This was a submitted project for a course I took at City, University of London for my Data Visualization Course
I have included my datasets, my markdown documents submitted, and some screenshots.
The visualizations I chose for the implementation of solving my first research question were helpful when put together to get a better understanding of a race performance for a typical lightweight male sculler. I think it’s important to note that there are a lot of factors that influence the total race duration for a world champion in any given year. These factors include, but are not limited to: race timing, air temperature, UV index, water temperature, conditions and currents, humidity, weather conditions, wind speeds and directions, and recovery time in between progression races. My first visualization, the scatter plot, showed that although there is a general average best time, which from research on previous years racing conditions, indicate the average speed required to win a world championship is somewhere around the 6:52-6:59 range. I think the important things to note are that most elite race courses are constructed in a way to minimize the impact of external factors. As a result, almost all race courses are on protected lakes that are protected from wind and run in the same direction of the prevailing winds of the area. This results in a faster race time as winds are pushing competitors boats, allowing them to achieve higher speeds and utilize higher stroke rates.
These factors are helped by faceted bar chart visualization. This facet shows the race profile of each year’s World Rowing Championship lightweight single sculls winner. I think the facet was the most effective way to analyze this info as conditions vary by year and so races can’t be directly compared to each other. I couldn’t imagine a better way to put all 10 years worth of race data next to each other and make accurate comparisons. Each individual chart is zoomable and scalable so although there is a predetermined zoom ranger that isn’t perfect from every year, it is adjustable to help get a better picture about race performance for lightweight men. The visualization helps to show how in reality the race plan of a lightweight man is a bluff: racers go off the line at significantly higher speeds and stroke rates than they are able to maintain for the race. While a more even race plan (see 2016) is arguably more efficient, there comes a significant mental advantage of racing from the front instead of behind, especially when the nature of your sport has you face behind you. Winners get to have the added information of where opponents are, while losers only see what is behind them, unless they want to sacrifice their speed to turn around to check. Usually, races involve a sprint at the start that utilizes rate and power, a slower middle section which is more of a “maintain” phase, and a sprint at the end, which mostly utilizes only power. Rates don’t go up as much at the end due to the exhaustion of racers. This excludes the Kiwi racer in 2016 who did not front end focus his race and instead started more modestly and closer to his target race speed which effectively helped conserve more energy for his final sprint, where he found his fastest speeds of the whole race.
Concluding, these visualizations helped to show that although rowing technology, training methodologies, scientific information, etc has improved and evolved in the past decade significantly, the race strategy to find World Champion-level success remains the same: A superhuman effort off the first 100 meters, a gradual shift into racing pace, and then a final sprint starting around the 1750 meter mark.
For my second research question, I wanted to analyze the existing debate for the value of lightweight rowing, being that lightweight rowing allows for poorer nations with traditionally smaller people to have a fair chance at competing at the highest level of rowing. Rowing is unique in that it is the only non-combat sport in the Olympics to utilize weights classes (however, this is quickly changing: Rio 2016 was the last year for the lightweight men’s coxless four, and the now postponed Tokyo 2020 was rumored to be the last year for lightweight rowing at the Olympics in general). I wanted to analyze the difference between the difference in countries entering world championships in lightweight and heavyweight events.
I used an interactive map to color the countries that entered racing each year. A great example of comparison is 2016. There is a lightweight/heavyweight toggle button and a slider to view different years. I found through this visualization that actually the opposite is true. Most successful lightweight athletes come from the richest countries, while the heavyweight event had much more geographic diversity, especially in Olympic years. While rowing is a predominantly European sport with significant interest from North America as well, the heavyweight event saw more competitors from Asia, Africa, and South American then the lightweight event could produce.
Finally, I produced a simple line graph showing the difference between entries of lightweight against heavyweight single scullers at World Championship regattas. Every year, there were more heavyweight men than lightweights. So my visualizations found that not only are there more countries interested in racing the heavyweight event, but also those countries are located in more continents.