Hi Kagglers,
\nWe’re happy to announce the conclusion of the 1st and Future - Player Contact Detection competition. Thank you everyone for your participation!
\nThis competition ended with 7,076 registrations and 1,334 participants on 939 teams. We had 12,559 submissions from 72 countries. For 176 users (including 29 in the top 100!), this was their first competition. Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking!
\nThe top potential winning teams have been contacted via email for the next steps. We look forward to learning more about their winning solutions.
\nHere is a curated list of your work. Should you be looking for takeaways in the future, you have a place to see what surfaced from the work performed.
\nHappy Modeling!
\nKaggle Team
You may find the solution writeups here under the “Solution” column.
\nNFL Player Contact Detection - Getting Started
\nNFL 2.5D CNN Baseline [Inference]
\nNFL Player Contact Detection - Simple XGB baseline
\nNFL Player Contact Detection + Helmet track ftrs
\nLB:0.671, 2.5D CNN Baseline（More TTA trick）
\n🏈 NFL Players Instance Segmentation (YOLOV7)
\n[Training] NFL 2.5D CNN (LB:0.671 with TTA)
\nNFL Player Contact Detection EDA 🏈
1st place solution
\n2nd place solution - Team Hydrogen
\nWelcome to the NFL Player Contact Detection competition!
\nTop solutions from previous NFL competitions
\nCNN can Visualize Contact (4th place K_mat part)
\nThe Matthews correlation coefficient (MCC)
\n6th Place Solution (TK&penguin46 part)
\n14th Place Solution
Hi Kagglers,
\nWe’re happy to announce the conclusion of the 1st and Future - Player Contact Detection competition. Thank you everyone for your participation!
\nThis competition ended with 7,076 registrations and 1,334 participants on 939 teams. We had 12,559 submissions from 72 countries. For 176 users (including 29 in the top 100!), this was their first competition. Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking!
\nThe top potential winning teams have been contacted via email for the next steps. We look forward to learning more about their winning solutions.
\nHere is a curated list of your work. Should you be looking for takeaways in the future, you have a place to see what surfaced from the work performed.
\nHappy Modeling!
\nKaggle Team
You may find the solution writeups here under the “Solution” column.
\nNFL Player Contact Detection - Getting Started
\nNFL 2.5D CNN Baseline [Inference]
\nNFL Player Contact Detection - Simple XGB baseline
\nNFL Player Contact Detection + Helmet track ftrs
\nLB:0.671, 2.5D CNN Baseline（More TTA trick）
\n🏈 NFL Players Instance Segmentation (YOLOV7)
\n[Training] NFL 2.5D CNN (LB:0.671 with TTA)
\nNFL Player Contact Detection EDA 🏈
1st place solution
\n2nd place solution - Team Hydrogen
\nWelcome to the NFL Player Contact Detection competition!
\nTop solutions from previous NFL competitions
\nCNN can Visualize Contact (4th place K_mat part)
\nThe Matthews correlation coefficient (MCC)
\n6th Place Solution (TK&penguin46 part)
\n14th Place Solution
Congratulations!
", "rawMarkdown": "Congratulations!", "votes": null } ], "comments": [ { "id": 2659172, "author_name": "rishitaverma02", "author_url": "", "post_date": "02/19/2024 16:41:04", "content": "Congratulations!
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2176583": "Hi Kagglers,\n\nWe’re happy to announce the conclusion of the 1st and Future - Player Contact Detection competition. Thank you everyone for your participation!\n\nThis competition ended with 7,076 registrations and 1,334 participants on 939 teams. We had 12,559 submissions from 72 countries. For 176 users (including 29 in the top 100!), this was their first competition. Thank you all for your hard work in this competition and congratulations to our winners and to those who gained a new ranking! \n\nThe top potential winning teams have been contacted via email for the next steps. We look forward to learning more about their winning solutions.\n\nHere is a curated list of your work. Should you be looking for takeaways in the future, you have a place to see what surfaced from the work performed.\n\nHappy Modeling!\nKaggle Team\n\n##Solution Writeups\nYou may find the solution writeups [here](https://www.kaggle.com/competitions/nfl-player-contact-detection/leaderboard) under the “Solution” column.\n\n##Top Notebooks (With >50 Votes)\n[NFL Player Contact Detection - Getting Started](https://kaggle.com/code/robikscube/nfl-player-contact-detection-getting-started)\n[NFL 2.5D CNN Baseline [Inference]](https://kaggle.com/code/zzy990106/nfl-2-5d-cnn-baseline-inference)\n[NFL Player Contact Detection - Simple XGB baseline](https://kaggle.com/code/columbia2131/nfl-player-contact-detection-simple-xgb-baseline)\n[NFL Player Contact Detection + Helmet track ftrs](https://kaggle.com/code/ahmedelfazouan/nfl-player-contact-detection-helmet-track-ftrs)\n[LB:0.671, 2.5D CNN Baseline（More TTA trick）](https://kaggle.com/code/royalacecat/lb-0-671-2-5d-cnn-baseline-more-tta-trick)\n[🏈 NFL Players Instance Segmentation (YOLOV7)](https://kaggle.com/code/samir95/nfl-players-instance-segmentation-yolov7)\n[[Training] NFL 2.5D CNN (LB:0.671 with TTA)](https://kaggle.com/code/royalacecat/training-nfl-2-5d-cnn-lb-0-671-with-tta)\n[NFL Player Contact Detection EDA 🏈](https://kaggle.com/code/mattop/nfl-player-contact-detection-eda)\n\n##Other Top Discussions (With >35 Votes)\n[1st place solution](https://kaggle.com/c/40277/discussion/391635)\n[2nd place solution - Team Hydrogen](https://kaggle.com/c/40277/discussion/391740)\n[Welcome to the NFL Player Contact Detection competition!](https://kaggle.com/c/40277/discussion/370678)\n[Top solutions from previous NFL competitions](https://kaggle.com/c/40277/discussion/370685)\n[CNN can Visualize Contact (4th place K_mat part)](https://kaggle.com/c/40277/discussion/391719)\n[The Matthews correlation coefficient (MCC)](https://kaggle.com/c/40277/discussion/370688)\n[6th Place Solution (TK&penguin46 part)](https://kaggle.com/c/40277/discussion/391620)\n[14th Place Solution](https://kaggle.com/c/40277/discussion/391609)", "2659172": "Congratulations!" }, "source": "meta" }