[OC] The Greatest NBA 3Pt Shooting Seasons Since 2013-14 Visualization

April 13, 2026
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By Alex Cartwright
[OC] The Greatest NBA 3Pt Shooting Seasons Since 2013-14 Visualization
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Data Analysis

What This Visualization Shows

This data visualization displays "[OC] The Greatest NBA 3Pt Shooting Seasons Since 2013-14" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on I created a stat that measures overall three point impact based on shot difficulty, accuracy, and volume. The details/source of the stat are [here](https://medium.com/@ballerinsights/introducing-3-point-shooter-rating-the-stat-that-quantifies-elite-3-point-shooting-0ee46541fdbb). The dataset is [here.](https://docs.google.com/spreadsheets/d/1wZnDRl88uPv5OK9_erPU1kJrOMLSno1E8AJGiJw3hVM/copy)

There are 3,250 total qualifying player-seasons in this data set. All 12 of Stephen Curry's seasons are in the top 62. His worst season is in the 99.4th percentile.

Stephen Curry owns the top 4 seasons and 5 of the top 7 seasons. Curry's 2016 season is 40% higher than the best non-Curry performance.

edit 1: In plain English, the 3 Point Shooter Rating (the stat on the y-axis) tells you how many extra points per game a shooter scores from threes compared to what their shot difficulty predicts.

edit 2: Type - The title of the graph should be 3PT Shooter Rating vs. **Actual 3P%** , which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

This data visualization represents a sophisticated analysis of complex information patterns that provide valuable insights into underlying trends and relationships. Data visualization serves as a bridge between raw numerical data and human understanding, transforming abstract statistics into comprehensible visual narratives.

The power of data visualization lies in its ability to reveal patterns, outliers, and correlations that might not be apparent in traditional tabular formats. Through careful selection of chart types, color schemes, and interactive elements, effective visualizations can communicate complex information quickly and accurately to diverse audiences.

Modern data visualization combines statistical analysis with design principles to create compelling visual stories. This interdisciplinary approach requires understanding both the underlying data and the cognitive processes involved in visual perception. The result is more effective communication of quantitative insights that can inform decision-making and drive positive change.

Data Analysis and Insights

The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.

Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.

The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.

Significance and Applications

This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.

For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.

The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.

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About the Author

Alex Cartwright

Alex Cartwright

Senior Data Visualization Expert

Alex Cartwright is a renowned data visualization specialist and infographic designer with over 15 years of experience in...

Infographic DesignData AnalysisVisual Communication
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Visualization Details

Published4/13/2026
CategoryData Analysis
TypeVisualization
Views28