The Heartbeat of Capoeira – Visualizing a Capoeira Game Offers New Insights into the Sport

I have been practicing Capoeira, a Brazilian Martial Art, for almost five years now and I was attracted to this beautiful sport from the first time I saw it. A game of Capoeira has so many components to it – music, singing and clapping, kicks, acrobatics, tricks – that it is almost impossible to analyze it the way one could do with a game of soccer, basketball or baseball. However, a deeper analysis could benefit the individual Capoeiristas in identifying their strengths and weaknesses and, eventually, help improving their game. Because Capoeira is not a competitive sport (at least not in most groups), there is no focus on examination and in-depth analysis of the games.

Capoeira is a Brazilian Martial Art, which consists of a basic step called ginga, different kicks (chutes), defense movements (esquivas), and acrobatic movements, called floreios. A game of Capoeira (jogo de capoeira) consists of two people “playing” capoeira in a circle, called the roda de capoeira. Capoeira games are beautiful to look at as the movements flow into each other while the players move within the circle, accompanied by music, singing and clapping.

I extracted data from a Capoeira game and visualized it in a way that shows hidden patterns and dynamics between the players in order to make statements about players’ interactions, dominance and style of game. With this, I want to approach data from a standpoint of data humanism – making data personal and meaningful. I believe that

“data visualization designers can make their interpretations more personal by spending time with any type of data. This is the only way we can unlock its profound nature and shed light on its real meaning” (Lupi, 2017).

Methodology

In order to create visualizations that can tell a story about a game of Capoeira, I took a video of a game between two high-level Capoeiristas, Professor Gugu from Brazil and Professor Fogo from Germany, who visited the Capoeira event of my group Capoeira Camará in Cork in September 2018. I analyzed the video for the types of movements mentioned above – ginga, esquivas, chutes, floreios – and recorded them with the help of Adobe Illustrator as a type of connected scatterplot. The graph shows four dimensions of the game: the time, the distance of the players from the middle of the roda, the individual movements of the players, and a curve that traces the movements to show their connectedness. As I created the graph from scratch and without the help of an existing plotting tool, I paid special attention to the efficiency and data-ink ratio of the chart (see Tufte, 2001). I decided to give the recorded movements a hand-drawn appearance to emphasize the fact that they were manually recorded and are all individual.

From the chart, I was able to extract data and create a CSV file with the number of each movement for each player and the amount of movements each player executed on each side, as well as the different movements per players per minute. Movements that touched the middle line were not counted into the movements for each side and instead recorded as a separate category. Finally, I created simple charts of the data set with the tools of datawrapper.de to visualize my findings, which I then integrated into the Adobe Illustrator file to create one consistent visualization. For additional interactive visualizations I used flourish.studio.

Two Different Styles of Capoeira

Comparing the games of Fogo and Gugu, it become apparent that they have very different styles of playing Capoeira. While Fogo moves in and out of the middle of the circle a lot and alternates sides, Gugu stays closer to the inside of the roda and focuses on one side.

Movements Fogo only.
Movements Gugu only.
Finished visualization with movements and curves of both players as well as bar and donut chart.

The line graph connecting the players’ movements shows the interconnectedness of their game and the constant in and out – close and far – between them. The visualization emphasizes the organic and dynamic nature of a game of Capoeira and its resemblance to the rhythm strip of an EKG.

The bar and donut charts of the individual movements per player give insights into their dominance and choice of movements. It becomes evident that Gugu was clearly the more dominant player, with more kicks (59%) and slightly more acrobatic movements (52%), while Fogo was more defensive (66% defense movements), but rested less during the game (42% basic step). It is interesting to see that it seems like the players had preferred sides of the roda, Fogo staying more on the right and Gugu more on the left side, which coincide with the sides they started the game on. To see if this is a pattern of Capoeira games in general, it would be necessary to analyze more games. The fact that the game finished with Gugu pushing Fogo into the right side of the roda and Fogo calling the end of the game (marked with an X) supports the claim that Gugu can be considered the more dominant player of this jogo de capoeira.

These charts give an overview of the game as a whole, but in order to make assumptions about the style of the players, one needs to delve deeper into the game. The following charts show the percentage of movements per minute of the game:

It becomes evident that while Gugu distributed his ginga – the basic step that creates time for some rest during the game – quite evenly throughout the game, Fogo, who started the game very active with little rest, got more tired towards the end and ended the game with no acrobatics in the last 30 seconds. While Fogo used most his energy to do acrobatics in the first minute, Gugu balanced his acrobatics with ginga and was thus able to execute floreios throughout the game with most acrobatics done towards the end. Furthermore, the graphs support the claim above that Gugu was the more dominant player with more kicks throughout the game, forcing Fogo to defend himself. It is of particular interest that in the last minute, Fogo used his remaining energy to attack, not even bothering about acrobatics. The graphs show the player’s individual styles and tactics of their game. It also hints at how experienced and advanced they are and how they interact in the roda.

Conclusions

The visualizations I created allow for an in-depth analysis of a Capoeira game. It enables the viewer to gain insights that could not be observed during the game itself by removing “noise” that distracts the senses while watching the game (music, other people, expressions and non-verbal communication between players). However, by removing the game from its context, integral parts of the culture of Capoeira and the relationship of the players are lost. Juggling the loss of important contextual information while making the visualization effective and efficient is the struggle of all data visualization. The data was recorded without the help of a specific software or calculation, which inevitably evokes inaccuracies, especially in terms of the exact time of the movements and the distance. Furthermore, as the movements flow into one another during the game, it is not always possible to distinguish them. Although most movements have an individual name and can be categorized, the game is not choreographed and thus players improvise, stop short of a movement, are caught by surprise. Additionally, the categories used for this visualization and the decisions about the categorization of the movements were my subjective choice and could be debated.

This visualization is an attempt to use data in order to tell personal stories, to

“connect numbers to what they really stand for: knowledge, behaviors, people” (Lupi, 2017b).

However, it gives a mere glimpse into what is possible in relation to visualizing Capoeira. Visualization of this kind would hugely benefit from more interactivity which could help highlight the individual movements and the players in all charts at the same time. It was an exciting project to work on and I hope to be able to do more work of this kind in order to gain insights into my favorite sport.


References

Lupi, G., 2017a. Data Humanism, the Revolution will be Visualized. Medium. Available at: <https://medium.com/@giorgialupi/data-humanism-the-revolution-will-be-visualized-31486a30dbfb#.ikys4iube> [Accessed 18 Feb. 2019].

Lupi, G., 2017b. We’ve Reached Peak Infographics. Are You Ready For What Comes Next? [Online Magazine] PrintMag. Available at: <http://www.printmag.com/information-design/data-humanism-future-of-data-visualization/>.

Tufte, E.R., 2001. The Visual Display of Quantitative Information. 2. ed ed. Cheshire, Conn: Graphics Press.

Prof. Gugu Quilombola vs Prof. Fogo Capoeira Cork 2018. 2018. Capoeira Cork. Available at: <https://www.youtube.com/watch?v=vkhmghTcsjA> [Accessed 8 Feb. 2019].

Tools used

datawrapper.de

flourish.studio

Adobe Illustrator CC

Dataset: Gugu vs Fogo


Download the full essay here.

(Copyright Charlotte Krause)

 

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