An interactive Microsoft Excel dashboard built to analyze Counter-Strike 2 (CS2) player and team performance using esports tournament data.
The dashboard provides insights into player performance, team performance, map-wise win/loss trends, player role performance, fatigue levels and top-performing players using PivotTables, PivotCharts, Slicers and KPI cards.
| Resource | Link |
|---|---|
| 📊 Excel Dashboard | Download Excel Dashboard |
| 📁 Dataset | Download Dataset |
| 🖼️ Dashboard Preview | View Dashboard Image |
- 📊 About the Project
- 🎯 Analytical Problem
- 💡 Solution
- 📈 Key Performance Indicators
- 📊 Dashboard Analysis
- 🎛️ Interactive Filters
- 💡 Key Insights
- 🧹 Data Preparation
- 🛠️ Tools & Techniques
- 📁 Project Files
- 📚 Data Source
- 🚀 What This Project Demonstrates
- 🔮 Future Scope
- 👤 Author
The dataset contains player-performance information across teams, maps, player roles, win/loss results, K/D ratios, performance scores, fatigue levels and MVP-related metrics.
The main challenge was to convert this detailed esports performance data into a clear analytical dashboard that could answer questions such as:
- Which teams show stronger overall performance?
- How do win and loss rates vary across different maps?
- Which player roles have higher average performance?
- How does performance vary across different fatigue levels?
- Who are the top-performing players based on performance score?
- How can the data be explored dynamically using different filters?
The dataset was structured and analyzed using Microsoft Excel.
An interactive dashboard was created using:
- PivotTables
- PivotCharts
- Slicers
- KPI Cards
- Excel formulas
- Data formatting
- Conditional formatting
- Interactive filtering
- Dashboard-focused data visualization
The dashboard allows users to explore the data using:
- Team
- Player Role
- Map Played
The interactive filters make it possible to analyze different sections of the dataset without manually modifying the underlying data.
| KPI | Value |
|---|---|
| Total Records | 6,000 |
| Average K/D Ratio | 2.71 |
| Win Rate | 51.9% |
| Average Performance Score | 39.5 |
| MVP Awards | 469 |
Note: The dataset contains 6,000 player-performance records/observations. These records should not be interpreted as 6,000 individual matches.
A comparison of average performance scores across different esports teams.
This visualization helps identify differences in overall team-level performance within the dataset.
The dashboard compares win and loss rates across different CS2 maps.
This analysis helps understand how performance outcomes vary depending on the map being played.
The dashboard analyzes average performance across different player roles, including:
- AWPer
- Entry Fragger
- IGL
- Lurker
- Support
This allows performance patterns to be compared across different responsibilities within a team.
This analysis compares player performance scores across different fatigue levels.
It helps explore how performance varies with fatigue level and identify potential patterns within the dataset.
Note: This analysis shows an observed relationship in the dataset and does not establish that fatigue directly causes changes in performance.
The dashboard highlights the top 5 players based on Performance Score.
The table includes:
- Rank
- Player Name
- Team
- Player Role
- Performance Score
- K/D Ratio
K/D Ratio is included as an additional performance metric for comparison.
The dashboard includes interactive slicers that allow users to filter and explore the dataset dynamically.
- Team
- Player Role
- Map Played
These slicers allow users to focus the dashboard on specific teams, roles or maps.
- The dataset contains 6,000 player-performance records for analysis.
- The overall average K/D Ratio is 2.71.
- The overall Win Rate is 51.9%.
- The average Performance Score is 39.5.
- The dataset contains 469 MVP Awards.
- Team performance varies across the analyzed teams.
- Win and loss rates vary across different maps.
- Player performance differs across different roles.
- Performance scores show variation across different fatigue levels.
- The Top 5 table highlights players with the highest Performance Scores in the dataset.
The dataset was prepared and structured for analysis in Microsoft Excel.
Key preparation activities included:
- Reviewing the dataset structure
- Checking data consistency
- Preparing fields for PivotTable analysis
- Organizing player, team, role and map information
- Preparing performance-related fields for analysis
- Formatting numerical and percentage values
- Preparing the dataset for dashboard filtering and visualization
The final dataset contains 6,000 player-performance records used throughout the dashboard.
- Microsoft Excel
- Git
- GitHub
- PivotTables
- PivotCharts
- Slicers
- KPI Cards
- Excel formulas
- Data cleaning and formatting
- Conditional formatting
- Dashboard layout and visualization
- Interactive reporting
| File | Description |
|---|---|
CS2_Esports_Master_Dataset.csv |
CS2 player-performance dataset |
esports_player_performance_tournament_analytics.xlsx |
Interactive Excel dashboard |
Dashboard_Screenshot.png |
Dashboard preview image |
README.md |
Project documentation |
This project uses an esports player-performance dataset structured for Counter-Strike 2 (CS2) performance analysis.
The dataset contains player-level performance observations covering different:
- Players
- Teams
- Maps
- Player Roles
- K/D Ratios
- Win/Loss Results
- Performance Scores
- Fatigue Levels
- MVP-related metrics
The dataset was prepared and structured for the purpose of building this Excel-based analytics project.
This project demonstrates practical experience in:
- Analyzing structured esports performance data
- Preparing datasets for analytical reporting
- Building KPI-driven dashboards
- Using PivotTables and PivotCharts for analysis
- Creating interactive dashboards with Slicers
- Comparing team and player performance
- Performing map-wise and role-wise analysis
- Exploring performance across fatigue levels
- Building dynamic Top 5 player analysis
- Designing dashboards with a focus on readability and usability
- Presenting analytical findings through data visualization
Potential extensions for this project include:
- Rebuilding the dashboard using Power BI
- Connecting the analysis with SQL
- Adding more detailed match-level analysis
- Adding player-level performance trends over time
- Adding advanced player segmentation
- Creating automated data refresh workflows
- Developing predictive performance analysis
Devansh Gupta
B.Tech Computer Science | Aspiring Data Analyst
Focused on Excel, SQL, Python, Power BI and Data Visualization.
- GitHub: DevanshGupta03
- LinkedIn: Devansh Gupta
⭐ If you find this project interesting, feel free to explore the dashboard and dataset.