Playing with NBA stats

I am a huge fan of the NBA and typically take an analytics approach to sports. I have my own NBA blog where my main focus this season has been on projecting teams’ win totals. In the future I plan to blog here about how I built that website and my win projection tools with Python. This post is about a smaller project I recently put together.

This project was my first time using nba_py, a Python wrapper for the NBA.com API. This means I can directly access stats from NBA.com in my Python scripts. Just as a matter of curiosity, I was wondering: which player had the best game against each team? For example, which player had the best game against the elite Houston Rockets? Who had the best game against the injury-laden Memphis Grizzlies? As a measure of best game I used Game Score, a statistic created by basketball analytics pioneer John Hollinger. Game Score attempts to put 1 number to a player’s contribution across major stats like points, assist, rebounds, etc. Here are the all-time best Game Scores.

As a first run, I used last season’s data. Using nba_py, I went through every game and calculated the Game Score for each player. Then, I set aside the best Game Score from each game along with the player and opponent. With this list of the best Game Scores, I then went through each team as the opponent and set aside the best Game Score. Below is the list of best Game Score against each team. What really stood out to me was how Russell Westbrook had the best Game Score against five different teams – no wonder he won the MVP award!