Wharton Sports Analytics Journal

Open Access · ISSN 3070-4065

From the Editor • Fall 2023

Krish Shah, W’24 SEAS’24
Former Editor-in-Chief

Dear Friends and Readers,

Earlier this year we shared plans to change the direction and vision of our publication. We’ve spent the past few months working to update our submission guidelines, standards and peer review process to deliver on our mission to be the premier destination for student research in the sports analytics community.

I’m excited to share our next step towards that goal, the Fall 2023 edition of the Wharton Sports Analytics Journal, featuring projects from students here at the University of Pennsylvania as well as universities and high schools across the country.

This edition features work in major US sports, including Algorithmic Player Acquisition in the NBA, but also research into volleyball, F1 racing and bias in sports reporting. We feel this wide range of topics, methodologies and backgrounds sets us apart from other journals in this space.

As we look forward, I’d invite any high schooler, undergraduate or graduate student doing research in sports analytics to submit to the journal – we are already accepting submissions for our Spring 2024 edition.

We hope you enjoy reading and learning from these exceptional papers as much as we enjoyed reviewing them. Please let us know your thoughts and feedback as we strive to bring you the best of sports analytics and statistics.

Sincerely,

Krish Shah, W’24 SEAS’24
Editor-in-Chief

Fall 2023

Optimal Rest Days for Pitchers: Maximizing Performance and Wins

Optimal Rest Days for Pitchers: Maximizing Performance and Wins, by Blake Zilberman, University of Pennsylvania (Moneyball Academy), Philip Sherr, University of Pennsylvania (Moneyball Academy), Lyev Pitram, University of Pennsylvania (Moneyball Academy), and Marc Sutton, University of Pennsylvania (Moneyball Academy) Read More

Serving to Win: A Statistical Exploration of Optimal Serves in Beach Volleyball

Serving is the only part of a game of volleyball that is the same every time. Despite this, serving strategy and philosophy vary greatly. Teams who use the analytically optimal serve the most can gain a distinct advantage. In identifying the optimal serve, conclusions about best serve type, best serve location, and relationships between errors ...Read More

Sidelined: Using Natural Language Processing to Investigate Gender Bias in Basketball Sports Journalism

In this journal article, I investigate gender bias in sports journalism, focusing on ESPN.com coverage of National Collegiate Athletic Association (NCAA) basketball. With the programming language R and using natural language processing (NLP) techniques, I analyze more than 1,700 articles on ESPN.com to create an R Shiny application Sidelined. This app reveals that gender bias ...Read More

Algorithmic NBA Player Acquisition

Player acquisition is one of the fundamental problems of basketball analytics. An analyst may be tempted to recommend simply acquiring the best available player, where best is defined by an all-encompassing skill metric. How a player fits with his teammates, however, is also important in determining the effectiveness of a lineup. Thus in this paper ...Read More

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