Motivations

Our project could be used by many different groups of people. Whether authority figures in the NFL, MLB, and NBA looking for injury trends in their league in order to implement prevention measures, young kids wanting to know the risk of playing a sport before they choose to do so, or just people interested in sports and injuries in general, our project could be used by many different people of ages. Our data sources were the current injury reports from the NFL, MLB, and NBA. They included the name of the player injured, the position that player plays, and the type of injury. We picked these data sources because we thought they fit very nicely with our original interest in injuries in professional sports. We decided to pick the NBA, MLB, and NFl injury reports, as we thought they were the most different in terms of levels of contact and exertion, and therefore would provide the most clear trends in amount and type of injuries. We chose this topic because we were interested in the potential differences in terms of injuries within different professional sports leagues. We agreed that we had never seen any sort of visualization illustrating the differences in the frequency of injuries within professional sports, let alone the frequency of different types of injuries, and realized that we had an opportunity to do so ourselves.

What was the most challenging aspect of the project?

The most challenging aspect of this project was getting the visualizations to look exactly how we wanted them too. It took a lot of playing around with lists and matplotlib/plotly functions in order to present visualizations that we thought presented our data the best and looked the most organized. This was especially so with our pie charts.

What was the most rewarding aspect of the project?

The most rewarding aspect of this project was probably during the initial data processing phase. Once we were able to process our data from our csv files and convert them into our intended meaningful lists, we knew we had a very solid basis for our future visualizations moving forward, which felt very rewarding.