NBA Player Performance Analytics Dashboard

Compare NBA players with data-driven insights to identify top performers and high-value players.

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Inspiration

I wanted to build this project because in basketball, people usually look at points first when judging a player. But after looking at the data, I realized that points alone do not show the full value of a player. Some players help their team through assists, rebounds, efficiency, limited turnovers, or by making a strong impact in fewer minutes.

So I decided to create an NBA Player Performance Analytics Dashboard that gives a more complete view of player performance. My goal was to make a dashboard that a coach, analyst, or basketball fan could use to quickly compare players and understand who is creating the most value.

What it does

This dashboard helps users analyze NBA players using different performance views.

It shows the top overall impact players, hidden-gem players who perform well in limited minutes, efficient scorers, and players with strong playmaking decision quality. I also added a coach recommendation table so users can compare players side by side using multiple metrics.

The dashboard helps answer questions like:

Who has the highest overall impact? Who is scoring efficiently? Who is creating value even with fewer minutes? Who is making good playmaking decisions with assists and turnovers?

How we built it

I built the project using an NBA player statistics CSV dataset for the 2025–2026 season. First, I cleaned the dataset and removed rows that were not needed, such as team total rows. Then I focused on player-level stats that were useful for comparing performance.

After that, I created new metrics to make the analysis stronger. These included Impact Score, Impact Per Minute, Scoring Efficiency Score, and Playmaking Ratio. These metrics helped me compare players in a better way instead of only using points.

Then I used Tableau Public to build the dashboard. I created different sheets for top impact players, hidden gems, efficient scorers, playmaking quality, and the final recommendation table. After building each sheet, I combined everything into one dashboard so the full story could be seen in one place.

Challenges we ran into

One challenge was deciding which stats should matter the most. At first, it was easy to think that points were the most important metric, but that would not be fair to players who contribute in other ways. Because of that, I used multiple metrics to show different types of player value.

Another challenge was making the dashboard clean and easy to understand. There were many columns in the data, and if I added too many charts or labels, the dashboard became crowded. I had to adjust filters, labels, chart sizes, and layout so the final dashboard looked organized.

I also had to test different Tableau filters and calculated fields to make sure the results were showing correctly.

Accomplishments that we're proud of

I am proud that I was able to turn raw NBA statistics into a complete dashboard with useful insights. The project does not only show star players like Nikola Jokić, Luka Dončić, and Shai Gilgeous-Alexander. It also finds hidden-gem players like Ty Jerome and Obi Toppin, who create strong value in limited minutes.

I am also proud of the coach recommendation table because it brings the main metrics together in one place. This makes the dashboard feel more useful for real decision-making instead of just being a set of charts.

What we learned

I learned that sports analytics is not only about showing data, but also about asking the right questions. A good player comparison should include more than one statistic. Scoring, efficiency, playmaking, minutes, and turnovers all help tell a better story.

I also learned more about Tableau, especially how to build bar charts, scatter plots, filters, calculated fields, and a final dashboard layout. This project helped me practice data cleaning, visualization, and storytelling with data.

Most importantly, I learned how to take raw sports data and turn it into something that is easier to understand and useful for decision-making.

What's next for NBA Player Performance Analytics Dashboard

In the future, I would like to improve this dashboard by adding more advanced basketball metrics, such as player efficiency rating, usage rate, win contribution, and team impact.

I would also like to add more filters for team, position, and season so users can explore the data in more detail. Another future improvement would be adding predictive analytics to forecast player performance and identify rising players before they become stars.

Built With

  • calculated-fields
  • csv-dataset
  • dashboard-design
  • data-cleaning
  • data-visualization
  • excel
  • github
  • sports-analytics
  • tableau-public
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