\n
![\"\"](\"https://pbs.twimg.com/media/FCUlmjHWUAQQXKB?format=png&name=900x900\")
We are one month into the Big Data Bowl and are happy to provide you with more demo code! This time, we analyze player speed on kickoffs and once again have demos in both R and Python!
\nR: Speed on Kickoff Plays Across Surfaces - R
\nPython: Speed on Kickoff Plays Across Surfaces - Python
We use a mixed effects model to analyze player speed on the kickoff team during the first 40 frames after the ball is kicked off across various surfaces.
\nWR Matt Cole (formerly of the 49ers) had the highest player effect for max speed using data from 2020:
\n
For those of you looking to merge a player's team / jersey number in various columns ('tacklers', 'gunners', 'specialTeamsSafeties' ect.) in the PFFScoutingData.csv to 'nflId', see the 'df_jerseyMap' variable in either of the demos at the top of the \"Clean Data\" section.
\nWe encourage you to reach out with any questions you have. Good luck to everyone!
", "messages": [ { "id": "1553986", "postDate": "10/22/2021 18:16:34", "content": "We are one month into the Big Data Bowl and are happy to provide you with more demo code! This time, we analyze player speed on kickoffs and once again have demos in both R and Python!
\nR: Speed on Kickoff Plays Across Surfaces - R
\nPython: Speed on Kickoff Plays Across Surfaces - Python
We use a mixed effects model to analyze player speed on the kickoff team during the first 40 frames after the ball is kicked off across various surfaces.
\nWR Matt Cole (formerly of the 49ers) had the highest player effect for max speed using data from 2020:
\n
For those of you looking to merge a player's team / jersey number in various columns ('tacklers', 'gunners', 'specialTeamsSafeties' ect.) in the PFFScoutingData.csv to 'nflId', see the 'df_jerseyMap' variable in either of the demos at the top of the \"Clean Data\" section.
\nWe encourage you to reach out with any questions you have. Good luck to everyone!
", "rawMarkdown": "We are one month into the Big Data Bowl and are happy to provide you with more demo code! This time, we analyze player speed on kickoffs and once again have demos in both R and Python!\n\nR: [Speed on Kickoff Plays Across Surfaces - R](https://www.kaggle.com/tombliss/speed-on-kickoff-plays-across-surfaces-r)\nPython: [Speed on Kickoff Plays Across Surfaces - Python](https://www.kaggle.com/dhritiyandapally/speed-on-kickoff-plays-across-surfaces-python)\n\nWe use a mixed effects model to analyze player speed on the kickoff team during the first 40 frames after the ball is kicked off across various surfaces.\n\nWR Matt Cole (formerly of the 49ers) had the highest player effect for max speed using data from 2020:\n\n\nFor those of you looking to merge a player's team / jersey number in various columns ('tacklers', 'gunners', 'specialTeamsSafeties' ect.) in the PFFScoutingData.csv to 'nflId', see the 'df_jerseyMap' variable in either of the demos at the top of the \"Clean Data\" section.\n\nWe encourage you to reach out with any questions you have. Good luck to everyone!", "votes": null }, { "id": "1556398", "postDate": "10/24/2021 23:05:45", "content": "Thanks so much. Do you know what the columns a, o, s, dis represent in the tracking files? We are doing some of our own player speed and motions stats, but are not certain how these data columns are used. x and y are ok, pretty obvious. Do the others represent speed, acceleration>>>???
", "rawMarkdown": "Thanks so much. Do you know what the columns a, o, s, dis represent in the tracking files? We are doing some of our own player speed and motions stats, but are not certain how these data columns are used. x and y are ok, pretty obvious. Do the others represent speed, acceleration>>>???", "votes": null }, { "id": "1557127", "postDate": "10/25/2021 12:49:19", "content": "Please see https://www.kaggle.com/c/nfl-big-data-bowl-2022/data for a full data dictionary where every variable in each file is explained.
\nHere is an excerpt from that page that should answer your question:
\ns: Speed in yards/second (numeric)a: Acceleration in yards/second^2 (numeric)dis: Distance traveled from prior time point, in yards (numeric)o: Player orientation (deg), 0 - 360 degrees (numeric)Are these notebooks \"Python: Speed on Kickoff Plays Across Surfaces - Python\" and \"Analyzing Place Kicker Offset from Center - Python\" are examples of what winner notebook should look like or just starter notebooks.
\n As I've an idea for metric that can be useful, but the notebook will not be very long but will be like these notebooks or a bit longer.
\n@dhritiyandapally @tombliss
These are more meant to be starter notebooks.
\nHowever, I would say projects that explore one idea or metric extensively are preferred over those that explore many ideas or metrics not as extensively. Thus, I would worry more about quality than length.
", "rawMarkdown": "These are more meant to be starter notebooks. \n\nHowever, I would say projects that explore one idea or metric extensively are preferred over those that explore many ideas or metrics not as extensively. Thus, I would worry more about quality than length.", "votes": null }, { "id": "1637434", "postDate": "01/03/2022 22:05:07", "content": "Thanks for the great starter notebooks! I noticed in the tracking datasets that there are some measurement issues with speed. For example, in (gameId: 2020112202, playId: 2197), a player is listed as having a (converted) speed of 32 mph. The world record is 27.8 mph. Obviously that's very sensitive to the accuracy of the positioning data. Do you have any information on the accuracy of the positioning data?
