![\"\"](\"https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F413189%2F26202a2b162e16ac5e3845baa97a70d8%2FCompetition.PNG?generation=1581777902619603&alt=media\")
Hi Everyone,
\n\nWelcome to this Kaggle Analytics Competition!
\n\nFor the third year in a row, we're excited to partner with Google Cloud and the NCAA® to bring you data for the March Madness® tournament. And this year, we're extra excited to bring you this unique competition format. Can you help explain the \"madness\" of March Madness?
\n\nThis is a different format for us and it will be a little different than the machine learning competitions you may know and love.
\n\nOne of the most important differences is how winners are determined. Kaggle's traditional machine learning competitions use fancy robots and algorithms to score your submissions and generate a live leaderboard. In Analytics competitions, the hosts (us at Kaggle) will determine the best submissions by evaluating your analyses against multiple criteria. If you've participated in any of our Data Science for Good Challenges, you'll be familiar with the format.
\n\nHere's how this works:
\n\nThe competition host presents a problem and provides a dataset.
You analyze the data using any method you want. You can use machine learning, pie charts, TI-83+ calculators, whatever. For this competition specifically, your final submission needs to be in the form of a presentation with your analysis done in a Notebook. Ultimately, your goal is to analyze the problem and convince the host that your approach is the best by presenting your evidence.
We're super excited to also host our annual NCAA competitions (Men's and Women's) at the same time! This challenge is really interesting and a great opportunity to make available on Kaggle. Welcome to the competition!
", "messages": [ { "id": "746455", "postDate": "02/15/2020 02:57:27", "content": "Hi Everyone,
\n\nWelcome to this Kaggle Analytics Competition!
\n\nFor the third year in a row, we're excited to partner with Google Cloud and the NCAA® to bring you data for the March Madness® tournament. And this year, we're extra excited to bring you this unique competition format. Can you help explain the \"madness\" of March Madness?
\n\nThis is a different format for us and it will be a little different than the machine learning competitions you may know and love.
\n\nOne of the most important differences is how winners are determined. Kaggle's traditional machine learning competitions use fancy robots and algorithms to score your submissions and generate a live leaderboard. In Analytics competitions, the hosts (us at Kaggle) will determine the best submissions by evaluating your analyses against multiple criteria. If you've participated in any of our Data Science for Good Challenges, you'll be familiar with the format.
\n\nHere's how this works:
\n\nThe competition host presents a problem and provides a dataset.
You analyze the data using any method you want. You can use machine learning, pie charts, TI-83+ calculators, whatever. For this competition specifically, your final submission needs to be in the form of a presentation with your analysis done in a Notebook. Ultimately, your goal is to analyze the problem and convince the host that your approach is the best by presenting your evidence.
We're super excited to also host our annual NCAA competitions (Men's and Women's) at the same time! This challenge is really interesting and a great opportunity to make available on Kaggle. Welcome to the competition!
", "rawMarkdown": "Hi Everyone,\n\nWelcome to this Kaggle Analytics Competition!\n\nFor the third year in a row, we're excited to partner with Google Cloud and the NCAA® to bring you data for the March Madness® tournament. And this year, we're extra excited to bring you this unique competition format. Can you help explain the \"madness\" of March Madness?\n\nThis is a different format for us and it will be a little different than the machine learning competitions you may know and love. \n\nOne of the most important differences is how winners are determined. Kaggle's traditional machine learning competitions use fancy robots and algorithms to score your submissions and generate a live leaderboard. In Analytics competitions, the hosts (us at Kaggle) will determine the best submissions by evaluating your analyses against multiple criteria. If you've participated in any of our Data Science for Good Challenges, you'll be familiar with the format.\n\nHere's how this works:\n\n- The competition host presents a problem and provides a dataset. \n\n- You analyze the data using any method you want. You can use machine learning, pie charts, TI-83+ calculators, whatever. For this competition specifically, your final submission needs to be in the form of a presentation with your analysis done in a Notebook. Ultimately, your goal is to analyze the problem and convince the host that your approach is the best by presenting your evidence.\n\nWe're super excited to also host our annual NCAA competitions ([Men's](https://kaggle.com/c/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament) and [Women's](https://kaggle.com/c/google-cloud-ncaa-march-madness-2020-division-1-womens-tournament)) at the same time! This challenge is really interesting and a great opportunity to make available on Kaggle. Welcome to the competition!", "votes": null }, { "id": "746681", "postDate": "02/15/2020 11:36:38", "content": "Are winners awarded medals for this contest?
", "rawMarkdown": "Are winners awarded medals for this contest?", "votes": null }, { "id": "746784", "postDate": "02/15/2020 14:45:16", "content": "
@aashishghosh - In the competition about, it has been metnioned that, this competition doesn't award medals/points.
", "rawMarkdown": "\n\n@aashishghosh - In the competition about, it has been metnioned that, this competition doesn't award medals/points.", "votes": null }, { "id": "747793", "postDate": "02/16/2020 21:29:48", "content": "Should produce some interesting entries!
