Linking NBA Salary to Performance: Sample Player Analysis With Python
Spotting high quality & low cost free agents
(Notes: All opinions are my own)
Introduction
This article explores correlations between NBA player salary data and actual on-court performance, using stats from the 2019/2020 regular season. It also illustrates how you can conduct web-scraping and simple data analysis in Python.
The aim is to identify above-average performers with below-average pay (in per minute terms) about to be free-agents in the upcoming off-season, as these players might represent sound opportunities to add quality rotation players to a team (from a front-office perspective).
Data Sources: Basketball Reference, HoopsHype
For this article, I am also going to show you how you can scrape and parse NBA salary data from HoopsHype.
You are additionally going to make use of in-game statistics coming from Basketball Reference.The Basketball Reference relevant dataset (2019/2020 NBA Season Player Totals)can be exported into csv format.
If you are also looking to learn how to scrape data from Basketball Reference in order to automate data ingestion, check out my other NBA-focused article below:
