ABOUT aTS%
Adjusted True Shooting % (aTS%) is an adapted and modified version
of the commonly used metric, True Shooting % (TS%). TS% by itself
is a good source of measuring efficiency, but it fails to account
for one thing that can sway a player’s efficiency based on their
situation: usage rate. In theory, the more shots a player takes,
and handles the ball, the less efficient they would become as there
is less variance due to hot shooting and there is also more fatigue
due to higher shot volume. aTS% rewards players who maintain elite
efficiency despite playing in high load situations, and does the
opposite for players in low volume situations. It incorporates usage
rate in its formula, creating a better and more advanced way of looking
at player efficiency. The formula for aTS% was derived through linear
regression amongst the two variables, TS% and Usage Rate. After finding
the correlation, a formula could be derived that incorporates that found
correlation with the TS% and usage rate of the player, as well as the
league average TS% and intercept. In the future, aTS% looks to incorporate
other factors such as spacing and teammate playmaking that can also have an
effect on efficiency. All in all, aTS% aims to be the future of efficiency
measuring statistics in the NBA.
ABOUT DEVELOPMENT
The development of aTS%, or Adjusted True Shooting Percentage, represents
a fascinating intersection of data science, web development, and basketball
analytics. This innovative statistic was brought to life through a multi-step
process that began with the extraction of data by scraping the NBA API using
Python. This versatile programming language proved instrumental in collecting
the necessary raw data for aTS%. Once obtained, this data underwent meticulous
transformation and analysis using the Pandas library. Linear regression, a
statistical technique, was employed to create the aTS% formula. This formula
dynamically incorporated various variables crucial to accurately assessing a
player's shooting performance. These variables, such as TS%, Usage Rate, league
average TS%, correlation coefficient, and intercept were seamlessly integrated
into a comprehensive DataFrame, a structured data representation, to facilitate
further analysis. The magic of aTS% truly came to life on the frontend of the
project, where HTML played a pivotal role. The DataFrame generated from the
Python calculations was elegantly displayed on the web interface using HTML
tables. This user-friendly presentation allowed basketball enthusiasts and
analysts alike to explore and dissect the aTS% data visually. The next step in
development is implementing data from other seasons, rather than just the
2022-23 league year. In essence, the development of aTS% showcases the power
of technology and data-driven insights in modern sports analysis.
ABOUT ME
I am a dedicated student enrolled at the University of Washington, pursuing a
degree in Electrical and Computer Engineering as a proud member of the Class
of 2026. While my academic pursuits are centered around engineering, my true
passion lies in the world of basketball. This dynamic intersection of technology
and sports has fueled my enthusiasm for both analytics and computer science. My
journey into the realm of basketball has been a multifaceted one. I've delved
deep into the intricacies of the game, honing my expertise in not only the
conventional facets but also the increasingly influential world of analytics.
As a testament to this passion, I take immense pride in being the originator of
the innovative statistic known as "aTS%," which redefines how we measure player
efficiency by incorporating usage rate. Beyond my academic and statistical
endeavors, I channel my basketball fervor into "HoopsLine," my very own Instagram
page. Here, I combine my insights and creativity through graphic design to share
my unique perspectives and opinions on the world of basketball. Through this
platform, I aim to engage, educate, and inspire fellow basketball enthusiasts,
fostering a community bound by our shared love for the game and its evolving
landscape.