Is the Shooting Guard Position in the NBA Dying? (A 2020-2021 Season Study)
Once upon a time, the Shooting Guard position was one of the most competitive and coveted positions in the NBA. The 90s were led by Michael Jordan who is one of, if not, the greatest player of all time. A slew of all-time Shooting Guards like Kobe Bryant, Ray Allen, Allen Iverson and Tracy McGrady made their own meteoric impacts on the NBA landscape in the 2000s. Though they had their differences, all these Shooting Guards excelled the same at a particular skill, i.e. putting the ball into the basket.
However, with the advent of high-scoring Point Guards like Steph Curry and Damian Lillard, as well as do-it-all forwards like LeBron James and Kawhi Leonard, the importance of the position as well as its main selling point, "shooting", has seemingly faded.
In the past 10 years, the MVP has been won by a Shooting Guard only once (by James Harden). For comparison, Small Forwards and Point Guards have won 3 apiece. In the 2020-2021 NBA All-Star Game, there were 6 Shooting Guards (18 combined appearances) to the 7 Point Guards (37 combined appearances) selected, more than double their total appearances!
Hence, this study aims to answer the question "Is the Shooting Guard Position in the NBA Dying?" both from an elite player as well as role player perspectives. The datasets will comprise of per game and advanced statistics of players from the 2020-2021 season. These datasets were obtained from Basketball Reference, using its CSV export function. The chosen datasets will be used to arrive at a conclusion, primarily, is the importance of the Shooting Guard position much weaker than its two direct counterparts, Point Guard and Small Forward. The tools which I will be using to perform this data analysis are Python and the libraries NumPy, Pandas, Matplotlib and Seaborn.
This project serves as a final course project for the free online certification course Data Analysis with Python: Zero to Pandas. Through this course, I have learnt how to use Python to analyse real world data and present it in useful and captivating graphs.
Use the "Run" button to execute the code.
Chapter 1 - Downloading the Dataset
This chapter involves the downloading of datasets to be analysed from Basketball Reference. These NBA statistics include their per-game and advanced statistics, in the form of CSV files.
!pip install jovian opendatasets --upgrade --quiet# Execute this to save new versions of the notebook
jovian.commit(project="my-course-project", files=['per_game.csv', 'adv_stats.csv'])[jovian] Updating notebook "aaronobon/my-course-project" on https://jovian.ai
[jovian] Uploading additional files...
[jovian] Committed successfully! https://jovian.ai/aaronobon/my-course-project