Performance Analysis in the Brazilian Soccer League: Applying Machine Learning Techniques to Team Evaluation, authored by José Vinicius Boaventura Barbeiro, Universidade Tecnológica Federal do Paraná (UTFPR), Londrina, Brazil…Read More
All-Star Based Evaluation of Draft Value Curves Across Major North American Sports Leagues, authored by Jordan Abell, Latin School of Chicago, Felix Soloway-Gilbert, Pilgrim School, Jonathan Pipping-Gamón, University of Pennsylvania, and Abraham J. Wyner, University of Pennsylvania…Read More
A runner’s primary lead off first base creates leverage to steal second but also exposure to pickoffs. We develop a nested sequence of logistic models to estimate (i) pickoff attempts, (ii) pickoff success given an attempt, (iii) steal attempts given no pickoff, and (iv) steal success given an attempt, using 2024 MLB data and Baseball Savant metrics. We map stage probabilities to expected runs via fixed linear weights (+0.20 for a successful steal; -0.45 for caught stealing or picked off) and optimize over lead distance to obtain a context-specific optimal lead L ∗ . Empirically, observed leads are modestly larger than optimal on average (+0.19 ft), with a larger gap on steal attempts (+0.67), consistent with unobserved intent to steal. This framework quantifies the central trade-off – greater leads increase steal success but raise pickoff risk – on a common expected-runs scale and yield actionable, interpretable recommendations within the observed support.…Read More
Research Note Introducing xCTRL: A Probabilistic Approach to Pitch Location Accuracy Authors: Matt Ludwig Ryan S. Brill, Ph.D., Wharton Sports Analytics and Business Initiative Research Team Abraham J. Wyner, Faculty Co-Director, Wharton Sports Analytics and Business Initiative Published: June 10, 2025 Traditional baseball metrics such as WHIP (walks plus hits…Read More
Research Note The Hall of Fame Cut in Major League Baseball Author: Shane T. Jensen, Professor of Statistics and Data Science, The Wharton School Published: January 22, 2025 1. Introduction In this paper, I create a new quantitative standard for the baseball Hall of Fame that is unambiguous while still…Read More
Modern Portfolio Theory aims to optimize risk-adjusted returns by identifying assets and creating portfolios with the highest Sharpe Ratio. Generally, there are two strategic approaches to optimizing risk-adjusted returns: maximizing returns or minimizing volatility. In traditional financial literature, it is generally understood that forecasting the future returns of an asset by using its historical returns as a proxy yields low correlation and limited accuracy. However, forecasting the future volatility of an asset is a much more precise science due to the autocorrelation of its squared returns resulting in volatility clusters. In this paper, I will draw comparisons between the ways returns and volatility are measured in financial markets and the ways they can be applied in baseball analytics. Furthermore, I will provide a framework for hitter evaluation by contextualizing the historical difficulty of predicting financial returns accurately, while capitalizing on the predictive nature of volatility.…Read More
Streakiness and baseball go hand in hand, but accurately measuring streakiness and consistency in sports is difficult. While studying hitting streaks is an old idea, relatively few works have examined streaks for hitters at the pitch outcome granularity, or for pitchers more generally. Furthermore, little is understood about how streaks correlate with more traditional player outcomes.…Read More
Wharton Sports Analytics Journal Open Access · ISSN 3070-4065 2023 MONEYBALL ACADEMY, ROOKIE REVIEW Are "Surprise Teams" in the MLB in 2023 More Surprising? AUTHORS Chad Federico, University of Pennsylvania (Moneyball Academy) Kotaro Nagano, University of Pennsylvania (Moneyball Academy) Alejandro Wick, University of Pennsylvania (Moneyball Academy) ABSTRACT Injuries in the…Read More
Over the past 20 years, baseball has reduced the rate of stolen base attempts and devalued baserunning. Our project examines team level baserunning statistics to discern if there is a correlation between a team’s baserunning and its winning percentage. A factor, BsR, was created that includes all plays on the basepaths. BsR was found to correlate with team winning percentage; better baserunning results in a slight advantage.…Read More
Traditional methods of computing WAR (wins above replacement) for pitchers are based on an invalid mathematical foundation. Consequently, these metrics, which produce reasonable values for many pitchers, can be substantially inaccurate for some. Specifically, Fangraphs and Baseball Reference compute a pitcher’s WAR as a function of his performance averaged over the entire season. This is wrong because not all runs allowed have the same impact in determining the outcome of a game: for instance, the difference in impact between allowing 1 run in a game instead of 0 is much greater than the difference in impact between allowing 6 runs in a game instead of 5. Hence we propose a new way to compute WAR for starting pitchers: Grid WAR (gWAR). The idea is to compute a starter’s gWAR for each of his individual games, and define a starter’s seasonal gWAR as the sum of the gWAR of each of his games. We find that gWAR highly values games in which a pitcher allows few runs (0 or 1).…Read More
