BMI 625 Final Project

Jelle’s Marble Runs and Marbula One

  • Marble racing on a track, with commentary
  • In the style of Formula 1 motorsport
  • Two-day competition:
    • Day 1: qualifying with marbles one at a time to determine order for Day 2
    • Day 2: the race (or “grand prix”)

Demo

Jelle’s Marble Runs received sponsorship from Last Week Tonight with John Oliver during the Covid-19 pandemic.

The Data

Sources

First six rows of the merged data set
Date Race ID Track Turns Marble Name Team Name Lap Time (ms)
15-Feb-20 S1Q1 Savage Speedway 13 Clementin O'rangers 28.11
15-Feb-20 S1Q1 Savage Speedway 13 Starry Team Galactic 28.37
15-Feb-20 S1Q1 Savage Speedway 13 Momo Team Momo 28.40
15-Feb-20 S1Q1 Savage Speedway 13 Yellow Mellow Yellow 28.70
15-Feb-20 S1Q1 Savage Speedway 13 Snowy Snowballs 28.71
15-Feb-20 S1Q1 Savage Speedway 13 Razzy Raspberry Racers 28.72

Audience

  • Anyone interested in marble racing or Jelle’s YouTube channel
    • i.e. kids and amused adults
  • Some curious physicists?
  • Fans of Formula 1 who need some kind of an adrenaline fix

Graph Type

  • Raincloud plot - hybrid of box plots, violin plots, and jittered points/dot plots/scatter plots
    • Combination resembles a raincloud and gives the chart its name
  • Enhances traditional box plots through multiple modalities:
    • Conveys distribution shape
    • Measures of central tendency
    • Preserves raw data information
  • Considered a transparent visualization

Representation Description

  • Start with the question: what is the relationship between the number of turns on a track and a marble’s lap time?
  • This question evolved to: how is the distribution of lap times related to the number of turns in a track?
  • Two ways to do this:
    1. Use qualification laps for each marble because there is no interference from other marbles.
    2. Use race lap time because more turns might cause more collisions with other marbles, and thus be more realistic.
  • For this visualization I used the qualification lap data.

Methods and Composition

Start with a scatter plot of number of turns vs. lap time.

A distribution may be more interesting than a line of points. Also, some of the race tracks have the same number of turns. I will encode that information with boxplots and color.

Want to also see distribution shape. The number of turns is not continuous, so treat as a discrete ordered set of categories and adjust the x axis.

I am also interested in the points themselves, so convert this to a raincloud plot.

Where is the individual champion of the first season of Marbula One?

Finishing touches on the graphic to make the color scheme colorblind-friendly and use fonts that evoke a racing theme.

How to Read

Raincloud plots combine:

  • A ‘split-half violin’
  • Visualization of central tendency and error (i.e. a boxplot)
  • Individual data points.

Strengths: inference at a glance, statistically robust, data transparency

Next Steps

  • Make the same graph with race data
  • Make paired raincloud plots that compare race lap times with qualifying lap times
  • Add interactivity so audience can look at individual cars and mouse over statistics

Citations and Resources

  1. Raincloud Plots Paper
  2. Raincloud Plots Blog
  3. Marbula One Open Data Repository
  4. Dr. Randal Olson’s Blog
  5. ggdist R package