{"id":15208863,"url":"https://github.com/rhejos/ipl_data_analysis","last_synced_at":"2026-03-07T18:03:21.212Z","repository":{"id":239528276,"uuid":"799777663","full_name":"rhejos/ipl_data_analysis","owner":"rhejos","description":"This project explores data analysis of the Indian Premier League utilizing AWS S3, Apache Spark, python, and SQL.","archived":false,"fork":false,"pushed_at":"2024-05-16T06:39:01.000Z","size":283,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-06T19:43:49.761Z","etag":null,"topics":["apache-spark","aws-s3","databricks-notebooks","pyspark","sql"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rhejos.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-05-13T04:45:17.000Z","updated_at":"2024-05-30T04:06:49.000Z","dependencies_parsed_at":"2024-09-24T09:10:38.450Z","dependency_job_id":null,"html_url":"https://github.com/rhejos/ipl_data_analysis","commit_stats":{"total_commits":15,"total_committers":1,"mean_commits":15.0,"dds":0.0,"last_synced_commit":"473a71fd8a1dfff6d103eb4204087fbf829e393f"},"previous_names":["rhejos/ipl_data_analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/rhejos/ipl_data_analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rhejos%2Fipl_data_analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rhejos%2Fipl_data_analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rhejos%2Fipl_data_analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rhejos%2Fipl_data_analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rhejos","download_url":"https://codeload.github.com/rhejos/ipl_data_analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rhejos%2Fipl_data_analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30225448,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-07T17:00:40.062Z","status":"ssl_error","status_checked_at":"2026-03-07T17:00:39.026Z","response_time":53,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["apache-spark","aws-s3","databricks-notebooks","pyspark","sql"],"created_at":"2024-09-28T07:02:44.657Z","updated_at":"2026-03-07T18:03:21.195Z","avatar_url":"https://github.com/rhejos.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Indian Premier League Data Analysis \n\nBall By Ball Data of all the IPL seasons (637 matches including 2017).\n\n## SUMMARY\nThis data set has the ball by ball data of all the Indian Premier League (IPL) matches till 2017 season. This is made up of six different datasets.\n\nSource: http://cricsheet.org/ (data is available on this website in the YAML format. This is converted to CSV format by using R Script ,SQL,SSIS.\n\nProject Design : https://www.youtube.com/watch?v=0iNJPKheQqM \n\n\n### Description\nIn this project I will be using Apache Spark, Python, \u0026 SQL to complete this project.\n\nThe Indian Premier League is a cricket league. This data is made up of six seperate datasets.\n- Ball by ball\n- Match\n- Player\n- Player match\n- Team\n  \n The data dictionary for these datasets can be found here. https://data.world/raghu543/ipl-data-till-2017/workspace/data-dictionary\n\n#### AWS S3 Bucket\nThe information for this project is stored within rhea-github AWS S3 bucket.\n\n#### Databricks platform\nDatabricks platform and juypter notebook was utilized for this project.\n\n\n### Visualizations\n\n#### Top 10 Economical Players within Indian Premier League \n\nThis shows the top ten economical players. An economical bowler/player is one who concedes relatively few runs per over while bowling.\n\n![image](https://github.com/rhejos/ipl_data_analysis/assets/153791988/bd85fb2d-4401-422e-a3c0-4c594d9eca98)\n\n#### Impact of winning toss on match outcomes\n\nThis shows the count of matches that were won or loss if the team won the coin toss.\n\n![image](https://github.com/rhejos/ipl_data_analysis/assets/153791988/c74f90fe-3b9c-45c1-aa89-ef862ce69616)\n\n#### The Top 10 scorers avergae runs for winning matches \n\nThis goes over the average runs the top scorer had for winning matches.\n\n![image](https://github.com/rhejos/ipl_data_analysis/assets/153791988/41ba4093-bb67-4593-b101-72a2ccca91fe)\n\n#### Distribution of Scores by Venue \n\nThis explores the coring trends based on match venues.\n\n![image](https://github.com/rhejos/ipl_data_analysis/assets/153791988/bb20a495-ec62-4c1e-86e4-8caf808bc82c)\n\n\n#### Team performance\n\nThis ranks teams performance by how many wins they had after winning the toss.\n\n![image](https://github.com/rhejos/ipl_data_analysis/assets/153791988/19e53869-6b4c-465f-874f-ecbb33c943d7)\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frhejos%2Fipl_data_analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frhejos%2Fipl_data_analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frhejos%2Fipl_data_analysis/lists"}