NBA player statistics from the 2024–2025 season can be used to show how data visualizations can spread disinformation (intentional) or misinformation (unintentional). Explore each technique below and think critically about what you see.
One classic way to mislead with data visualizations is by adjusting the y-axis to make differences appear larger (or smaller). Compare the points per game for three players using two different y-axis scales — the charts use identical data.
How to read these charts: Hover over any bar to see the exact value. Notice how the left chart (truncated axis) makes the gap between players look dramatic, while the right chart (zero-based axis) shows the true proportion.
A y-axis can also be flipped to imply that an increase is actually a decrease (or vice versa). The chart below seems to show that players who make more field goals score fewer points — which is obviously wrong.
How to read this chart: Hover over points to see each player's stats. Look at the direction of the y-axis labels — they decrease as you go up.
By pulling a slice away from the center of a pie chart, you can make the viewer perceive it as larger or more important than it really is. The two charts below contain identical data — only the styling has changed.
How to read these charts: Click a player's name in the legend to hide or show their slice. Notice how the pulled slice in the right chart draws your eye even though no numbers have changed.
Sometimes two unrelated things appear to be correlated in a dataset purely by chance. The chart below shows a relationship between the total number of characters in all player names on a team and the average games played per player — obviously one does not cause the other.
How to read this chart: Hover over each point to see which team it represents. The trendline looks convincing, but correlation does not mean causation.
Occasionally a visualization contains intentional or unintentional errors. In the two charts below, one shows the Indiana Pacers' scoring correctly, while the other has an inflated bar — but the label above that bar still shows the original (correct) value.
How to read these charts: Compare the bar heights to the numbers printed above them. Can you spot which player's bar does not match its label?