The frequency of triple-doubles has exploded over the past few years. Stat sheets are being stuffed like never before. What’s the reason behind it?
We all know that NBA players are shooting more 3-pointers than ever before. However, we’re not talking about the increasing frequency of deep 3-pointers.
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.
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.