You can generally assume the data is accurate to 6 inches, but there will be random issues like the one you found. I would agree that that speed value is an error which is greater than that.
", "rawMarkdown": "You can generally assume the data is accurate to 6 inches, but there will be random issues like the one you found. I would agree that that speed value is an error which is greater than that.", "votes": null } ], "comments": [ { "id": 1556398, "author_name": "roberterdman", "author_url": "", "post_date": "10/24/2021 23:05:45", "content": "Thanks so much. Do you know what the columns a, o, s, dis represent in the tracking files? We are doing some of our own player speed and motions stats, but are not certain how these data columns are used. x and y are ok, pretty obvious. Do the others represent speed, acceleration>>>???
", "votes": null, "replies": [ { "id": 1557127, "author_name": "tombliss", "author_url": "", "post_date": "10/25/2021 12:49:19", "content": "Please see https://www.kaggle.com/c/nfl-big-data-bowl-2022/data for a full data dictionary where every variable in each file is explained.
\nHere is an excerpt from that page that should answer your question:
\ns: Speed in yards/second (numeric)a: Acceleration in yards/second^2 (numeric)dis: Distance traveled from prior time point, in yards (numeric)o: Player orientation (deg), 0 - 360 degrees (numeric)Are these notebooks \"Python: Speed on Kickoff Plays Across Surfaces - Python\" and \"Analyzing Place Kicker Offset from Center - Python\" are examples of what winner notebook should look like or just starter notebooks.
\n As I've an idea for metric that can be useful, but the notebook will not be very long but will be like these notebooks or a bit longer.
\n@dhritiyandapally @tombliss
These are more meant to be starter notebooks.
\nHowever, I would say projects that explore one idea or metric extensively are preferred over those that explore many ideas or metrics not as extensively. Thus, I would worry more about quality than length.
", "votes": null, "replies": [] } ] }, { "id": 1637434, "author_name": "mdbaileykaggle", "author_url": "", "post_date": "01/03/2022 22:05:07", "content": "Thanks for the great starter notebooks! I noticed in the tracking datasets that there are some measurement issues with speed. For example, in (gameId: 2020112202, playId: 2197), a player is listed as having a (converted) speed of 32 mph. The world record is 27.8 mph. Obviously that's very sensitive to the accuracy of the positioning data. Do you have any information on the accuracy of the positioning data?
You can generally assume the data is accurate to 6 inches, but there will be random issues like the one you found. I would agree that that speed value is an error which is greater than that.
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "1553986": "We are one month into the Big Data Bowl and are happy to provide you with more demo code! This time, we analyze player speed on kickoffs and once again have demos in both R and Python!\n\nR: [Speed on Kickoff Plays Across Surfaces - R](https://www.kaggle.com/tombliss/speed-on-kickoff-plays-across-surfaces-r)\nPython: [Speed on Kickoff Plays Across Surfaces - Python](https://www.kaggle.com/dhritiyandapally/speed-on-kickoff-plays-across-surfaces-python)\n\nWe use a mixed effects model to analyze player speed on the kickoff team during the first 40 frames after the ball is kicked off across various surfaces.\n\nWR Matt Cole (formerly of the 49ers) had the highest player effect for max speed using data from 2020:\n\n\nFor those of you looking to merge a player's team / jersey number in various columns ('tacklers', 'gunners', 'specialTeamsSafeties' ect.) in the PFFScoutingData.csv to 'nflId', see the 'df_jerseyMap' variable in either of the demos at the top of the \"Clean Data\" section.\n\nWe encourage you to reach out with any questions you have. Good luck to everyone!", "1556398": "Thanks so much. Do you know what the columns a, o, s, dis represent in the tracking files? We are doing some of our own player speed and motions stats, but are not certain how these data columns are used. x and y are ok, pretty obvious. Do the others represent speed, acceleration>>>???", "1557127": "Please see https://www.kaggle.com/c/nfl-big-data-bowl-2022/data for a full data dictionary where every variable in each file is explained.\n\nHere is an excerpt from that page that should answer your question:\n\n\n- `s`: Speed in yards/second (numeric)\n- `a`: Acceleration in yards/second^2 (numeric)\n- `dis`: Distance traveled from prior time point, in yards (numeric)\n- `o`: Player orientation (deg), 0 - 360 degrees (numeric)", "1627067": "Are these notebooks \"Python: Speed on Kickoff Plays Across Surfaces - Python\" and \"Analyzing Place Kicker Offset from Center - Python\" are examples of what winner notebook should look like or just starter notebooks. \n\n As I've an idea for metric that can be useful, but the notebook will not be very long but will be like these notebooks or a bit longer.\n\n@dhritiyandapally @tombliss", "1628353": "These are more meant to be starter notebooks. \n\nHowever, I would say projects that explore one idea or metric extensively are preferred over those that explore many ideas or metrics not as extensively. Thus, I would worry more about quality than length.", "1637434": "Thanks for the great starter notebooks! I noticed in the `tracking` datasets that there are some measurement issues with speed. For example, in (`gameId`: 2020112202, `playId`: 2197), a player is listed as having a (converted) speed of 32 mph. The world record is 27.8 mph. Obviously that's very sensitive to the accuracy of the positioning data. Do you have any information on the accuracy of the positioning data?", "1638446": "You can generally assume the data is accurate to 6 inches, but there will be random issues like the one you found. I would agree that that speed value is an error which is greater than that." }, "source": "meta" }