Shay Samat

I am currently working on a project, where I am utilizing SQL and data visualization techniques to analyze NBA officiating decisions. Beginning with data collection and preparation, I loaded NBA Last Two Minute Reports (L2M) data into a structured format using DuckDB and Pandas. Through exploratory data analysis (EDA) with SQL queries, I examined the distribution and frequency of different types of calls, their ratings, and player involvement. Temporal analysis revealed trends in officiating decisions throughout games, highlighting variations across different periods. Team and player analyses unveiled the impact of officiating on both teams and individual players, identifying those with the highest counts of both correct and incorrect calls. Leveraging Matplotlib, I visualized these findings with bar plots, providing clear summaries of top players with the highest counts of incorrect calls, total calls, and correct calls. This project demonstrates the power of SQL and data visualization in dissecting and understanding complex phenomena like NBA officiating. I am currently working on ways to expand on this project, for instance, looking into creating a statistic to measure “Clutchness” based on the L2M of a given NBA game.