{"version":1,"type":"story","url":"https://digestai.news/story/python-script-separates-basketball-shot-selection-from-making","json":"https://digestai.news/story/python-script-separates-basketball-shot-selection-from-making.json","markdown":"https://digestai.news/story/python-script-separates-basketball-shot-selection-from-making.md","slug":"python-script-separates-basketball-shot-selection-from-making","headline":"Python script separates basketball shot selection from making","summary":"A new analysis on Towards Data Science explores how machine learning can distinguish between a basketball player's shooting accuracy and their shot selection. The article argues that field-goal percentage alone is insufficient because it does not account for the difficulty of the shots taken.\n\nThe author proposes using Python to build a model that evaluates the quality of shot attempts. By analyzing historical data, the method aims to determine if a player is genuinely a good shooter or simply chooses easier shots that inflate their statistics. This approach provides a more nuanced view of player performance than traditional metrics.\n\nThe piece serves as a technical tutorial for data scientists interested in sports analytics. It demonstrates the application of predictive modeling to real-world sports data, highlighting the utility of machine learning in evaluating athletic performance beyond surface-level statistics.","keyPoints":["Field-goal percentage ignores shot difficulty, limiting its value as a performance metric.","Python and machine learning can separate shot selection quality from shooting accuracy.","The method offers a more nuanced evaluation of basketball player performance."],"whyItMatters":"It demonstrates a practical application of machine learning in sports analytics, showing how data science can refine traditional metrics to provide deeper insights into player performance.","category":{"slug":"research","name":"Research","url":"https://digestai.news/category/research"},"entities":{"companies":[],"models":[],"people":[]},"firstPublishedAt":"2026-11-03T14:00:00Z","updatedAt":"2026-11-03T14:00:00Z","sourceCount":1,"hasPrimarySource":false,"sources":[{"outlet":"Towards Data Science","title":"A Good Shooter or Just a Good Shot Selector? Using Python to Find Out","url":"https://towardsdatascience.com/a-good-shooter-or-just-a-good-shot-selector-using-python-to-find-out","publishedAt":"2026-11-03T14:00:00Z","type":"newsletter","primary":false,"lead":true}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"Python script separates basketball shot selection from making\", 3 November 2026, https://digestai.news/story/python-script-separates-basketball-shot-selection-from-making","publisher":"Digest AI","title":"Python script separates basketball shot selection from making","datePublished":"2026-11-03T14:00:00Z","url":"https://digestai.news/story/python-script-separates-basketball-shot-selection-from-making"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}