Bradley J. Congelio, Ph.D.

Bio

Hi, I am Brad. I am currently an Assistant Professor in the College of Business at Kutztown University of Pennsylvania. My main area of instruction & research is in Sport Data Analytics, as well as teaching my very popular Sport Analytics (SPT 313) course. I am also the author of Introduction to NFL Analytics with R which was published with CRC Press in December of 2023.

My research focuses on using big data, the R programming language, and analytics to explore the impact of professional stadiums on neighboring communities. I use the proprietary Zillow ZTRAX database as well as the U.S. Census and other forms of data to create robust, applied, and useful insight into how best to protect those livings in areas where stadiums are proposed for construction.

As well, my work in sport analytics, specifically the NFL, has been featured on numerous media outlets, including the USA Today and Sports Illustrated.

Examples of Projects

  1. Introduction to NFL Analytics with R (book, CRC Press): It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plan, and commentators, throughout broadcasts, use terms such as air yards, CPOE, and EPA on a casual basis. This rapid growth, combined with an increasing accessibility to NFL data, has helped create a burgeoning amateur analytics movement. However, because learning a coding language can be a difficult enough endeavor, Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to, opting to provide step-by step instructions in a structured, jargon-free manner.
  2. Customer Churn Prediction (ShinyApp): Using ticket purchase data from the LA Kings (provided via the National Sports Analytics Intercollegiate Championship), an XGBoost classification machine learning model is used to predict (with roughly 90% accuracy) whether a ticket-buying customer is likely to return to a second game or not. Click the link above to view the model in its current ShinyApp format.

Education

University of Western Ontario | London, Ontario, Canada

Ph.D. in Socio-Cultural Studies | 2014

The University of California, San Diego | San Diego, California

Specialized Certificate: R for Data Analytics | 2021

Experience

Kutztown University
Tenured Assistant Professor | 2023 - Present
Assistant Professor | 2018 - 2023

Keystone College
Assistant Professor | 2014 - 2018

Recent Publications

  1. Congelio, B. (2023). Introduction to NFL Analytics with R (CRC Press, 2023).

  2. Congelio, B. (2022). “Examining the Impact of New Stadium Construction on Local Property Prices Using Data Analytics and the Zillow ZTRAX Database.” Journal of Business, Economics, and Technology. Spring 2022, 39-55.

  3. Congelio, B. (2021). “Monitoring the Policing of Olympic Host Cities: A Novel Approach Using Data Analytics and the LA2028 Olympic Summer Games.” Journal of Olympic Studies 2(2), 129-145.

Bradley J. Congelio, Ph.D.

Bio

Hi, I am Brad. I am currently an Assistant Professor in the College of Business at Kutztown University of Pennsylvania. My main area of instruction & research is in Sport Data Analytics, as well as teaching my very popular Sport Analytics (SPT 313) course. I am also the author of Introduction to NFL Analytics with R which was published with CRC Press in December of 2023.

My research focuses on using big data, the R programming language, and analytics to explore the impact of professional stadiums on neighboring communities. I use the proprietary Zillow ZTRAX database as well as the U.S. Census and other forms of data to create robust, applied, and useful insight into how best to protect those livings in areas where stadiums are proposed for construction.

As well, my work in sport analytics, specifically the NFL, has been featured on numerous media outlets, including the USA Today and Sports Illustrated.

Examples of Projects

  1. Introduction to NFL Analytics with R (book, CRC Press): It has become difficult to ignore the analytics movement within the NFL. An increasing number of coaches openly integrate advanced numbers into their game plan, and commentators, throughout broadcasts, use terms such as air yards, CPOE, and EPA on a casual basis. This rapid growth, combined with an increasing accessibility to NFL data, has helped create a burgeoning amateur analytics movement. However, because learning a coding language can be a difficult enough endeavor, Introduction to NFL Analytics with R is purposefully written in a more informal format than readers of similar books may be accustomed to, opting to provide step-by step instructions in a structured, jargon-free manner.
  2. Customer Churn Prediction (ShinyApp): Using ticket purchase data from the LA Kings (provided via the National Sports Analytics Intercollegiate Championship), an XGBoost classification machine learning model is used to predict (with roughly 90% accuracy) whether a ticket-buying customer is likely to return to a second game or not. Click the link above to view the model in its current ShinyApp format.

Education

University of Western Ontario | London, Ontario, Canada

Ph.D. in Socio-Cultural Studies | 2014

The University of California, San Diego | San Diego, California

Specialized Certificate: R for Data Analytics | 2021

Experience

Kutztown University
Tenured Assistant Professor | 2023 - Present
Assistant Professor | 2018 - 2023

Keystone College
Assistant Professor | 2014 - 2018

Recent Publications

  1. Congelio, B. (2023). Introduction to NFL Analytics with R (CRC Press, 2023).

  2. Congelio, B. (2022). “Examining the Impact of New Stadium Construction on Local Property Prices Using Data Analytics and the Zillow ZTRAX Database.” Journal of Business, Economics, and Technology. Spring 2022, 39-55.

  3. Congelio, B. (2021). “Monitoring the Policing of Olympic Host Cities: A Novel Approach Using Data Analytics and the LA2028 Olympic Summer Games.” Journal of Olympic Studies 2(2), 129-145.