
- Data Connection:Connecting to various data sources such as Excel files, CSV files
- Data Preparation: Cleaning and transforming data using Tableau, Handling missing values, data type conversions, and other data cleaning tasks. Creating calculated fields to derive new insights or perform calculations on the data.
- Data Visualization: Building visualizations such as bar charts, bubble chart, scatter plots, heat maps, treemaps, histograms, and more to represent data. Using dual-axis charts, and stacked charts to compare multiple measures or dimensions effectively. Applying filters, parameters, and sets to interactively explore and drill down into data subsets.
- Dashboard Creation: Designing interactive dashboards by combining multiple visualizations, filters, and other elements onto a single canvas. Adding interactivity to dashboards using actions, filters, and highlight actions to facilitate exploration and analysis. Optimizing dashboard layout and design for clarity and usability.
- Advanced Analytics: Performing statistical analysis within Tableau using built-in functions and capabilities (e.g., trend lines, reference lines, forecasting). Conducting spatial analysis by visualizing geographic data and using spatial calculations. Utilizing clustering, regression analysis, and other advanced analytics techniques for deeper insights.
- Mapping: Creating maps to visualize geographic data using built-in geographic roles or custom latitude-longitude data. Customizing maps with various map layers, backgrounds, and map styles. Using spatial analysis tools to perform geographic analysis and create spatial visualizations.
- Calculations and Expressions: Writing calculated fields and calculated measures using Tableau's formula language to perform complex calculations. Using table calculations to compute values based on the data displayed in a visualization. Implementing level of detail (LOD) expressions to control the granularity of calculations.
- Data Exploration and Interactivity: Enabling interactivity in visualizations by allowing users to filter, highlight, and drill down into data dynamically. Utilizing tooltips, parameter actions, and dashboard actions to enhance user experience and provide context-sensitive information. Implementing quick table calculations, sorting, and ranking to explore data trends and patterns.
The dataset for the work is gotten from Dahel Consultant Techies. It consists of 458 records and 4 fields of data. I studied the dataset well to gain proper insight into the dataset. You can find a link to download the dataset here:
Step 1:
- The first row is represented as column headers (field names).
Step 2:
- Some certain data types were changed to values in the data source.
Step 3:
- Data derived from an Excel merged cell is changed to value in the data source.
Step 4:
- Data is ignored and not included as part of your data source.
Several expressions and functions were made to arrive at the desired KPI or Metrics. I arrived at a report with a single dashboard consisting of different visuals such as bar charts, and line chart, giving the summary of the insights gained into the world football performance.
The dashboard conveys information about the following key areas:
- highest away team wins
- teams with the highest win or success rate
- trend of matches over time
- top 10 home teams in total matches
- stability in annual match counts
Summary of the insights gained into the world football performance:
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Egypt excels with the highest away team wins at 8 points, while a closely competitive group including Argentina, Angola, Botswana, Cameroon, Colombia, Guinea, Ivory Coast, South Korea, and Uganda follows closely with 5 points each, showcasing a balanced performance among top teams on the road.
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Brazil dominates with a 100% win rate, while other top teams like Angola, Botswana, Colombia, and Guinea show consistent success at 83.33%. Croatia and Ethiopia perform well with a 75% win rate. The dataset suggests a competitive environment with notable team performances.
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The trend of matches over time shows a consistent level of 40 matches in 2002 and 2004, with a slight decrease in 2000 to 39, and earlier variations in 1995 (29), 1996 (28), and 1984 (28), indicating stability with occasional fluctuations in annual match counts.
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The top 10 home teams in total matches reveal Zambia as the leader with 11 matches, followed by Senegal, South Africa, and Iran with 9 matches each. South Korea, Uruguay, and Egypt also achieved 9 matches, showcasing competitive home performance across these teams.
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The top 10 home teams in total matches reveal Zambia as the leader with 11 matches, followed by Senegal, South Africa, and Iran with 9 matches each. South Korea, Uruguay, and Egypt also achieved 9 matches, showcasing competitive home performance across these teams.
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Teams should focus on strengthening their away game strategy, especially considering Egypt's impressive 8-point away wins and the competitive group with 5 points each.
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Brazil's 100% win rate highlights dominance, while the competitive 83.33% success among Angola, Botswana, Colombia, Guinea, and the 75% rate for Croatia and Ethiopia suggest a balanced and competitive football environment.
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Recognize the stability in annual match counts, with a consistent level of 40 matches in 2002 and 2004. Teams should plan accordingly for the occasional fluctuations observed in earlier years (2000, 1995, 1996, and 1984).
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Teams should leverage their home ground advantage for maximum performance. Zambia leads with 11 home matches, while Senegal, South Africa, Iran, South Korea, Uruguay, and Egypt also display competitive home records with 9 matches each.
I am open for entry-level to mid-level data analyst role.