End -To- End Cricket Data Analytics Project Using Python, Pandas and Power BI

Problem Statement:

Welcome to an exciting project where planet Earth faces an imminent threat from invading aliens! These extraterrestrial beings are determined to conquer Earth, but there’s a glimmer of hope: They’ve challenged us to a high-stake T20 cricket match. The fate of humanity rests on the shoulders of the “BEST 11” cricket team we assemble to take on the Alien 11.

Requirement Scoping:

To ensure a fair and thrilling contest, we’ve established specific parameters to select the BEST 11 players for our team. To achieve this, we have followed the below parameters.

Data Cleaning and Transformation in Python Pandas:

The first step in our project is to clean and transform the raw data using Python and Pandas. Data cleaning involves handling missing values, correcting data types, and ensuring the data is consistent and ready for analysis.

Building Parameters Using DAX Function:

Next, we use DAX (Data Analysis Expressions) functions to create calculated columns and measures to help us build a compelling dashboard in Power BI. DAX functions allow us to create new metrics and perform complex calculations on our data.

Building Dashboard in PowerBI:

Using Power BI, we build an interactive dashboard that visualizes our data and helps us identify the top players for our team. The dashboard includes various charts, graphs, and tables that display key performance metrics and comparisons.

Example Dashboard Features:

• Player Performance Comparison: Compare players based on runs scored, wickets taken, and other performance metrics.
• Batting and Bowling Averages: Visualize batting and bowling averages for individual players.
• Top Players: Highlight the top-performing players in different categories.

Here are some of the snippets from the final dashboard of our end-to-end cricket data analytics project:

• Player Selection Criteria: Visual representation of the criteria used for selecting the best players.
• Performance Metrics: Detailed performance metrics for each player.

Assembling the “Final 11”

Based on the comprehensive analysis and insights derived from our data, we have assembled the “Final 11” players who will take on the “Alien 11.” These players have been selected based on their performance metrics, consistency, and overall contribution to the game.

Conclusion

This project demonstrates the power of data analytics in making informed decisions, even in the context of a thrilling and unconventional challenge. By leveraging Python, Pandas, and Power BI, we have created a robust data analysis pipeline that helps us select the best cricket team to defend Earth against alien invaders.

I hope you find this article knowledgeable and gain valuable insights from it. If you enjoyed this project, please follow for more content related to data analysis and visualization.

Thank you for reading!