Monday 10:00 AM–10:40 AM in Music Box 5411/Winter Garden 5412 (5th fl)

Understanding NBA Foul Calls with Python

Austin Rochford

Audience level:
Novice

Description

Since 2015, the NBA has released a detailed report of foul calls and non-calls that occur in the final two minutes of close games. This talk is a case study in using open source Python packages to analyze these reports in order to understand the relationship between game dynamics, player abilities, and foul calls. We use pandas to load the data and transform it into a format amenable to analysis,

Abstract

Since 2015, the NBA has released a detailed report of foul calls and non-calls that occur in the final two minutes of close games. This talk is a case study in using open source Python packages to analyze these reports in order to understand the relationship between game dynamics, player abilities, and foul calls. Our main goal is to quantify the relationship between player ability and foul calls. Since intentional fouls are a ubiquitous part of the NBA endgame, this data set also contains rich information about the relationship between game dynamics and intentional fouls for us to model. This case study showcases the role of open source Python pacakges in a modern statistical modeling workflow:

This case study in open source data analysis with Python based on previously published modeling work.

Subscribe to Receive PyData Updates