On this webpage I will be critiquing an interactive data visualization of the most valuable sports franchises.
Here is a link to the data visualization .
I decided to use both the ACCENT Principles as well as the Connor and Irizarry framework to criqitue this data visualization, as I believe that both of these criterion have merits and lead to important discussions about the subject. The ACCENT Principles seek to evaluate the effectiveness of a visual display for protraying data, whereas the Connor and Irizzary framework seeks to determine if the data visualization is successful in achieving what it wants to achieve.
The goal of this data visualization is to examine relationship between longevity of a sports franchise and their success, as well as the value of each of the sports franchises. I believe that this infographic is very successful in portraying what it sets out to do, as the obejctive of the data visualization is very simple. The sports franchises are graphed on a very simple x-y plot that is very easily read by the user. Furthermore, the user can very easily distinguish the many different sports franchises (based on both color and bubble size), as well as determine the relative value of the sports franchises through the size of the bubbles. This data visualization has a very simple objective, and presents its data very efficiently and succinctly.
Definition: Ability to corectly perceive relations among variables.
I believe that this graphic presents its data in a very clever manner. It is graphing 3 variables against each other, however, choose to only plot this information on a 2D plot of two of the variables, and instead present the 3rd variable as the relative size of the bubble of each sports franchise, where the larger the bubble demonstrates a larger value. This is better than presenting the data on a 3D plot, as this would be more difficult for the user to interpret and maneuver. Furthermore, the use of larger bubbles to demonstrate more value is a very intuitive choice.
Definition: Ability to visually distinguish all the elements of a graph.
The information that this data visualization portrays is extremely clear and distinguishable. The user can very clearly distinguish one bubble from another, as well as one type of sports franchise from the other, due to the color coordination of the bubbles. Also, the use of larger bubbles to demonstrate more value is very intuitive and the use of the legend makes the information demonstrated here very clear.
Definition: Ability to interpret a graph based on similarity to previous graphs.
The data representations in this graph are very consistent, as all the sports franchises are color coded consistently, and the size of the bubbles remains proportional to their value, with the proportionality holding between each of the sports franchises.
Definition: The need for the graph, and the graphical elements.
I believe that this method of data visualization was the best method, as the user can clearly see the relationship between the x and y axis, and interpret how the larger the bubble signalled a larger value. Furthermore, this is a very minimalist and simple data visualization and does not present a lot of information. As a result, it conveys what it wishes to convey very easily and clearly, as it does not introduce unnecessary clutter.
Definition: Ability to determine the true value represented by any graphical element by its magnitude relative to the implicit or explicit scale.
The bubbles in this graph are sized consistently and proportionally, and thus accurately reflect the relative value of the sports franchises.
Despite being very successful in achieving what it sets out to achieve, this data visualization has a very limited level of user interactivity. The only way that the user can interact with the data is that they can hover over a sports franchise's bubble to reveal a little more information about the franchise, and they can choose to exclusively show one franchise. We would like to be able to manipulate the graph in other ways, such as graphing most valuable sports team over time, sources of revenue for each of the franchises, etc. This graphic does not give reasons behind why a certain sports franchise is more valuable that the other. This data visualization generates a lot of additional questions which it cannot answer due to the limited interactivity.