With almost 25% of the NBA regular season done, we can now evaluate the performances of the league’s players using expected effective field goal percentage.
In this article, I show how to use official individual matchup data in order to quantify defensive performance in the NBA with Python.
In this article, I document the calculation of Expected Effective Field Goal Percentage (XeFG%) entirely through Python and its web-scraping capabilities.
Which players pull the defense towards them due to the threat of their perimeter shooting? Let’s use ridge regression to find out.
Floor spacing is one of the most fundamental components of basketball and with NBA player tracking data, we can quantify it.
Since his rookie year, Alvin Kamara has been one of the league’s most electrifying players. His efficiency and versatility as a running back is unmatched.
Michael Thomas has always performed at an elite level. Now, the Saints’ All-Pro receiver is proving that he’s the most valuable receiver in the NFL.
In his debut preseason campaign, Zion Williamson blew expectations out of the water. The 19-year-old delivered a historic performance through four games.
Jorge Soler has finally realized his potential. The 27-year-old has made historic contributions for the Kansas City Royals in his third year with the team.