{
  "id": 367617,
  "title": "My way of analyzing the given dataset",
  "url": "/competitions/big-data-derby-2022/discussion/367617",
  "author_name": "TB Dukale",
  "post_date": "2022-11-21T13:32:01.410000",
  "votes": 3,
  "comment_count": 0,
  "views": 0,
  "content": "<p>Here is How i did it!</p>\n<p>We were asked to analyze Path Efficiency, drafting, race tactics, Horse and jockey efficiencies. While we were given geographical positions of the horses every 0.25secs, and some general details about each race (like track id, course type, distance id, run-up distance, jockey name, weight load etc).</p>\n<p>So, in order to analyze the business tasks, i believe we should know at least a <strong>general image</strong> of the race at any given time. by general image i mean we should know their speeds, headings, the heading they should keep, part of the distance_id (expected race distance) they have already covered, their distance from the edge of the track,  their angular velocity (if possible) at any given time. Knowing these facts wll give us the general image of the race at any time (like knowing distance_id covered and edge distance will let us know where and how far are all the horses from each other and so on). </p>\n<p>But to know i those listed factors (fields), we shall know the dimensions of all the race tracks. which is not given! So, what i did was I extracted field dimensions of each course from Google Earth, segregate each course regionally, and generated the fields i need. Its a long process but worth trying. </p>\n<p>Once the fields are generated it was easy to analyze path efficiency, Jockey efficiency, drafting, Horse efficiency, drifting and so on. </p>\n<p><strong>Specifically i can tell everything about a horse once i have placed it on its exact position in a race track.</strong> U can even tell if a horse is drafting or not, drifting or not and so on.</p>\n<p><a href=\"https://www.kaggle.com/code/turebogale/nytha-nyra-big-data-derby-2022\" target=\"_blank\">Here is my notebook</a></p>\n<p>pls read it and let me know if u have any comments.</p>",
  "messages": [
    {
      "id": 2038548,
      "postDate": "2022-11-21T13:32:01.410Z",
      "content": "<p>Here is How i did it!</p>\n<p>We were asked to analyze Path Efficiency, drafting, race tactics, Horse and jockey efficiencies. While we were given geographical positions of the horses every 0.25secs, and some general details about each race (like track id, course type, distance id, run-up distance, jockey name, weight load etc).</p>\n<p>So, in order to analyze the business tasks, i believe we should know at least a <strong>general image</strong> of the race at any given time. by general image i mean we should know their speeds, headings, the heading they should keep, part of the distance_id (expected race distance) they have already covered, their distance from the edge of the track,  their angular velocity (if possible) at any given time. Knowing these facts wll give us the general image of the race at any time (like knowing distance_id covered and edge distance will let us know where and how far are all the horses from each other and so on). </p>\n<p>But to know i those listed factors (fields), we shall know the dimensions of all the race tracks. which is not given! So, what i did was I extracted field dimensions of each course from Google Earth, segregate each course regionally, and generated the fields i need. Its a long process but worth trying. </p>\n<p>Once the fields are generated it was easy to analyze path efficiency, Jockey efficiency, drafting, Horse efficiency, drifting and so on. </p>\n<p><strong>Specifically i can tell everything about a horse once i have placed it on its exact position in a race track.</strong> U can even tell if a horse is drafting or not, drifting or not and so on.</p>\n<p><a href=\"https://www.kaggle.com/code/turebogale/nytha-nyra-big-data-derby-2022\" target=\"_blank\">Here is my notebook</a></p>\n<p>pls read it and let me know if u have any comments.</p>",
      "rawMarkdown": "Here is How i did it!\n\nWe were asked to analyze Path Efficiency, drafting, race tactics, Horse and jockey efficiencies. While we were given geographical positions of the horses every 0.25secs, and some general details about each race (like track id, course type, distance id, run-up distance, jockey name, weight load etc).\n\nSo, in order to analyze the business tasks, i believe we should know at least a **general image** of the race at any given time. by general image i mean we should know their speeds, headings, the heading they should keep, part of the distance_id (expected race distance) they have already covered, their distance from the edge of the track,  their angular velocity (if possible) at any given time. Knowing these facts wll give us the general image of the race at any time (like knowing distance_id covered and edge distance will let us know where and how far are all the horses from each other and so on). \n\nBut to know i those listed factors (fields), we shall know the dimensions of all the race tracks. which is not given! So, what i did was I extracted field dimensions of each course from Google Earth, segregate each course regionally, and generated the fields i need. Its a long process but worth trying. \n\nOnce the fields are generated it was easy to analyze path efficiency, Jockey efficiency, drafting, Horse efficiency, drifting and so on. \n\n**Specifically i can tell everything about a horse once i have placed it on its exact position in a race track.** U can even tell if a horse is drafting or not, drifting or not and so on.\n\n[Here is my notebook](https://www.kaggle.com/code/turebogale/nytha-nyra-big-data-derby-2022)\n\npls read it and let me know if u have any comments.",
      "votes": 2
    }
  ],
  "comments": [],
  "raw_markdown_by_id": {
    "2038548": "Here is How i did it!\n\nWe were asked to analyze Path Efficiency, drafting, race tactics, Horse and jockey efficiencies. While we were given geographical positions of the horses every 0.25secs, and some general details about each race (like track id, course type, distance id, run-up distance, jockey name, weight load etc).\n\nSo, in order to analyze the business tasks, i believe we should know at least a **general image** of the race at any given time. by general image i mean we should know their speeds, headings, the heading they should keep, part of the distance_id (expected race distance) they have already covered, their distance from the edge of the track,  their angular velocity (if possible) at any given time. Knowing these facts wll give us the general image of the race at any time (like knowing distance_id covered and edge distance will let us know where and how far are all the horses from each other and so on). \n\nBut to know i those listed factors (fields), we shall know the dimensions of all the race tracks. which is not given! So, what i did was I extracted field dimensions of each course from Google Earth, segregate each course regionally, and generated the fields i need. Its a long process but worth trying. \n\nOnce the fields are generated it was easy to analyze path efficiency, Jockey efficiency, drafting, Horse efficiency, drifting and so on. \n\n**Specifically i can tell everything about a horse once i have placed it on its exact position in a race track.** U can even tell if a horse is drafting or not, drifting or not and so on.\n\n[Here is my notebook](https://www.kaggle.com/code/turebogale/nytha-nyra-big-data-derby-2022)\n\npls read it and let me know if u have any comments."
  }
}