A common method that coaches tell their players in basketball is to visualize their shot going into the hoop before the action takes place. This is a trick that can help a shooter’s confidence. But are their other ways that sports data can be “visualized” for human use?
This week’s reading by Lev Manovich analyzed the usefulness of data visualization. Manovich provides a rough definition of infovis “as a mapping between discrete data and a visual representation.” The question that arises out of this definition is how accurately does a visual embody the raw data? The answer varies. Manovich mentions data reduction as well as the use of special variables as two main characteristics of infovis. These methods effectively smooth the data in order to represent key aspects to the viewers.
Although useful, I think that these two characteristics of visual analysis can have a tendency to digress from a dataset’s overall meaning. They do offer the viewer an opportunity to make sense of the datapoints, but by omitting aspects, the individualized data could lead to different results. For example, a graph can show a player’s field goal percentage in several games, but the analysis will leave out variables such as whether the shot was contested, minutes played, and the location of the shot.
A new system of NBA tracking has been implemented that tracks the movement of players 25 times per second. This new form of data allows for far more advanced statistics concerning touches, rebound opportunities, drives, and catch and shoots. The system also allows viewers to see video as well as movement animations to form a more complete level of analysis. Manovich would characterize this type of data as “direct visualization.” In this fashion, the NBA tracking system uses actual images and video to make a new depiction of the data. Will this new way of visualization change the way players and coaches think about stats? No way to tell now, but I encourage you to check out the site.
The NBA’s new player tracking system is amazing, and it highlights the flipside of maps and data visualizations, which is the collection of data that goes into the graphic representations. In general, sports analytics is one arena in which big data is making a big impact. More locally, even NASCAR teams employ mathematicians and data analysts to hone and refine everything from the car itself to driving practices.