{"id":21352654,"url":"https://github.com/arya-io/ipl-run-chase-prediction","last_synced_at":"2026-04-24T23:32:14.929Z","repository":{"id":238486427,"uuid":"796621071","full_name":"arya-io/IPL-Run-Chase-Prediction","owner":"arya-io","description":"This model predicts the winning probabilities for both teams during the second innings of an IPL match.","archived":false,"fork":false,"pushed_at":"2025-04-04T02:44:50.000Z","size":29304,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-04T03:26:31.590Z","etag":null,"topics":["cricketanalysis","cricketstats","cricsheetdata","datascience","ipl2023","kaggledataset","logisticregression","machinelearning","opensource","predictivemodeling","python","randomforest","runchaseprediction","sportsanalytics"],"latest_commit_sha":null,"homepage":"https://run-chase-prediction.streamlit.app/","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/arya-io.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":"2024-05-06T10:01:29.000Z","updated_at":"2025-04-04T02:44:53.000Z","dependencies_parsed_at":"2025-04-04T03:33:56.781Z","dependency_job_id":null,"html_url":"https://github.com/arya-io/IPL-Run-Chase-Prediction","commit_stats":null,"previous_names":["arya-cpp/a-machine-learning-approach-for-run-chase-prediction-in-ipl","arya-io/a-machine-learning-approach-for-run-chase-prediction-in-ipl","arya-io/ipl-run-chase-prediction"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/arya-io/IPL-Run-Chase-Prediction","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arya-io%2FIPL-Run-Chase-Prediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arya-io%2FIPL-Run-Chase-Prediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arya-io%2FIPL-Run-Chase-Prediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arya-io%2FIPL-Run-Chase-Prediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/arya-io","download_url":"https://codeload.github.com/arya-io/IPL-Run-Chase-Prediction/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/arya-io%2FIPL-Run-Chase-Prediction/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259804870,"owners_count":22913903,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","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":["cricketanalysis","cricketstats","cricsheetdata","datascience","ipl2023","kaggledataset","logisticregression","machinelearning","opensource","predictivemodeling","python","randomforest","runchaseprediction","sportsanalytics"],"created_at":"2024-11-22T03:14:32.375Z","updated_at":"2026-04-24T23:32:14.877Z","avatar_url":"https://github.com/arya-io.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# A Machine Learning Approach for Run Chase Prediction in IPL\n\nThis project utilizes a machine learning model to predict the win/loss outcome of the batting team in the second innings of IPL matches, based on historical data from IPL seasons 2008 to 2023.\n\n### Dataset\nThe data used for this project was obtained from a public dataset available on Kaggle, titled [IPL 2008 to 2023 dataset](https://www.kaggle.com/datasets), contributed by user **Sri tata**. The dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. We acknowledge **Sri tata** and further recognize **cricsheet.com** as a potential source of the raw data based on the contributor's note.\n\n### Model Overview\nTwo machine learning models were developed for this project:\n- **Random Forest Classifier**: Achieved an accuracy of **99.87%**.\n- **Logistic Regression**: Achieved an accuracy of **80.38%**.\n\nThe models predict the win/loss probability for the batting team during the run chase in the second innings of IPL matches. After analysis, we selected **Logistic Regression** as the final model due to its ability to provide probability percentages with more engaging and interpretable figures for users, especially cricket fans. The **Random Forest** model, while accurate, produced more extreme predictions.\n\n---\n\n### Visualization and Results\n\n#### 1. Working of Algorithm with Pipeline\n*Fig. 1*: Illustration of the end-to-end algorithm pipeline used in the project.\n\n#### 2. Over by Over Win and Lose Probability for Chase Team\n*Fig. 2*: Visualization of the win/lose probabilities for the chasing team on an over-by-over basis.\n\n#### 3. Visual Representation of Over by Over Data Using Random Forest Algorithm\n*Fig. 3*: Graphical representation of the Random Forest model predictions for the chase scenario, over by over.\n\n### Random Forest Model Visuals\nBelow are images representing the results and functioning of the **Random Forest Algorithm**:\n\n![Random Forest Image 1](https://github.com/arya-io/IPL-Run-Chase-Prediction/assets/127336304/2de29bf8-da21-4d8c-8568-7fa397196cdd)\n![Random Forest Image 2](https://github.com/arya-io/IPL-Run-Chase-Prediction/assets/127336304/e7427548-d82c-43ce-9ca3-4a06ee57f3ba)\n![Random Forest Image 3](https://github.com/arya-io/IPL-Run-Chase-Prediction/assets/127336304/15bb35c8-8681-4877-8b5f-f7c320529615)\n\n### Logistic Regression Model Visuals\nBelow are images representing the results and functioning of the **Logistic Regression Algorithm**:\n\n![Logistic Regression Image 1](https://github.com/arya-io/IPL-Run-Chase-Prediction/assets/127336304/9b854477-92a6-43d6-8bdc-1e4b1fbdc763)\n![Logistic Regression Image 2](https://github.com/arya-io/IPL-Run-Chase-Prediction/assets/127336304/52fd0db0-38a7-4a86-b616-6bf8f44fea40)\n![Logistic Regression Image 3](https://github.com/arya-io/IPL-Run-Chase-Prediction/assets/127336304/f4cef24f-692f-4501-a375-4b534dba5fb8)\n\n---\n\n### Conclusion\nAlthough the **Random Forest** model achieved higher accuracy, the **Logistic Regression** model was chosen as the final model due to its ability to present win/loss probabilities in a more interpretable manner, making it more engaging for cricket fans.\n\n---\n\n### Video Demonstration\nA video demonstration of the project has been attached.\n\n---\n\n### License\nThis project is licensed under the **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)** license.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farya-io%2Fipl-run-chase-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Farya-io%2Fipl-run-chase-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Farya-io%2Fipl-run-chase-prediction/lists"}