Basketball

Intentionally fouling a team during their offensive possession is a strategy that basketball teams have employed for many years. Though popularized in the National Basketball Association (NBA), the technique has also made its way into Collegiate Basketball in recent years. The purpose of intentionally fouling is often to slow down opponent scoring and expose poor free-throw (FT) shooters. Despite previous studies using Markov Chains or analyzing intentional fouling in the NBA, there is a lack of research combining these methods with collegiate men’s basketball rules, particularly the one-and-one bonus.…Read More

Injuries in the NBA have become consequential not only for team success but for the financial costs those teams suffer. This study develops a machine learning framework that predicts next-game injury risk using publicly available box-score data, player attributes, and injury history, then translates these probabilities into expected financial costs. Combining five datasets from 2010-2022, I derived sixteen workload and recency features and trained a Random Forest model optimized with five-fold cross-validation. At a 2% threshold for classification, the model predicts out-of-sample 69% of injuries while correctly ruling out 62% of healthy games, indicating better-than-chance predictive power is possible using solely public data. Feature-importance analysis identified workload shifts and rest as primary predictors.…Read More

Within college basketball, the decision to foul or not with a 3-point lead (the “foul up 3” dilemma) significantly impacts teams. A single fouling decision can directly determine the outcome of a game or even an entire season. Within this research project, I take a novel look at the “foul up 3” dilemma. As opposed to using time as a blocking factor, as previous studies have, my research focuses on generating a coachable strategy that outlines the superior fouling decision for each moment in time.…Read More

NBA teams often trade draft picks. We are interested in the relative value of draft positions, which determines whether a team should accept or reject a trade, or which draft picks it should offer in a trade to make it fair. In this work, we explore various NBA draft value curves. We introduce a novel method, mapping player performance measures (e.g., WAR, RAPTOR, BPM) to salary using Gamma regression in order to constrain draft value curves to be positive. We find that, depending on the measure of performance value and the method of aggregation (e.g., mean or median), draft value curves are wildly different.…Read More

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

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 is prevalent in online NCAA basketball coverage at the league, team, and individual player level. At the league level, I find a significant quantitative difference in female coverage, in the number of articles, game recaps and correct assignment of articles on ESPN.com for the women’s teams and players as compared to male counterparts. Being mentioned in one article is considered an outlier for female athletes. I also uncover that male players who play and score more receive more media attention, but this relationship is less clear for female players.…Read More

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 we model the effectiveness of a lineup as an interacting function of the offensive skill, defensive skill, and player archetype of all ten players on the court. We find that fit is indeed just as essential as skill in crafting a lineup.…Read More