Our Objective is to transform raw data into actionable insights, employing Excel's robust capabilities for data analysis and visualization. From cleaning and organizing the dataset to crafting compelling visual narratives, this project aims to showcase the power of data in understanding the nuances of IPL cricket.
Decision-Making Insights: Provide decision-makers in the cricket industry with actionable insights for strategic planning.
Fan Engagement: Engage cricket fans by offering unique insights and visualizations about their favorite teams and players.
Educational Value: Serve as an educational resource for enthusiasts interested in data analysis and visualization using Excel.
Enhanced Understanding: Help cricket enthusiasts, analysts, and team management gain a deeper understanding of the game dynamics.
Decision Support for Teams: Offer decision support for IPL teams by showcasing successful strategies and historical performance trends.
Analysing IPL data begins with importing and cleaning in Excel, utilizing features like PivotTables and charts for player stats and team performance.
Raw data collection in the context of the Indian Premier League (IPL) is pivotal for comprehensive cricket analysis and strategic decision-making.
- Data Cleaning:
-> Extracted Year: Utilized the =YEAR function to extract the year from the date column.
-> Season Naming: Named the seasons using the format "IPL - YYYY" for better clarity.
-> Table Conversion: Employed Ctrl + T to convert the dataset into a table for easier manipulation and analysis.
- Matches Win Analysis:
-> Pivot Table Creation: Developed a Pivot Table on a new sheet to analyze matches won by teams, focusing on winner and toss winner, toss decision using a stacked column chart.
-> Visualization: Named the Pivot Table as "Matches Win" for easy reference.
Created a stacked column chart to visually represent the relationship between the winner, toss winner, and toss decision.
- Donut Chart Analysis:
-> Total Bat and Field Practice:
Created a Donut Chart to showcase the distribution of wins based on batting and fielding decisions. Provided data points such as 283 wins when batting first and 413 wins when fielding first.
- Top 10 Venue Analysis:
-> Top 10 Venues:
Applied a value filter in the Pivot Table to isolate the top 10 venues based on toss decision and winner.
Presented the findings using a stacked bar chart for clear visualization.
- Man of the Match Analysis:
-> Player of the Match: Utilized a clustered column chart to represent the count of players winning the "Man of the Match" title. Highlighted players who won the title multiple times for additional insights.
- Title Winner Tree Chart:
-> Winner Data Utilization: Extracted data on winners and winner counts.
Developed a Tree Chart to visualize the hierarchy of winners and their respective counts.
NOTE:THIS IS COLLAB PROJECT UNDER THE GUIDANCE OF OUR PROFESSOR STUDENTS INVOLVED IN PROJECT : DANISH MADAN, ISHANI MAHAJAN, GAURAV
