By Ankit Srivastava | Data Analytics & Power BI Trainer
Introduction
Cricket generates an enormous amount of statistical data. Runs, boundaries, strike rate, batting average, matches, innings and hundreds can all be converted into meaningful visual insights when they are presented correctly.
In this Power BI project, I created an interactive Top 20 Six Hitting Batters in IPL – Analysis dashboard using a structured Excel dataset. The objective is simple: take batting statistics and turn them into an interactive report that allows users to compare players, teams and important performance metrics.
One important aspect of this project is that I have not used DAX. The dashboard is created using Power BI’s built-in aggregation capabilities, standard visuals, filters and formatting options. This makes the project particularly useful for beginners who are learning Power BI and want to understand how much can be achieved before moving into DAX.
The accompanying video tutorial demonstrates the dashboard-building process.
The final dashboard contains KPI cards, a player filter, a team filter, a combination chart comparing sixes and strike rate, and another visual comparing runs and hundreds.
This project is therefore not only about IPL statistics. It is also a practical exercise in data loading, visual selection, aggregation, dashboard design and analytical storytelling.
About the Dataset
Download the dataset here: https://colorstech.net/wp-content/uploads/2026/09/PowerBI_IPL_Batting_Dataset_Clean.xlsx
The Excel dataset used for this project contains 20 IPL batters and 13 columns describing their batting performance.
The main fields are:
| Column | Description |
|---|---|
| Player_Rank | Rank assigned to the player |
| Player | Name of the IPL batter |
| Team | Team abbreviation associated with the player |
| Sixes | Number of sixes hit |
| Matches | Matches played |
| Innings | Innings played |
| Highest_Score | Highest individual score |
| Batting_Average | Batting average |
| Strike_Rate | Strike rate |
| Hundreds | Number of centuries |
| Fifties | Number of half-centuries |
| Fours | Number of fours |
| Runs | Total runs |
Because the dataset is already structured in Excel, it is suitable for a beginner-level Power BI project.
The important point is that each row represents a player, while the columns contain different performance metrics. This allows Power BI to aggregate the numerical columns automatically when they are placed into visuals.
For example, Power BI can calculate:
- Average Batting Average
- Average Strike Rate
- Sum of Fifties
- Sum of Hundreds
- Sum of Fours
- Sum of Innings
- Sum of Matches
- Maximum Highest Score
No custom DAX measure is required for these basic calculations.
Step-by-Step Power BI Tutorial
Step 1: Open Power BI Desktop
Start by opening Power BI Desktop.
From the Home tab, select:
Get Data → Excel Workbook
Locate the IPL batting Excel file and open it.
Power BI will display the available worksheet. Select the batting data table and click Load.
Once the data has loaded, you will see the fields in the Data/Fields pane.
Before starting the dashboard design, quickly verify that:
- Player is recognized as text
- Team is recognized as text
- Sixes is numeric
- Matches is numeric
- Innings is numeric
- Highest Score is numeric
- Batting Average is numeric
- Strike Rate is numeric
- Hundreds is numeric
- Fifties is numeric
- Fours is numeric
- Runs is numeric
This step is important because incorrect data types can affect Power BI’s aggregations.
Step 2: Set Up the Dashboard Canvas
Create a clean report page and use a dark background for the dashboard.
The dashboard has been designed with a simple structure:
- Title section
- Player and Team filters
- KPI cards
- Sixes and Strike Rate chart
- Runs and Hundreds chart
At the top, add a text box with the title:
Top 20 Six Hitting Batters in IPL – Analysis
Increase the font size and make the title prominent.
The purpose of a dashboard title is not just decoration. It immediately tells the viewer what the report is analysing.
Step 3: Create the Player Filter
The first interactive component is the Player slicer.
Select the Slicer visual from the Visualizations pane.
Drag:
Player → Field
This creates a list of players.
The user can now select an individual batter and instantly filter the dashboard.
For example, selecting a player will update the KPI cards and charts according to the selected player.
You can format the slicer by adjusting:
- Background
- Font
- Border
- Search
- Selection controls
This makes the dashboard easier to navigate.
Step 4: Create the Team Filter
Create another slicer.
Drag:
Team → Field
The slicer will display the available IPL team abbreviations.
This gives the dashboard another level of interactivity.
For example, a user can select a particular team and investigate the corresponding batting statistics.
The combination of Player + Team filters makes the dashboard much more useful than a static chart.
Step 5: Create the KPI Cards
The next step is to create the summary cards visible at the top of the dashboard.
The first card shows:
Average of Batting_Average
Add a Card visual and drag:
Batting_Average → Fields
Power BI will automatically aggregate the field.
Change the summarization to:
Average
The dashboard displays approximately:
34.58
Step 6: Total Fifties
Create another Card visual.
Drag:
Fifties → Fields
Set the aggregation to:
Sum
This produces:
650
The card therefore communicates the combined number of half-centuries represented by the selected dataset.
Step 7: Total Hundreds
Create another Card.
Drag:
Hundreds → Fields
Use:
Sum
The resulting dashboard displays:
53
This gives viewers an immediate indication of the number of centuries in the dataset.
Step 8: Total Fours
Create another Card.
Drag:
Fours → Fields
Set the aggregation to:
Sum
The dashboard displays:
8,675
This becomes one of the most visually interesting metrics because it provides a quick indication of boundary-hitting activity.
Step 9: Maximum Highest Score
Create another Card.
Drag:
Highest_Score → Fields
Change the aggregation to:
Maximum
The dashboard then displays the highest individual score in the dataset:
175
This is a good example of how Power BI’s built-in aggregation can answer an analytical question without writing DAX.
Step 10: Total Innings and Matches
Create two additional cards.
