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Link to Shorthand preview: https://preview.shorthand.com/FMiyqguPXfyhLVYF/responsive/desktop
As a note, I have spent a lot of my time getting my data wrangled. That is almost entirely done and so the focus of my work for the next week is to work on the story and visual presentation.
I want my story to appeal to people who consider themselves fans of women’s basketball and those who are not familiar with the sport, so I solicited input from both of those groups. The 2 non-basketball fans and 1 basketball fan I interviewed were all graduate students.
My main goals for this round of feedback were to understand if my visualizations made sense to non-basketball fans. I had them look at the visualizations with some verbal information and asked for their feedback and any information they thought might be helpful to better understand the visualizations. For the basketball fans, I wanted to know if there were gaps in the visualizations I created or potential areas for additional analaysis.
For the stacked bar chart, the non-basketball fans suggested adding 0 (no transfer) as a category and consolidating the higher categories into 2+. One suggested changing the total transfers from line chart to a bar chart. One suggested adding a line graph showing just the number of 2+ transfers. The basketball fan suggested adding some examples of players that fit the reasons for transferring as human interest stories. They also suggested adding annotations to the charts to help describe the patterns in the data (i.e. the bump in transfers due to an additional year of eligibility from COVID, etc.). They also suggested adding a visualization that shows the last 5 years of champions and their share of starters who are transfers (included in this update).
My main tasks for part III are:
None
I did not use AI.