Rudy Gobert has unequivocally been the league’s best and most valuable defensive player over the past three seasons. And he’s only getting better.
It’s common knowledge that home-court advantage is a significant factor in the NBA. But why? Could officiating be a factor?
Can we use basketball statistics to find which NBA players tend to hunt for triple-doubles the most?
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.