The objective of this project is to scrape, process, analyze, and visualize cricket match data available at Cricsheet. Learners will use Selenium to scrape JSON files of different cricket match types (ODI, T20, Test), store the data in SQL tables, and create a Power BI dashboard to analyze key performance metrics. The project also includes writing 20 SQL queries to uncover insights such as top-performing players, team statistics, and match outcomes.And an EDA using python libraries such as matplotlib,seaborn,plotly.
Business Use Cases
- Player Performance Analysis: Analyze players' performance across different match formats (Test, ODI, T20).
- Team Insights: Compare team performance over time and across match types.
- Match Outcomes: Understand win/loss patterns, margin of victories, and trends.
- Strategic Decision-Making: Assist analysts, coaches, and management in making informed decisions.
- Fan Engagement: Present interactive dashboards for fans to explore match data and statistics.
Approach
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Data Scraping Using Selenium 1.1. Automate navigation to https://cricsheet.org/matches/.
1.2. Use Selenium to scrape and download all the JSON files available on the page. -
Data Transformation 2.1. Parse JSON files using Python’s pandas library.
2.2. Create separate DataFrames for Test, ODI, and T20 match types. -
Database Management 3.1. Create an SQL database (e.g., MySQL or SQLite). 3.2. Design separate tables for each match type: test_matches, odi_matches, t20_matches. 3.3. Test Match Tables 3.3.1.1. testmatchinfo Table fields and datatype: Field Type match_id int file_id int balls_per_over int dates_0 date event_name varchar(255) gender varchar(10) match_type varchar(55) season varchar(10) team_type varchar(55) teams_0 varchar(55) teams_1 varchar(55) toss_winner varchar(55) toss_decision varchar(55) winner varchar(55) by_runs decimal(5,0) by_innings decimal(3,0) by_wickets decimal(3,0) outcome varchar(55) outcome_by varchar(55)
3.3.1.2. Testinningdata: Contains following fields and datatype:
Field Type match_id bigint batter text bowler text non_striker text runs_batter bigint runs_extras bigint runs_total bigint file_id bigint inning bigint Over bigint delivery bigint team text season text team_type text gender text wickets_0_kind text
3.4. ODI Match Tables 3.4.1. odimatchinfo Contains following fields and datatype:
Field Type match_id int file_id int balls_per_over int dates_0 date event_name varchar(255) gender varchar(10) match_type varchar(55) season varchar(10) team_type varchar(55) teams_0 varchar(55) teams_1 varchar(55) toss_winner varchar(55) toss_decision varchar(55) winner varchar(55) by_wickets decimal(2,0) by_runs decimal(3,0) outcome varchar(55)
3.4.2. odiinningdata Contains following fields and datatype:
Field Type match_id bigint batter text bowler text non_striker text runs_batter bigint runs_extras bigint runs_total bigint file_id bigint inning bigint Over bigint delivery bigint team text season text team_type text gender text extras_wides double wickets_0_kind text
3.4.3. t20matchinfo Contains following fields and datatype:
Field Type match_id int file_id int balls_per_over int dates_0 date event_name varchar(255) gender varchar(10) match_type varchar(55) season varchar(10) team_type varchar(55) teams_0 varchar(55) teams_1 varchar(55) toss_winner varchar(55) toss_decision varchar(55) winner varchar(55) by_wickets decimal(2,0) by_runs decimal(3,0) outcome varchar(55)
3.4.4. t20inningdata Contains following fields and datatype:
Field Type match_id bigint batter text bowler text non_striker text runs_batter bigint runs_extras bigint runs_total bigint file_id bigint inning bigint Over bigint delivery bigint team text season text team_type text gender text extras_wides double wickets_0_kind text
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SQL Queries for Data Analysis SQL queries for creating tables and extracting data is written in the below python file. SQL_TABLE_&_QUERRY.ipynb
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EDA using Python different visualizations using libraries like matplotlib,seaborn,plotly All visualizations are created in the query file stated above and visuals. A powerpoint presentation sing the visuals are prepared for the Test Team of Australia. Both visuals and presentation are saved in the file named: Pandas Plots and Presentation
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Power BI Dashboard PowerBI dash board is created for better data visualization and analysis. 04 numbers of Power BI files are created. I. For Players a. For Test Matches - test_cricsheet_inning .pbix b. For ODI Matches - odi_cricsheet_inning.pbix c. For T20 Matches - t20_cricsheet_inning.pbix II. For Matches a. For Match Summary of all formats - match_info_all_format.pbix All .pbix files are saved in the folder Power BI Files along with the screenshots of some dashboards.
Results ● Automated scraping of JSON files from Cricsheet. ● Structured SQL database with separate tables for Test, ODI, and T20 matches. ● SQL queries to analyze player and team performance metrics. ● A dynamic Power BI dashboard to present insights visually.
Technical Tags ● Matplotlib,Seaborn ● Web Scraping ● Selenium ● Python ● Pandas ● SQL (MySQL/SQLite) ● Power BI ● JSON ● Data Analysis