", "rawMarkdown": "Should produce some interesting entries!", "votes": null }, { "id": "749148", "postDate": "02/18/2020 11:16:07", "content": "didn't see that! thank you!
", "rawMarkdown": "didn't see that! thank you!", "votes": null }, { "id": "749352", "postDate": "02/18/2020 16:12:06", "content": "Hi! Thanks for the explanation. 😄 Only a little thing is unclear: the analysis should be on the datasets for men, women or both? I'm trying to understand if we can choose or we have to look at both. Thanks!
", "rawMarkdown": "Hi! Thanks for the explanation. 😄 Only a little thing is unclear: the analysis should be on the datasets for men, women or both? I'm trying to understand if we can choose or we have to look at both. Thanks!", "votes": null }, { "id": "749465", "postDate": "02/18/2020 18:13:05", "content": "You may choose to use either or both - there is no requirement to use both.
", "rawMarkdown": "You may choose to use either or both - there is no requirement to use both.", "votes": null }, { "id": "749474", "postDate": "02/18/2020 18:18:34", "content": "nice!
", "rawMarkdown": "nice!", "votes": null }, { "id": "753411", "postDate": "02/22/2020 06:27:47", "content": "", "rawMarkdown": "", "votes": null } ], "comments": [ { "id": 746681, "author_name": "aashishghosh", "author_url": "", "post_date": "02/15/2020 11:36:38", "content": "Are winners awarded medals for this contest?
", "votes": null, "replies": [ { "id": 746784, "author_name": "manojprabhaakr", "author_url": "", "post_date": "02/15/2020 14:45:16", "content": "
@aashishghosh - In the competition about, it has been metnioned that, this competition doesn't award medals/points.
", "votes": null, "replies": [] }, { "id": 749148, "author_name": "aashishghosh", "author_url": "", "post_date": "02/18/2020 11:16:07", "content": "didn't see that! thank you!
", "votes": null, "replies": [] } ] }, { "id": 747793, "author_name": "daneavanderbilt", "author_url": "", "post_date": "02/16/2020 21:29:48", "content": "Should produce some interesting entries!
", "votes": null, "replies": [] }, { "id": 749352, "author_name": "andradaolteanu", "author_url": "", "post_date": "02/18/2020 16:12:06", "content": "Hi! Thanks for the explanation. 😄 Only a little thing is unclear: the analysis should be on the datasets for men, women or both? I'm trying to understand if we can choose or we have to look at both. Thanks!
", "votes": null, "replies": [ { "id": 749465, "author_name": "addisonhoward", "author_url": "", "post_date": "02/18/2020 18:13:05", "content": "You may choose to use either or both - there is no requirement to use both.
", "votes": null, "replies": [] } ] }, { "id": 749474, "author_name": "", "author_url": "", "post_date": "02/18/2020 18:18:34", "content": "nice!
", "votes": null, "replies": [] }, { "id": 753411, "author_name": "ninebinder", "author_url": "", "post_date": "02/22/2020 06:27:47", "content": "", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "746455": "Hi Everyone,\n\nWelcome to this Kaggle Analytics Competition!\n\nFor the third year in a row, we're excited to partner with Google Cloud and the NCAA® to bring you data for the March Madness® tournament. And this year, we're extra excited to bring you this unique competition format. Can you help explain the \"madness\" of March Madness?\n\nThis is a different format for us and it will be a little different than the machine learning competitions you may know and love. \n\nOne of the most important differences is how winners are determined. Kaggle's traditional machine learning competitions use fancy robots and algorithms to score your submissions and generate a live leaderboard. In Analytics competitions, the hosts (us at Kaggle) will determine the best submissions by evaluating your analyses against multiple criteria. If you've participated in any of our Data Science for Good Challenges, you'll be familiar with the format.\n\nHere's how this works:\n\n- The competition host presents a problem and provides a dataset. \n\n- You analyze the data using any method you want. You can use machine learning, pie charts, TI-83+ calculators, whatever. For this competition specifically, your final submission needs to be in the form of a presentation with your analysis done in a Notebook. Ultimately, your goal is to analyze the problem and convince the host that your approach is the best by presenting your evidence.\n\nWe're super excited to also host our annual NCAA competitions ([Men's](https://kaggle.com/c/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament) and [Women's](https://kaggle.com/c/google-cloud-ncaa-march-madness-2020-division-1-womens-tournament)) at the same time! This challenge is really interesting and a great opportunity to make available on Kaggle. Welcome to the competition!", "746681": "Are winners awarded medals for this contest?", "746784": "\n\n@aashishghosh - In the competition about, it has been metnioned that, this competition doesn't award medals/points.", "747793": "Should produce some interesting entries!", "749148": "didn't see that! thank you!", "749352": "Hi! Thanks for the explanation. 😄 Only a little thing is unclear: the analysis should be on the datasets for men, women or both? I'm trying to understand if we can choose or we have to look at both. Thanks!", "749465": "You may choose to use either or both - there is no requirement to use both.", "749474": "nice!", "753411": "" }, "source": "meta" }