{"id":17103276,"url":"https://github.com/pravj/data-artistry-2018","last_synced_at":"2026-04-25T16:34:32.178Z","repository":{"id":152091027,"uuid":"123816181","full_name":"pravj/data-artistry-2018","owner":"pravj","description":":bar_chart: Submission for Data Artistry Tournament (2018) **Selected for finals**","archived":false,"fork":false,"pushed_at":"2018-03-09T19:28:22.000Z","size":408,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-23T19:47:01.313Z","etag":null,"topics":["d3js","data-visualization","mapbox","react"],"latest_commit_sha":null,"homepage":"https://pravj-da18.surge.sh/","language":"Jupyter Notebook","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/pravj.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":"2018-03-04T18:23:35.000Z","updated_at":"2018-03-19T08:03:58.000Z","dependencies_parsed_at":null,"dependency_job_id":"7744f074-8a6a-422f-a81f-d01b95ab5405","html_url":"https://github.com/pravj/data-artistry-2018","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/pravj/data-artistry-2018","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pravj%2Fdata-artistry-2018","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pravj%2Fdata-artistry-2018/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pravj%2Fdata-artistry-2018/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pravj%2Fdata-artistry-2018/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pravj","download_url":"https://codeload.github.com/pravj/data-artistry-2018/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pravj%2Fdata-artistry-2018/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32269462,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-25T09:15:33.318Z","status":"ssl_error","status_checked_at":"2026-04-25T09:15:31.997Z","response_time":59,"last_error":"SSL_read: 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":["d3js","data-visualization","mapbox","react"],"created_at":"2024-10-14T15:32:42.063Z","updated_at":"2026-04-25T16:34:32.163Z","avatar_url":"https://github.com/pravj.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"Submission for Data Artistry Tournament (2018)\n==============================================\n\n[Demo](https://pravj-da18.surge.sh/)\n\n* Performance of IPL teams on Indian grounds\n* Effect of toss on match results\n* Change in the performance (Average Score, Win/Loss Ratio) of teams over time\n* Most frequent bowler/batsman pair (dismissal)\n* 100 balls of IPL (dot matrix)\n\n### Why did you use this tool?\nI have used D3 as the core tool for the visualization. The main reason is the maturity the project provides, both in terms of features/API and community support. Also, I have used MapBox for one of map charts.\n\n### Why did you pick the particular dataset?\nI am a Cricket fan, was a great fielder and a medium fast bowler in my childhood. Also, I was thinking of working on another side-project using the old matches data, and them came this tournament. I would love to utilize the dataset further and work on it.\n\n### Pros \u0026 cons of the tool\n+ D3 community, there are more and more tools to get people started. Be it \"bl.ocks\" or \"blockbuilder\" or the latest \"observable\".\n+ Similar to the goodness provided by GitHub, you can fork an existing block and implement it according to your needs.\n- I find that writing D3 is a little verbose. I haven't tried Vega and other DSL sort of toos, would love to frame an opinion about them too.\n\n### Ease of implementation / adoption\nIf I'm getting this question correctly, it was relatively easy for the chart other than the \"slopegraph\" that I've created. For it, I have used a block as a reference and it was the tough part. Overall, D3 is one my favorite tools anyway.\n\n\u003e All I could do in two days\n\u003e\n\u003e As my teamname (timeconstraint) for the tournament suggests, I didn't get much time to work on it. (Worked only on March 4 and 9)\n\n[Pravendra Singh](https://hackpravj.com)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpravj%2Fdata-artistry-2018","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpravj%2Fdata-artistry-2018","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpravj%2Fdata-artistry-2018/lists"}