Basketball Positioning Neural Network and Kmean Cluster

Inspiration

Growing up as a basketball enthusiast, I was captivated by the brilliance of players from Kobe Bryant to Victor Wembanyama. Their strategic play, creativity, and relentless work ethic inspired me not only as a fan but also as someone eager to analyze the game beyond surface-level statistics. This project was a natural extension of my passion, allowing me to merge my love for basketball with data science and machine learning.

What it does

One of my main motivations was the underrepresentation of certain statistics in NBA analysis. While field goal percentages and three-point shooting efficiency are frequently highlighted, I found that matchup-based statistics—which assess how specific players perform against particular defenders—are often overlooked. This project aims to bridge that gap by providing a data-driven approach to answering questions like:

What is the best offensive strategy for a player against a specific defender? Which defender would be most effective in guarding a particular opponent? By leveraging machine learning and statistical modeling, I sought to offer insights that could enhance strategic decision-making in basketball.

How we built it

Challenges we ran into

One of the biggest challenges was the self-directed nature of the learning process. Without a structured course guiding me, I had to explore machine learning techniques, data processing methods, and visualization tools on my own. Additionally, time constraints made it difficult to implement all the features I envisioned—one night was never enough to experiment with every model or optimization technique I wanted to try.

While this project may not rival high-end machine learning models in competitive data science, I view it as a valuable learning experience that has fueled my curiosity in coding and AI-driven analytics. More than just a technical exercise, it represents a passion project that combines my love for basketball with my growing interest in technology.

Whether or not this project reaches professional-level analytics, it has solidified my enthusiasm for data-driven sports insights. If it inspires others to explore the intersection of sports and machine learning, then I consider it a success.

Accomplishments that we're proud of

We are proud of the fact that we were able to make something as we planned. Our initial plan was to create a model that predicts the most efficient way for a team to play against its opponents. As we were working on it, we were able to bring our vague plan to real life.

What we learned

As a non-CS major with a growing interest in programming, this project pushed me beyond my formal coursework in CS 1301 at Georgia Tech. I had to self-teach many concepts, from data processing to machine learning, that were not covered in my curriculum. Some key areas of learning included:

Machine Learning Basics: Implementing a KMeans clustering model to classify player positions and using linear regression for predictive analysis. Data Standardization & Feature Engineering: Understanding how to normalize and preprocess raw data to improve model performance. Data Visualization: Creating visual models to analyze specific player matchups and classify players based on in-game statistics.

What's next for Basketball Positioning Neural Network and Kmean Cluster

We think that we had short time to come up with specific data parameters to analyze. We were able to come up with few such as match up statistics but we believe that there is way more information that could be represented through the csv given by the hacklytics team. We are going to work on making more sophisticated data that can be applied on analyzing the data, and hopefully, the model could be applied to the data of the NBA from nowadays.

Built With

• colab
• python