Hi Everyone,
\n\nWelcome to this Kaggle Analytics Competition!
\n\nFor the second year in a row, we're throwing something different at you, and we're super excited to be working with the NFL to bring you this unique competition format. In last year's competition, you used analytics to not only analyze punt plays, but an actual rule was added to help keep players safer as a result.
\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 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, or last year's competition, 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 have two competitions with the NFL live at the same time! This challenge is really interesting and a great opportunity to make available on Kaggle. Thanks to the NFL for hosting, and welcome to the competition!
", "messages": [ { "id": 681237, "postDate": "2019-11-25T22:05:41.680Z", "content": "Hi Everyone,
\n\nWelcome to this Kaggle Analytics Competition!
\n\nFor the second year in a row, we're throwing something different at you, and we're super excited to be working with the NFL to bring you this unique competition format. In last year's competition, you used analytics to not only analyze punt plays, but an actual rule was added to help keep players safer as a result.
\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 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, or last year's competition, 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 have two competitions with the NFL live at the same time! This challenge is really interesting and a great opportunity to make available on Kaggle. Thanks to the NFL for hosting, and welcome to the competition!
", "rawMarkdown": "Hi Everyone,\n\nWelcome to this Kaggle Analytics Competition!\n\nFor the second year in a row, we're throwing something different at you, and we're super excited to be working with the NFL to bring you this unique competition format. In last year's competition, you used analytics to not only analyze punt plays, but [an actual rule was added](https://operations.nfl.com/the-rules/2019-rules-changes-and-points-of-emphasis/) to help keep players safer as a result.\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 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, or [last year's competition](https://www.kaggle.com/c/NFL-Punt-Analytics-Competition), 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\n\nWe're super excited to have [two competitions](https://www.kaggle.com/c/nfl-big-data-bowl-2020) with the NFL live at the same time! This challenge is really interesting and a great opportunity to make available on Kaggle. Thanks to the NFL for hosting, and welcome to the competition!", "votes": 13 }, { "id": 687685, "postDate": "2019-12-04T16:52:31.970Z", "content": "Hi,\ncould you please enlighten us, which of the rules in the link you posted came as an aftermath of the last competition? :)
", "rawMarkdown": "Hi,\ncould you please enlighten us, which of the rules in the link you posted came as an aftermath of the last competition? :)" }, { "id": 687349, "postDate": "2019-12-04T09:27:42.710Z", "content": "A question?\nThe notebook should be in presentation format or we need to prepare a presentation slides along with notebook analysis
", "rawMarkdown": "A question?\nThe notebook should be in presentation format or we need to prepare a presentation slides along with notebook analysis", "replies": [ { "id": 687666, "postDate": "2019-12-04T16:25:09.133Z", "content": "You will need to prepare presentation slides along with the notebook analysis. But having your notebook in presentation format is very helpful :)
", "rawMarkdown": "You will need to prepare presentation slides along with the notebook analysis. But having your notebook in presentation format is very helpful :)", "votes": 1 } ] }, { "id": 681872, "postDate": "2019-11-26T16:20:08.843Z", "content": "What is DM_M1, M7, M28 and M42?
", "rawMarkdown": "What is DM_M1, M7, M28 and M42?", "replies": [ { "id": 681998, "postDate": "2019-11-26T19:21:13.017Z", "content": "Designates how long the player missed time.
