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Visualizing and quantifying defensive impact
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Visualizing and quantifying defensive impact


This repo contains a notebook (visualize_defense.ipynb) using the playerdashptshotdefend endpoint of the API. With the Player and League classes from the py_ball package, the code here explores the defensive data for when a player is the closest defender to a field goal attempt.


The figures below show the DIFF% for Boban Marjanovic and Joel Embiid in the 2018-19 NBA Season. DIFF% is the difference between the field goal percentage when the given player is the closest defender and the expected field goal percentage. Therefore, negative values indicate better defense by the given player. The figures below are split into four sections (not necessarily exclusive): Less than 6ft, Less than 10ft, 2 pointers, and 3 pointers.

These figures indicate that Boban and Joel defend very well on 2 pointers, especially close to the basket.

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