{"id":44947770,"url":"https://github.com/sharmaabhishekk/interactive-freeze-frames-xg","last_synced_at":"2026-02-18T10:01:04.027Z","repository":{"id":110897031,"uuid":"405455365","full_name":"sharmaabhishekk/Interactive-freeze-frames-xg","owner":"sharmaabhishekk","description":"Interactive site to mess around with xG values","archived":false,"fork":false,"pushed_at":"2024-01-14T04:06:40.000Z","size":3840,"stargazers_count":6,"open_issues_count":0,"forks_count":3,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-01-14T10:55:35.953Z","etag":null,"topics":["football","html-css-javascript","tensorflow"],"latest_commit_sha":null,"homepage":"https://sharmaabhishekk.github.io/Interactive-freeze-frames-xg","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/sharmaabhishekk.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}},"created_at":"2021-09-11T18:34:32.000Z","updated_at":"2023-08-16T23:33:24.000Z","dependencies_parsed_at":"2024-01-14T05:44:58.328Z","dependency_job_id":"e4bbc6f2-578d-4610-a1ec-35fa8c446174","html_url":"https://github.com/sharmaabhishekk/Interactive-freeze-frames-xg","commit_stats":null,"previous_names":[],"tags_count":0,"template":null,"template_full_name":null,"purl":"pkg:github/sharmaabhishekk/Interactive-freeze-frames-xg","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sharmaabhishekk%2FInteractive-freeze-frames-xg","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sharmaabhishekk%2FInteractive-freeze-frames-xg/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sharmaabhishekk%2FInteractive-freeze-frames-xg/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sharmaabhishekk%2FInteractive-freeze-frames-xg/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sharmaabhishekk","download_url":"https://codeload.github.com/sharmaabhishekk/Interactive-freeze-frames-xg/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sharmaabhishekk%2FInteractive-freeze-frames-xg/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":29575343,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-02-18T08:38:15.585Z","status":"ssl_error","status_checked_at":"2026-02-18T08:38:14.917Z","response_time":162,"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":["football","html-css-javascript","tensorflow"],"created_at":"2026-02-18T10:00:52.574Z","updated_at":"2026-02-18T10:01:04.018Z","avatar_url":"https://github.com/sharmaabhishekk.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# xG Board\n\nThis site is meant as a demo to go along with this blog post [xG with CNNs](\"https://www.opengoalapp.com/xg-with-cnns-full-study\"). The Expected Goals (xG) model developed there is a CNN-based model trained on Statsbomb's freeze frames dataset. The features it takes into account are:\n\n* shot location\n* location of the goalkeeper\n* location of the defenders\n\n## Model details\n\nTo learn more about the model, it is strongly suggested you read the full write-up linked above. The important part is the model architecture and the inputs.\nFor each shot scenario, the model takes into account three things - the location of the ball, the location of defenders, and the location of the goalkeeper, and merges them to form a single input.\n\n![Model Inputs](https://raw.githubusercontent.com/sharmaabhishekk/Interactive-freeze-frames-xg/main/public/images/model-input.png)\n\nThe model itself has 3 [Convolution + Max-pooling] layers, followed by a single dense layer to flatten out the outputs from the convolution layer. Finally, we predict a single value which is the the probability of the shot resulting in a goal (AKA, the xG value).\n\n![Model Architecture](https://raw.githubusercontent.com/sharmaabhishekk/Interactive-freeze-frames-xg/main/public/images/final_arch.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsharmaabhishekk%2Finteractive-freeze-frames-xg","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsharmaabhishekk%2Finteractive-freeze-frames-xg","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsharmaabhishekk%2Finteractive-freeze-frames-xg/lists"}