Buckets & Dimes: How NBA Data Reveals the Game Within the Game

When I first picked up a basketball, I was captivated by how a single player could shift the momentum of an entire game. Years later, that same fascination led me to data. This project, Buckets and Dimes, explores NBA player performance using Tableau to uncover deeper patterns in scoring and playmaking. Through data visualization, I gained new insight into how roles vary across teams and positions.

Why This Project:

The idea for this project started simply. I wanted to improve my SQL skills, and as a basketball fan, I found NBA data full of potential stories. I became curious about what player and team statistics might reveal about how games are really won and lost. This project was more than just querying numbers. It was about uncovering meaningful insights from the flow of the game. I aimed to address a significant business question:

What patterns in scoring, shot selection, and possession reveal the true drivers of winning in the NBA?

What You Will Gain:

By reading this article, you will gain a clearer understanding of standout NBA performers, the distribution of team contributions, and how various positions impact the game differently. You will also see how data visualization helps bring these patterns to life in ways that are both engaging and informative.

Key Takeaways:

• Trae Young leads in assists, playing a critical role in his team’s offensive coordination.

• Nikola Jokić, a center, demonstrates elite scoring and assisting capabilities, challenging traditional positional expectations.

• Some teams rely heavily on one or two players for production, while others have a more evenly distributed approach.

• Point Guards lead in assists, while Shooting Guards and Small Forwards often rank highest in three-point efficiency.

Dataset Details:

The dataset was sourced from Basketball Reference and includes data on active NBA players from all 30 teams. It contains structured tabular data featuring player names, teams, positions, total points, assists, field goal and three-point shooting percentages, and other advanced stats. This comprehensive dataset provided a strong foundation for exploring performance trends.

Analysis Process:

My process included data preparation, cleaning, and building visualizations in Tableau. One of the most revealing charts was a bubble scatterplot comparing scoring and assists. Players like Nikola Jokić stood out in both areas, offering clear evidence of their multidimensional impact on the game.

Visuals and Insights:

Bubble Scatter Plot (PTS vs AST): Highlights player performance in scoring and assists. Trae Young and Nikola Jokić emerged as clear outliers, showing their exceptional dual impact.

Full Stacked Bar (Team-Level Contribution):   Visualizes each player’s scoring contribution to their team. This helps identify whether teams operate with shared scoring responsibilities or rely heavily on a few players.

 Stacked Bar (Team-Level Points Contribution): Shows how team scoring is distributed across individual players. Some teams exhibit balance, while others depend on one or two stars.

 Treemap (AST by Position): roups assists by position. Point Guards lead significantly, though players like LeBron James and Jokić contribute at levels far above the average for their roles.

Heatmap (3P Efficiency by Position and Team): Compares three-point shooting percentages. Shooting Guards and Small Forwards consistently show higher efficiency, while Centers trail behind with a few exceptions.

Main Takeaways:

Basketball performance is shaped not only by talent, but by how teams structure their roles and distribute responsibilities. This project revealed just how much variation exists across the league in both scoring and playmaking. Players like Jokić show that positions are evolving, and that versatility is a growing asset in modern basketball.

Conclusion and Personal Reflections:

This project showed me how data can uncover stories that are not immediately visible in traditional box scores. The biggest challenge was translating statistical insights into visuals that tell a compelling narrative. Working through that challenge gave me a deeper appreciation for both basketball and data analysis, and it has inspired me to continue exploring this intersection.

Call to Action:

If you are interested in data storytelling, basketball analytics, or are seeking to hire a data analyst with a strong focus on insight-driven visuals, I would love to connect on LinkedIn. You can also view the project on Tableau Public and share your thoughts.