Best Data Viz
Ranking

Bump Chart

Displays how the ranking of items changes over time using curved or straight lines that shift vertically to reflect position changes.

Team Rankings 2019-2022

How competitive positions shifted over four seasons

View data (12 rows)
Chart data table: Team Rankings 2019-2022
yearrankteam
20191Alpha
20192Beta
20193Gamma
20202Alpha
20201Beta
20203Gamma
20213Alpha
20211Beta
20212Gamma
20221Alpha
20223Beta
20222Gamma
Make a bump chart with your data

Use a bump chart when…

  • Tracking rank changes of a small set of items across discrete time periods
  • Comparing competitive standings such as sports league tables or brand rankings
  • Showing how relative positions shift rather than absolute values

Avoid when…

  • When you need to show the magnitude of differences between items
  • When there are more than 10-12 items, as lines become tangled
  • When time intervals are irregular and gaps matter

Data it needs

PropertyValue
Min Rows6
Min Columns3
Column Types
stringnumberdate
NotesRequires a category column, a time/period column, and a rank column.

Visual anatomy

Marks
linecircle
Channels
position-y (rank)color (item)position-x (time)
Axes
x-axis: time periodsy-axis: rank (inverted, 1 at top)

Guiding principles

Consider instead

Common mistakes

  • Including too many items, creating an unreadable spaghetti chart

  • Not labelling start and end ranks clearly

  • Using thin lines that are hard to distinguish

History

Bump charts have circulated in sports infographics since at least the 1970s and were popularized for cycling's Tour de France general-classification tables. They descend from slope graphs and parallel-coordinate ideas, adapted specifically for ordinal rank data.

Accessibility notes

Provide a text table of rankings per period as an alternative. Use distinct colors with sufficient contrast and consider pattern-coded lines for color-blind users.

Related reading

Got data? Let's see what works.

Drop your CSV. You'll get a Bump Chart plus four alternatives - ranked by which one actually fits your data best.