Information from Data - Stephen Curry Shot Analytics

Data Sub-Genre

Sports analytics - Basketball player shot analytics

Raw Data on Which It Is Based

Figure 1 - Season-by-season shooting splits by distance.

The raw data for this analysis consists of season-by-season shooting statistics for Stephen Curry across his NBA career. This dataset includes field goal attempts and percentages by shot distance, distribution of shots across distance ranges, games played, minutes, and assisted shot frequency.

Because this information is presented as a large numerical table, it is difficult for a viewer to immediately identify patterns such as shot preference, efficiency trends, or changes over time. Relationships between shot distance, volume, and efficiency are not obvious without visualization.

This is why visualization is essential. It converts dense numerical records into patterns that can be more easily interpreted and compared across seasons.

Examples of Information Published Based on That Data

Figure 2 - Shot chart showing efficiency by court location.

Figure 3 - Zone-based shooting efficiency and shot volume.

The raw shooting data is transformed into information through visual outputs such as shot charts and efficiency maps. These visualizations aggregate thousands of shot attempts and present them spatially and proportionally.

By mapping shot attempts to physical locations on the court, these graphics allow viewers to quickly identify high-usage and high-efficiency areas. Patterns that would require careful comparison of multiple columns of numbers become immediately visible.

In Stephen Curry's case, the visuals clearly show that long-range shots, especially three-pointers, are not only frequent, but also highly efficient compared to league averages.

Insights and Knowledge

• Curry takes a disproportionately high percentage of shots from beyond the arc.
• His three-point efficiency remains above league average despite high volume.
• Shots at the rim and beyond the arc are more efficient than mid-range shots.
• His average shot distance has increased over the course of his career.

The broader knowledge gained from this analysis is that shot value is not determined solely by distance, but by the combination of player skill and shot selection. Curry's ability to maintain elite efficiency from long range challenges traditional assumptions about what constitutes a good shot in basketball.

Potential Action Items

For players and coaches

• Emphasize shot selection that prioritizes high-efficiency zones.
• Design offensive systems that maximize spacing and off-ball movement.
• Use player-specific shot profiles to guide decision making.

For opposing teams

• Extend defensive pressure beyond the three-point line.
• Force shot attempts from less efficient areas.
• Adjust defenses based on shooting range.

Suggestions for Improved Interactive Outputs

• Time-based animations showing how shot selection evolves across seasons.
• Simulations estimating expected points per possession for different shot mixes.
• Real-time analytics dashboards for in-game strategic adjustments.
• Interactive filters for defender distance, shot type, and game context.

Walkthrough - How to Draw Insights

1. Start with Basketball-Reference to examine shooting percentages by distance.
2. Use StatMuse to quickly query season or zone-based shooting stats.
3. Use 3StepsBasket shot charts to visualize efficiency and volume.
4. Compare numerical data with visual patterns to identify high-value shots.
5. Use these insights to guide offensive or defensive strategy decisions.

Web Resources


Basketball-Reference
Stephen Curry Player Page


StatMuse
StatMuse NBA Queries


3StepsBasket
Shot Analytics Visualizations