Predicting NBA Salaries with Machine Learning
<p>The NBA stands out as one of the most <strong>lucrative </strong>and <strong>competitive </strong>leagues in sports. In the last few years, the salaries of NBA players have been on an <strong>ascending </strong>trend, but behind every awe-inspiring dunk and three-pointer lies a complex web of factors that determine these salaries.</p>
<p>From <strong>player performance</strong> and <strong>team success </strong>to <strong>market demand</strong> and <strong>endorsement deals</strong>, numerous variables come into play. Who never pondered why their team spent so much on an underperforming player, or marveled at the strategy behind a particularly successful deal?</p>
<p>In this article, we use the capabilities of machine learning with Python to <strong>predict NBA salaries</strong> and uncover the <strong>crucial factors</strong> with most impact on players’ earnings.</p>
<p>All the code and data used are available on <a href="https://github.com/GabrielPastorello/NBASalaryPrediction" rel="noopener ugc nofollow" target="_blank"><strong>GitHub</strong></a>.</p>
<h1>Understanding the problem</h1>
<p>Before diving into the problem, it is essential to grasp the fundamentals of the league’s salary system. When a player is available on the market to sign a contract with any team he is known as a <strong>free agent</strong> (FA), a term that will be used a lot in this project.</p>
<p>The NBA operates under a complex set of rules and regulations that aim to maintain competitive balance among teams. Two key concepts are at the core of this system: the <strong>salary cap</strong> and the <strong>luxury tax</strong>.</p>
<p>The <strong>salary cap</strong> serves as a spending limit, restricting how much a team can spend on player salaries in a given season. The cap is determined by the league’s revenue, and it is updated every year to ensure that teams operate within a reasonable financial framework. It also intends to prevent large-market teams from significantly outspending smaller-market counterparts, promoting parity among franchises.</p>
<p><a href="https://towardsdatascience.com/predicting-nba-salaries-with-machine-learning-ed68b6f75566">Click Here</a></p>