Case Studies: R in Sport

Seattle is home to lots of bright minds in data analytics. This upcoming meetup we have 2 speakers that also happen to be part of our favorite local NHL Franchise, and great people to learn from. Namita Nandakumar and Dani Chu will be giving some fun presentation on R in Sport!

This meetup will be online and a link will be shared here before the event.

** Note that this not work related to NHL Seattle

Putting the Fun in Functional Data: A tidy pipeline to identify routes in NFL tracking data - Dani Chu
Currently in football many hours are spent watching game film to manually label the routes run on passing plays. Using tracking data, each route can be described as a sequence of spatial-temporal measurements that varies in length depending on the duration of the play. This data can be conveniently analyzed using nested columns in tidyr and purrr. We demonstrate how model-based curve clustering using Bernstein polynomial basis functions (i.e. Bézier curves) fit using the Expectation Maximization algorithm can cluster route trajectories. Each cluster can then be labelled to obtain route names for each route and create route trees for all receivers. The clusters and routes can be visualized nicely using ggplot and seen developing over time using gganimate.

R + Tidyverse in Sports - Namita Nandakumar
This talk will use a case study, most likely in hockey, to showcase the many ways in which R and the Tidyverse can be used to analyze sports data as well as the unique priorities and considerations that are involved in applying statistical tools to sports problems.