The influx of analytics in basketball has propelled the NBA’s three-point movement. More shots are being taken from beyond the arc than ever before.
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.
In his debut preseason campaign, Zion Williamson blew expectations out of the water. The 19-year-old delivered a historic performance through four games.
I used a Random Forest Regression model to project the future success of the 2019 NBA Draft picks.
After an extremely disappointing 2019 season, the Los Angeles Lakers have added some much-needed perimeter shooting to their roster.