{
  "id": 133424,
  "title": "Women's Kenpom Dataset?",
  "url": "/competitions/google-cloud-ncaa-march-madness-2020-division-1-womens-tournament/discussion/133424",
  "author_name": "",
  "post_date": "2020-03-02T20:14:34.186224700Z",
  "votes": 2,
  "comment_count": 2,
  "views": 0,
  "content": "<p>Hi!\nDo you know any reliable dataset with external Ratings, Adjusted Offensive Efficiency, Adjusted Defensive Efficiency etc. for women's competition? \nSimilar to the Kenpom dataset from NCAA Men's Competition.</p>",
  "messages": [
    {
      "id": "761644",
      "postDate": "03/02/2020 20:14:34",
      "content": "<p>Hi!\nDo you know any reliable dataset with external Ratings, Adjusted Offensive Efficiency, Adjusted Defensive Efficiency etc. for women's competition? \nSimilar to the Kenpom dataset from NCAA Men's Competition.</p>",
      "rawMarkdown": "Hi!\nDo you know any reliable dataset with external Ratings, Adjusted Offensive Efficiency, Adjusted Defensive Efficiency etc. for women's competition? \nSimilar to the Kenpom dataset from NCAA Men's Competition.",
      "votes": null
    },
    {
      "id": "767393",
      "postDate": "03/09/2020 15:17:18",
      "content": "<p>Not that I am aware of, but efficiencies can be calculated. Last year's winner calculated it <a href=\"https://www.kaggle.com/jleecook/1st-place-regression-to-projected-mov/\">here </a>at lines 30-37.</p>",
      "rawMarkdown": "Not that I am aware of, but efficiencies can be calculated. Last year's winner calculated it [here ](https://www.kaggle.com/jleecook/1st-place-regression-to-projected-mov/)at lines 30-37.",
      "votes": null
    },
    {
      "id": "767418",
      "postDate": "03/09/2020 15:51:59",
      "content": "<p>Thank you for your comment! \nThat is exactly the way I did it, using jleecook's formula. Although it seems the Offensive Efficiency and the Defensive Efficiency calculated this way are not very informative in my model.\nIs this consistent with your findings? </p>",
      "rawMarkdown": "Thank you for your comment! \nThat is exactly the way I did it, using jleecook's formula. Although it seems the Offensive Efficiency and the Defensive Efficiency calculated this way are not very informative in my model.\nIs this consistent with your findings?",
      "votes": null
    }
  ],
  "comments": [
    {
      "id": 767393,
      "author_name": "davelo12",
      "author_url": "",
      "post_date": "03/09/2020 15:17:18",
      "content": "<p>Not that I am aware of, but efficiencies can be calculated. Last year's winner calculated it <a href=\"https://www.kaggle.com/jleecook/1st-place-regression-to-projected-mov/\">here </a>at lines 30-37.</p>",
      "votes": null,
      "replies": [
        {
          "id": 767418,
          "author_name": "roburadu",
          "author_url": "",
          "post_date": "03/09/2020 15:51:59",
          "content": "<p>Thank you for your comment! \nThat is exactly the way I did it, using jleecook's formula. Although it seems the Offensive Efficiency and the Defensive Efficiency calculated this way are not very informative in my model.\nIs this consistent with your findings? </p>",
          "votes": null,
          "replies": []
        }
      ]
    }
  ],
  "raw_markdown_by_id": {
    "761644": "Hi!\nDo you know any reliable dataset with external Ratings, Adjusted Offensive Efficiency, Adjusted Defensive Efficiency etc. for women's competition? \nSimilar to the Kenpom dataset from NCAA Men's Competition.",
    "767393": "Not that I am aware of, but efficiencies can be calculated. Last year's winner calculated it [here ](https://www.kaggle.com/jleecook/1st-place-regression-to-projected-mov/)at lines 30-37.",
    "767418": "Thank you for your comment! \nThat is exactly the way I did it, using jleecook's formula. Although it seems the Offensive Efficiency and the Defensive Efficiency calculated this way are not very informative in my model.\nIs this consistent with your findings?"
  },
  "source": "meta"
}