{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Predicting Detailed Punt Positions using NFL Punt Analytics data","metadata":{}},{"cell_type":"markdown","source":"   With the lack of punt unit position labels for this years big data bowl, I trained a multiclass classification model using the [old NFL punt analytics competition data](https://www.kaggle.com/c/NFL-Punt-Analytics-Competition) to predict positions for punt and punt return units. Applying more detail to their positions should improve your analysis. The code is available on [github](https://github.com/jdruzzi/BDB22/blob/main/Detailed%20Player%20Positions/Position_Detail_Model.ipynb) and the data is downloadable from this notebook. Just match the gameId, playId, and nflId to Big Data Bowl data to get the positions\n\n#### Punt Unit Predicted Positions:\n - P - Punter\n - PP - Punt Protector\n - W - Wing\n - PL - Punt Lineman\n - G - Gunner\n - LS - Long Snapper\n\n#### Punt Return Unit Predicted Positions:\n - DL - Punt Return Defensive Lineman\n - V - Vise\n - PLB - Punt Return Linebacker\n - PR - Punt Returner\n\n","metadata":{}},{"cell_type":"markdown","source":"## ![](https://github.com/jdruzzi/BDB22/blob/main/Detailed%20Player%20Positions/Position_Classification_Ex.png?raw=true)","metadata":{}},{"cell_type":"markdown","source":"-------------------------------------------------------------\n## More 2022 Big Data Bowl Content\n\n### [ ⭐ HAVOC: Decoding the Punt Rush ⭐ ](https://www.kaggle.com/jdruzzi/havoc-decoding-the-punt-rush)\n\n- [Quantifying Punt Rush Ability with HAVOC](https://www.kaggle.com/jdruzzi/quantifying-punt-rush-ability-with-havoc)\n\n- [Extended: How to Improve HAVOC & Block Punts 📝](https://www.kaggle.com/jdruzzi/extended-how-to-improve-havoc-block-punts)\n\n- [Alternate Outcomes WIth Punt Pressure & HAVOC](https://www.kaggle.com/jdruzzi/alternate-outcomes-with-punt-pressure-havoc)\n\n\n#### Alternative Punt / Punt Rush\n\n- [Evaluating Punt/Punt Rush Units with Convex Hulls](https://www.kaggle.com/jdruzzi/evaluate-punt-punt-return-units-with-convex-hulls)\n\n#### Punt Protection\n\n- [Estimating Punt Protection Assignments](https://www.kaggle.com/jdruzzi/estimating-punt-protection-blocking-assignments)\n\n#### Misc / Additional Data\n- [Generating Detailed Punt Positions](https://www.kaggle.com/jdruzzi/generating-detailed-punt-positions)\n\n- [Combine, Snap Counts, & Left Footed Kicker Data](https://www.kaggle.com/jdruzzi/combine-snap-counts-left-footed-kicker-data)\n\n------------------------------------------------------------\n#### Socials\n- [Twitter](https://twitter.com/j_druzzi)\n- [LinkedIn](https://www.linkedin.com/in/joe-andruzzi-27b3a7149/)\n\n------------------------------------------------------------","metadata":{}}]}