Uncovering Hidden Trends in NBA Data

In the world of data visualization, uncovering hidden patterns and trends can often feel like finding a needle in a haystack. That’s where jitter plots come in! In this post, I’ll explore how jitter plots can be a game-changer when it comes to analyzing NBA player performance data over the last 50 seasons.

What’s a Jitter Plot?

A jitter plot is a variation of a scatter plot designed to make it easier to visualize data points that might otherwise overlap. By adding a slight random variation (or “jitter”) to the position of the data points, jitter plots help reveal clusters, trends, and outliers that could be hidden in a traditional scatter plot.

Why Jitter Plots Over Scatter Plots?

While scatter plots are great for visualizing relationships between two variables, they can become messy when the data points are too close together, leading to overlap. Jitter plots solve this by spreading the points out just enough to make them distinguishable. This can be particularly useful when you’re working with large datasets, like NBA performance stats.

In my upcoming video, I’ll show you how jitter plots in Tableau can be used to uncover unique insights into player performance over the decades.

Using Tableau’s Story Mode to Uncover Insights

One of the key features I’ll be highlighting is Tableau’s Story Mode Dashboard Template. This allows you to guide your audience through a data-driven narrative, moving seamlessly from one insight to the next. By using jitter plots and Tableau’s interactive tools, you can make complex datasets feel intuitive and engaging.

View below for the full video where I’ll walk you through these concepts in action.