Training Workshops

UCSAS 2024 offers 10 workshops in four tracks on Friday, April 11, 2025

Track A (Introductory Level)
Track B (Intermediate Level)
Track C (Advanced Level)
Track D (Educators)

Introduction to R

Outline

R is an open-source programming language for statistical computing and graphics. R offers a wide range of graphical and statistical tools, including time-series analysis, classification, clustering, and linear and nonlinear modeling. This workshop introduces R to those who have had little to no prior experience. Topics include: 1) an overview of basic R; 2) data structure of R; 3) data management of R; and 4) some useful package of R. A real-life sports dataset will be used to provide a better understanding of R.

Instructor

Lucy Liu Lucy Liu is a senior in Statistics at UConn. TBA

Prerequisites

A laptop with R/RStudio installed; previous experience using R is NOT required; basic programming knowledge would be helpful but NOT required.

Training Materials

On GitHub

Introduction to Python

Outline

TBA

Instructor

Charitath Chugh Charitath Chugh is a senior in Statistics at UConn. TBA

Prerequisites

TBA

Training Materials

On GitHub

Building a performance metric with player tracking data

Outline

Player tracking data offers a great opportunity for creating performance metrics in sports. This workshop will provide a detailed walkthrough of how to build a metric with player tracking data. We will focus on American football and use data provided by the NFL Big Data Bowl competition. The workshop will feature (1) an overview of tracking data and basic visualization and data preprocessing; (2) metric formulation and application to NFL data; and (3) metric validation and statistical properties of sports metrics.

Instructor

Quang Nguyen Quang Nguyen is a third-year PhD student in the Department of Statistics & Data Science at Carnegie Mellon University. His current research focuses on statistical analysis of complex data such as player tracking data in sports and network data. Quang previously received his MS in Applied Statistics from Loyola University Chicago and BS in Mathematics and Data Science from Wittenberg University in Springfield, Ohio. He is a two-time NFL Big Data Bowl finalist, and a die-hard supporter of Manchester United.

Prerequisites

Familiarity with R and basic data science tasks (data wrangling & data visualization)

Training Materials

On GitHub

Uncovering Causal Relationships in Sports Analytics: Methods and Applications

Outline

TBA

Instructor

Shinpei Nakamura Sakai Shinpei Nakamura Sakai Shinpei Nakamura-Sakai is a Ph.D. candidate in Statistics and Data Science at Yale University. His research in sports analytics introduces a framework for analyzing age curves to examine how factors like rest days impact athlete performance across career stages. Shinpei has industry experience as a quantitative analytics associate at JPMorgan Chase and an applied scientist at Amazon, and he has won Best Poster Awards at both UCSAS 2022 and NESSIS 2023.

Prerequisites

TBA

Training Materials

On GitHub

How to Create a Module for SCORE

Outline

We conduct a workshop for educators to gain exposure to materials generated by the SCORE Network and to inspire them to use these educational materials with their students.  The workshop will consist of an introduction to the SCORE Network, an investigation into pedagogical materials available including specific modules, a discussion of how to utilize these materials with students and a period of brainstorming about potential modules.  This workshop will be led by senior personnel from the SCORE Network and attendees will be asked to bring a laptop or tablet for the workshop.

Instructor

Rachel Gidaro Dr. Rachel Gidaro is an Assistant Professor in the Department of Mathematical Sciences at the United States Military Academy, West Point, New York. She earned her Bachelors of Science in Mathematics in 2019 from Colorado Mesa University. Beginning in 2019, she attended Baylor University and earned a Master of Science in Statistics in 2020 before completing her Doctor of Philosophy in Statistics in 2024. Her research interests in statistics are focused on discrete variate time series analysis. In her free time, Rachel enjoys reading, exercising, and working with the cheer team, the Rabble Rousers, at USMA.

Prerequisites

TBA

Training Materials

On GitHub

Introduction to Visualizationd

Outline

TBA

Instructor

TBA

Prerequisites

TBA

Training Materials

On GitHub