jai-agrawal / nba-relevant-stats

Investigating the correlation of box stats in the NBA using Pearson Correlation Coefficients.

Evaluating NBA Box Statistics for Their Relevance to a Game's Outcome

While in the past I may have elected to predict a game's outcome using merely the final or 3rd-quarter score, over the course of this project I decided to investigate the correlation between these statistics.

Data Used

Refer to this repository to access the data used for this project.

Working of the Code

As shown in main.py, the overall flow involves itself with the loading of the data as a pandas DataFrame, and the further plotting of the Pearson correlation matrix using matplotlib and seaborn. The following is the correlation matrix as plotted:

Evaluation

Among the stats which are highly correlated, is of field-goal percentage and total points (for both home and away, as would be intuitive). Their correlation stands at 0.67. Secondly, we have assists and total points - which have a corr. matrix of 0.59 and 0.57 for home and away respectively. Consequently, assists and field-goal percentage have a correlation of 0.55 and 0.52 for home and away.