Using R to analyze pro motorcycle racing
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EMC recently ran a competion to find out why John McGuinness, the legendary motorcycle racer known as the “Morecambe Missile”, is outperforms the average motorcycle racer. To answer this question, EMC instrumented his bike and his suit with a number of real-time sensors. (Data collected included gear and RPM for the bike, and heart rate and acceleration for the rider.) They did the same for Adam Child, a motorcycle racing journalist who acted as a “control” in this study. The two motorcyclists then completed 10 laps of Circuit Monteblanco in Spain, and the sensor data was provided for analysis in a CrowdAnalytix competition. (Sadly, the data themselves are proprietary and not available for others to analyze.) Prizes were awarded for the best model and for the best data visualization, and both winners used R.
In the “best model” competition, winner Stefan Jol (a revenue management Analyst at a leading UK radio group) used a random forest to determine that bike position and rider position were the primary determinants of speed (and race performance). His analysis appeared to divide the track into segments, shown in the map below.
In the visualization competition, winner Charlotte Wickham (Assistant Professor in the Department of Statistics at Oregon State University) also divided the track into segments, with an interactive data visulization to compare the pro racer and journalist in each segment.
As this demonstration video shows, Charlotte's app allows you to select one or more segments from the racetrack, and compare summary statistics and even the racing line used by each rider. EMC noted Charlotte's use of R in this quote:
“Professor Wickham built a very unique and insightful application (using the R statistical programming language) which allowed users to look at sections of the course and compare John and Chad across a number of different variables. R is a powerful language that we use at EMC for predictive analytics, and her use of R demonstrates the versatility of open source analytical programs.”
You can learn more about the winning submissions at the link below, by scrolling down to the “The Analysis” section.
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