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Utilizing Web Scraping to extract data from online sources, we employ Python's libraries like NumPy and Pandas to clean and merge datasets. Leveraging DAX queries, we create advanced measures for PowerBI dashboards. Our reports, following a meticulous color coding scheme, feature creative charts and plots, maintaining the specified format of mockup

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Aashay30/Cricket_Analysis

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  • Title :- Cricket Analysis
  • Created by :- Aashay Tamrakar
  • Tool used:- MicrosoftSQLServer Microsoft Excel Power Bi Python

📜PDF Link of PowerBI Dashboard

Objective 🎯

The goal of this project is to analysis the players data, extract necessary information about players based on a combination of their strengths for an unbeatable team and make a dashboard to review the performance of the team.

Problem statement 📜

  • Do ETL : Extract-Transform-Load on dataset
  • Perform EDA through Python and Power BI
  • Extract various information using DAX queries such as Average Runs, Strike Rate, Total Runs, Wickets Taken, Economy etc.
  • Extract necessary information about All Rounders, Batters, Bowlers.
  • Make necessary dashboard
  • Find key metrics (KPIs) and factors and show the meaningful relationships between attributes

Dataset 📀

Cricket_Dataset

Technology 🤖

Business Intelligence

Domain 🛒

Cricket Analysis

Tools 🛠

MS-Excel, MS-Power BI, Python

Architecture ⚙

App Screenshot

Analysis :-

KPI's Requirement

We need to analyze key indicators for our cricket data to gain insights into our overall game performance. Specially, we want to calculate the following metrics:

  1. Total Runs - Total number of runs scored by the batsman.

  2. Total Innings Batted - Total number of innings a batsman got a chance to bat.

  3. Total Innings Dismissed - To find the number of innings batsman got out.

  4. Batting Average - Average runs scored in an innings.

  5. Total balls Faced - Total number of balls faced by the batsman.

  6. Strike Rate - No of runs scored per 100 balls.

  7. Batting Position - Batting position of a player.

  8. Boundary % - Percentage of boundaries scored by the Batsman.

  9. Avg. balls Faced - Average balls faced by the batter in an innings.

  10. Wickets - Total number of wickets taken by a bowler.

  11. Balls Bowled - Total number of balls bowled by the bowler.

  12. Runs Conceded - Total runs conceded by the bowler.

  13. Bowling Economy - Average number of runs conceded in an over.

  14. Bowling Strike Rate - Number of balls bowled per wicket.

  15. Bowling Average - No. of runs allowed per wicket.

  16. Total Innings Bowled - Total number of innings bowled by a bowler.

  17. Dot Ball % - Percentage of dot balls bowled by a bowler.

  18. Player Selection - To understand if a player is selected or not.

  19. Display Text - To display a text of no player is selected.

  20. Color Callout Value - To display a value only when a player is selected.

  21. Boundary Runs - To find the total number of runs scored by hitting fours and sixes.

  22. Boundary Runs Bowling - To find the total number of runs conceded by bowlers in boundaries.

Charts Requirement

We would like to visualize various aspects of our cricket data to gain insights and understand the perfrormance of players and them combined as team. We have identified the following requirements for creating charts:

  1. Power Hitters - Created a matrix for their name, team, batting style, innings batted, runs, balls faced, batting strike rate, batting average, batting position, boundary %. An area chart showing their performances like batting average, average balls faced, strike rate, boundary % in various matches. Also a scatter plot for tracking the top 5 players with taking strike rate and batting average into account.

  2. Anchors / Middle Order Batters - Created a matrix for their name, team, batting style, innings batted, runs, balls faced, batting strike rate, batting average, batting position, boundary %. An area chart showing their performances like batting average, average balls faced, strike rate, boundary % in various matches. Also a scatter plot for tracking the top 5 players with taking strike rate and batting average into account.

  3. Finishers - Created a matrix for their name, team, batting style, innings batted, runs, balls faced, batting strike rate, batting average, batting position, boundary %. An area chart showing their performances like batting average, average balls faced, strike rate, boundary % in various matches. Also a scatter plot for tracking the top 5 players with taking strike rate and batting average into account.

  4. All Rounders - Created a matrix for their name, team, batting style, innings batted, runs, balls faced, batting strike rate, batting average, batting position, boundary %. An area chart showing their performances like batting average, average balls faced, strike rate, boundary % in various matches. Also a scatter plot for tracking the top 5 players with taking strike rate and batting average into account.

  5. Specialist Fast Bowlers - Created a matrix for their name, team, batting style, innings batted, runs, balls faced, batting strike rate, batting average, batting position, boundary %. An area chart showing their performances like batting average, average balls faced, strike rate, boundary % in various matches. Also a scatter plot for tracking the top 5 players with taking strike rate and batting average into account.

Screenshots :-

Power Hitters or Openers with no selection

Power Hitters or Openers with no selection

Power Hitters or Openers with selection

Power Hitters or Openers with selection

Tool Tip Preveiw

Tool Tip Preveiw

Anchors or Middle Order

Anchors or Middle Order

Finisher or Lower Order Anchors

Finisher or Lower Order Anchors

All Rounders or Lower Middle Order

All Rounders or Lower Middle Order

Specialist Fast Bowlers or Tail End

Specialist Fast Bowlers or Tail End

Final 11

Final 11

Tool Tip for Power Hitters

Tool Tip for Power Hitters

Tool Tip for All Rounders

Tool Tip for All Rounders

Tool Tip for Bowlers

Tool Tip for Bowlers

About

Utilizing Web Scraping to extract data from online sources, we employ Python's libraries like NumPy and Pandas to clean and merge datasets. Leveraging DAX queries, we create advanced measures for PowerBI dashboards. Our reports, following a meticulous color coding scheme, feature creative charts and plots, maintaining the specified format of mockup

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