In a divide and recombine (D&R) paradigm, the Tessera tool suite of packages (https://tessera.io), developed at Pacific Northwest National Laboratory, presents a method for dynamic and flexible exploratory data analysis and visualization. At the front end of Tessera, analysts program in the R programming language, while the back end utilizes a distributed parallel computational environment. Using these tools, we have created an interactive display where users can explore visualizations and statistics on a large dataset from the National Football League (NFL). These visualizations allow any user to interact with the data in meaningful ways, leading to an in depth analysis of the data through general summary statistics as well as insights on fine grain information. In addition, we have incorporated an unsupervised machine learning scheme utilizing an interactive R Shiny application that predicts positional rankings for NFL players. We have showcased these tools using a variety of available data from the NFL in order to make the displays easily interpretable to a wide audience. Our results, fused into an interactive display, illustrate Tessera’s efficient exploratory data analysis capabilities and provide examples of the straightforward programming interface.