\n\nDM_M42 = 1 then '42 or more days'\nDM_M28 = 1 then '28 or more days'\nDM_M7 = 1 then '7 or more days'\nDM_M1 = 1 then '1 or more days'
", "rawMarkdown": "Designates how long the player missed time. \n\nDM_M42 = 1 then '42 or more days'\nDM_M28 = 1 then '28 or more days'\nDM_M7 = 1 then '7 or more days'\nDM_M1 = 1 then '1 or more days'" }, { "id": 694830, "postDate": "2019-12-14T06:42:05.477Z", "content": "Some Player have 1s in all DMXXX variables like below playerrkey. Does it mean injury for this player is >=42 days\nPlayerKey GameID PlayKey BodyPart Surface DM_M1 DM_M7 DM_M28 DM_M42\n39873 39873-4 39873-4-32 Knee Synthetic 1 1 1 1
", "rawMarkdown": "Some Player have 1s in all DMXXX variables like below playerrkey. Does it mean injury for this player is >=42 days\nPlayerKey\tGameID\tPlayKey\tBodyPart\tSurface\tDM_M1\tDM_M7\tDM_M28\tDM_M42\n39873\t39873-4\t39873-4-32\tKnee\t Synthetic\t1\t1\t1\t1\n" } ] }, { "id": 700712, "postDate": "2019-12-22T13:37:19.897Z", "rawMarkdown": "", "isDeleted": true }, { "id": 681296, "postDate": "2019-11-26T00:18:33.780Z", "rawMarkdown": "", "isDeleted": true } ], "comments": [ { "id": 687685, "author_name": "habbeda", "author_url": "", "post_date": "2019-12-04T16:52:31.970000", "content": "Hi,\ncould you please enlighten us, which of the rules in the link you posted came as an aftermath of the last competition? :)
", "votes": 0, "replies": [] }, { "id": 687349, "author_name": "CBR", "author_url": "", "post_date": "2019-12-04T09:27:42.710000", "content": "A question?\nThe notebook should be in presentation format or we need to prepare a presentation slides along with notebook analysis
", "votes": 0, "replies": [ { "id": 687666, "author_name": "Addison Howard", "author_url": "", "post_date": "2019-12-04T16:25:09.133000", "content": "You will need to prepare presentation slides along with the notebook analysis. But having your notebook in presentation format is very helpful :)
", "votes": 1, "replies": [] } ] }, { "id": 681872, "author_name": "Tomas", "author_url": "", "post_date": "2019-11-26T16:20:08.843000", "content": "What is DM_M1, M7, M28 and M42?
", "votes": 0, "replies": [ { "id": 681998, "author_name": "DanielMeier-Conroy", "author_url": "", "post_date": "2019-11-26T19:21:13.017000", "content": "Designates how long the player missed time.
\n\nDM_M42 = 1 then '42 or more days'\nDM_M28 = 1 then '28 or more days'\nDM_M7 = 1 then '7 or more days'\nDM_M1 = 1 then '1 or more days'
", "votes": 0, "replies": [] }, { "id": 694830, "author_name": "Harish Nagpal", "author_url": "", "post_date": "2019-12-14T06:42:05.477000", "content": "Some Player have 1s in all DMXXX variables like below playerrkey. Does it mean injury for this player is >=42 days\nPlayerKey GameID PlayKey BodyPart Surface DM_M1 DM_M7 DM_M28 DM_M42\n39873 39873-4 39873-4-32 Knee Synthetic 1 1 1 1
", "votes": 0, "replies": [] } ] }, { "id": 700712, "author_name": "", "author_url": "", "post_date": "2019-12-22T13:37:19.897000", "content": "", "votes": 0, "replies": [] }, { "id": 681296, "author_name": "", "author_url": "", "post_date": "2019-11-26T00:18:33.780000", "content": "", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "681237": "Hi Everyone,\n\nWelcome to this Kaggle Analytics Competition!\n\nFor the second year in a row, we're throwing something different at you, and we're super excited to be working with the NFL to bring you this unique competition format. In last year's competition, you used analytics to not only analyze punt plays, but [an actual rule was added](https://operations.nfl.com/the-rules/2019-rules-changes-and-points-of-emphasis/) to help keep players safer as a result.\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 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, or [last year's competition](https://www.kaggle.com/c/NFL-Punt-Analytics-Competition), 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\n\nWe're super excited to have [two competitions](https://www.kaggle.com/c/nfl-big-data-bowl-2020) with the NFL live at the same time! This challenge is really interesting and a great opportunity to make available on Kaggle. Thanks to the NFL for hosting, and welcome to the competition!", "687685": "Hi,\ncould you please enlighten us, which of the rules in the link you posted came as an aftermath of the last competition? :)", "687349": "A question?\nThe notebook should be in presentation format or we need to prepare a presentation slides along with notebook analysis", "681872": "What is DM_M1, M7, M28 and M42?", "700712": "", "681296": "" } }