For the first:
Innings → Sum
For the second:
Matches → Sum
The dashboard displays:
- 3,464 total innings
- 3,540 total matches
These metrics provide additional context around the players included in the dataset.
Step 11: Average Strike Rate
Create another Card visual.
Drag:
Strike_Rate → Fields
Change the aggregation to:
Average
The dashboard displays approximately:
143.08
This is an important batting-performance metric because strike rate indicates how quickly runs are scored.
Step 12: Create the Sixes and Strike Rate Chart
Now we move from KPI cards to analytical visualizations.
The first major chart is:
Top 10 Six Hitters Sixes & Strike Rate
For this visual, use a Line and Clustered Column Chart.
Place:
Player → X-axis
Then add:
Sixes → Column Y-axis
And:
Strike_Rate → Line Y-axis
Power BI will create columns representing sixes and a line representing strike rate.
The next step is to limit the visual to the Top 10 players.
Use the Filters pane and apply a Top N filter based on Sixes.
This produces a clear comparison between players who hit the most sixes and their strike rates.
The chart makes it possible to ask questions such as:
- Who has hit the most sixes?
- Which top six hitters have the highest strike rate?
- Are the biggest six hitters also aggressive scorers?
- Which players combine volume and scoring speed?
This is where a dashboard becomes more useful than a spreadsheet.
Step 13: Create the Runs and Hundreds Chart
The second major visualization is:
Top 10 Batters by Runs Scored & 100s
Again, use a Line and Clustered Column Chart.
Add:
Player → X-axis
Then:
Runs → Column Y-axis
And:
Hundreds → Line Y-axis
Apply a Top 10 filter based on Runs.
Now the columns show total runs while the line represents the number of centuries.
This provides a simple comparison between overall run production and century-making ability.
For example, a player may have a very high run total without having the highest number of centuries. Another player may have fewer total runs but a strong century count.
That difference is exactly the kind of comparison that data visualization can highlight.
Step 14: Format the Visuals
Once all visuals are created, formatting becomes important.
Use a consistent dark dashboard theme.
For the KPI cards, make the numbers large and easy to read.
For charts:
- Use meaningful titles
- Increase axis readability
- Enable data labels where useful
- Adjust chart spacing
- Reduce unnecessary visual clutter
- Keep fonts consistent
- Use readable player names
- Align visuals properly
The objective is not to add as many design elements as possible.
The objective is to make the information easier to understand.
Step 15: Test the Interactivity
Now test the dashboard.
Select different players from the Player slicer.
Then select different teams from the Team slicer.
Observe how the cards and charts respond.
This is one of the most important concepts beginners should understand about Power BI.
A Power BI report is not simply a collection of charts. The visuals can interact with each other and allow the user to explore the data dynamically.
Why This Project Does Not Need DAX
One of the key learning points from this project is that DAX is not always required to create a useful Power BI dashboard.
For this particular dataset, Power BI’s standard aggregations are sufficient.
We can use:
- Sum
- Average
- Maximum
- Top N filtering
- Slicers
- Standard visual interactions
without creating custom DAX measures.
This makes the project particularly suitable for beginners.
Once you understand these fundamentals, you can move toward DAX and create more advanced calculations such as year-over-year growth, rankings, percentage contribution, moving averages and dynamic measures.
Future Scope of the IPL Dashboard
This dashboard can be developed much further.
1. Add IPL Seasons
Adding a season/year column would allow analysis of batting performance over time.
You could investigate how a player’s performance changed from one IPL season to another.
2. Add Match-Level Data
Instead of only having aggregated player statistics, match-level or ball-by-ball data could be introduced.
This would enable analysis of individual matches, innings and deliveries.
3. Add Venue Analysis
A venue column could help answer questions such as:
- Which stadium produces the most sixes?
- Which players perform best at particular venues?
- Where are batting averages highest?
4. Add Team-Level Analysis
The dashboard could be expanded to compare teams based on:
- Runs
- Sixes
- Fours
- Strike rate
- Batting average
- Hundreds
- Fifties
5. Introduce DAX
After mastering the current dashboard, DAX can be introduced for advanced calculations.
For example, you could create dynamic player rankings, percentage contribution, average runs per innings and other calculated metrics.
6. Add More Dashboard Pages
The project could become a complete IPL analytics solution with separate pages for:
- Player Analysis
- Team Analysis
- Venue Analysis
- Season Analysis
- Batting Analysis
- Bowling Analysis
This would transform a beginner dashboard into a much larger sports analytics portfolio project.
Conclusion
This IPL Six-Hitting Batters dashboard demonstrates an important lesson for anyone beginning their Power BI journey: you do not have to start with complicated DAX to create a meaningful analytical dashboard.
With a clean Excel dataset, Power BI’s standard aggregations, appropriate visuals, slicers and good dashboard design, we can already build an interactive report capable of answering several useful questions.
In this project, we created KPI cards for batting average, fifties, hundreds, fours, highest score, innings, matches and strike rate. We also created two combination charts to compare sixes with strike rate and runs with hundreds.
Most importantly, the entire exercise focuses on understanding the fundamentals of Power BI: importing data, selecting fields, using aggregations, creating visuals, applying filters and building an interactive analytical story.
Once these fundamentals become comfortable, learners can progress to Power Query, data modelling, relationships and DAX.
That is the natural path from creating your first Power BI dashboard to developing professional-level data analytics projects.
Watch the Video Tutorial
I have also created a video tutorial showing the process of building this IPL dashboard step by step in Power BI. Watch the IPL Power BI Dashboard Tutorial on YouTube
Join the Data Analytics Course
If you want to learn Power BI, Excel, Data Analytics and other practical data skills through hands-on projects, you can explore the courses offered by Colorstech Institute of Data Analytics.
Join the course at Colorstech Institute of Data Analysis:
Join the Data Analytics Course
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