# Python script separates basketball shot selection from making

Digest AI · Research · published 2026-11-03T14:00:00Z

Canonical: https://digestai.news/story/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.

The 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.

The 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.

## Key points

- 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.

## Why it matters

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.

## Sources

1. [A Good Shooter or Just a Good Shot Selector? Using Python to Find Out](https://towardsdatascience.com/a-good-shooter-or-just-a-good-shot-selector-using-python-to-find-out) (Towards Data Science, 2026-11-03)

## Cite

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

---

Written by Digest AI's editorial model from the linked sources; the sources are the record. 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
JSON: https://digestai.news/story/python-script-separates-basketball-shot-selection-from-making.json