{"id":17295417,"url":"https://github.com/sfirke/predicting-march-madness","last_synced_at":"2025-09-03T16:33:00.468Z","repository":{"id":91053906,"uuid":"53179767","full_name":"sfirke/predicting-march-madness","owner":"sfirke","description":"Machine learning tutorial to create an entry for the Kaggle March Mania contest","archived":false,"fork":false,"pushed_at":"2019-03-21T17:34:24.000Z","size":16928,"stargazers_count":30,"open_issues_count":5,"forks_count":12,"subscribers_count":6,"default_branch":"master","last_synced_at":"2024-11-15T04:47:22.681Z","etag":null,"topics":["introduction","machine-learning","march-madness"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sfirke.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":"License.md","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":"2016-03-05T02:46:16.000Z","updated_at":"2024-06-24T05:37:22.000Z","dependencies_parsed_at":"2023-05-03T05:03:38.670Z","dependency_job_id":null,"html_url":"https://github.com/sfirke/predicting-march-madness","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sfirke%2Fpredicting-march-madness","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sfirke%2Fpredicting-march-madness/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sfirke%2Fpredicting-march-madness/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sfirke%2Fpredicting-march-madness/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sfirke","download_url":"https://codeload.github.com/sfirke/predicting-march-madness/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":231904235,"owners_count":18443643,"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":["introduction","machine-learning","march-madness"],"created_at":"2024-10-15T11:10:16.180Z","updated_at":"2024-12-30T19:25:10.978Z","avatar_url":"https://github.com/sfirke.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput:\n  md_document:\n    variant: markdown_github\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, echo = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE, \n  comment = \"#\u003e\",\n  fig.path = \"README-\"  \n)\noptions(width = 110)\n```\n\n## Predicting March Madness\n\nKaggle's [March Madness prediction competition](https://www.kaggle.com/c/mens-machine-learning-competition-2018/) is an accessible introduction to machine learning.  If you happen to like college basketball, you'll like that in this competition you can't bust your bracket, since you make a prediction for every game.  Plus this year there's a big prize pool, and luck plays a big enough role that you can be a legit contender fairly easily.\n\nIn 2016, my simple process using tidyverse functions in R placed in the top 10%.  I refined it a bit for 2017 and finished in the top 25%.\n\nI'm sharing my code and process here for others to use as a starting point.  My approach is similar to that of the 2014 winners, Gregory Matthews and Michael Lopez.  They published [a paper about the role that luck plays in this competition](https://arxiv.org/abs/1412.0248), putting their model in perspective.  A takeaway: take  my model, tweak it a bit to generate some distance from the field, and you are competitive to win!\n\n## What's here\n\nIn the Kaggle competition, you estimate how likely it is that Team A beats Team B, for each of the 2,278 possible matchups in the tournament.  **[My guide](march_madness_how_to.md)** documents a set of scripts for each step of:\n\n* Deciding on possible input parameters\n* Scraping the input data with the `rvest` package\n* Cleaning and joining data sources to get tidy, prediction-ready data\n* Training and evaluating machine learning models on the data\n* Making and submitting predictions\n\n\n## Licensing/usage\n\nThis code is public, please reuse it.  It's under an [MIT license](License.md).  Please acknowledge its role in any write-up or discussion of work that relies on it.  And if you win a cash prize from Kaggle using this, congratulations!  I wouldn't turn down a thank-you gift ;)\n\n## Thanks\n\nThanks to contributors **@MHenderson** and **@BillPetti**.\n\n## Contact me\n\nLet me know what you think, either on twitter @samfirke or compose a friendly e-mail to: \u003cimg src = \"http://samfirke.com/wp-content/uploads/2016/07/email_address_whitespace_top.png\" alt = \"samuel.firke AT gmail\" width = \"210\"/\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsfirke%2Fpredicting-march-madness","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsfirke%2Fpredicting-march-madness","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsfirke%2Fpredicting-march-madness/lists"}