Fall 2025

Using Markov Chains and Statistical Analysis to Model Intentional Fouling Situations in NCAA Division 1 Men’s Basketball

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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

Boosting Draft Accuracy: A Two-Stage Classifier–Regressor Approach for NFL Wide Receiver Prospect Evaluation

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The NFL draft is an opportunity for teams to draft new players, address positional needs, and strengthen their roster for the upcoming season. Teams often trade players or compensation packages to secure certain prospects, which can be critical for team success. Wide receivers were selected as the focus of this study because the position is heavily influenced by objective data — such as receiving statistics and combine metrics like speed, agility, and explosiveness — making it well-suited for predictive modeling using machine learning.…Read More

Beyond the Hot Hand: Skill, Experience, and Context as Determinants of Elite Badminton Performance

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This study develops a predictive model for elite badminton match outcomes to identify the key performance drivers in the sport. Using a comprehensive dataset of 3,761 men’s singles matches from the BWF World Tour (2018-2021), features have been engineered to capture player skill, via custom Elo rating system, experience, recent form and match context. The Elo was then benchmarked against logistic regression and an optimized XGBoost classifier, with evaluations tested on a held-out test set. The XGBoost model achieved superior prediction accuracy of 76.49%, statistically improving upon traditional methods.…Read More

Re-evaluating the Qualifying/Finish Relationship in Formula One: A Replication and Correction of Prior Findings

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Prior academic research in Formula One, most notably M¨uhlbauer (2010), concluded that starting grid positions were the strongest determinants of race outcomes while only examining the top eight finishers (fewer than 40%) of the competitors over a shortened sample of 4 seasons (2006–2009). This truncated sample approach limited the generalizability of its findings and likely affected the observed relationships.…Read More

PRSS: A New Metric to Quantify Pocket Collapses in the National Football League

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In the NFL, “quarterback pressure” refers to defensive actions that disrupt a passer’s timing, decisions, and positioning. Numerous quantitative measures have been proposed, with mixed effectiveness. We introduce the Pocket Reduction Speed Score (PRSS), a geometric, tracking-based metric that quantifies how quickly the quarterback’s pocket shrinks. We apply the metric to player-tracking data from the 2021 NFL regular season, compute PRSS for each play, and examine its association with yards gained. PRSS offers a continuous, interpretable measure that captures multi-defender effects and the moment of greatest pressure, complementing existing binary and closest-defender metrics while offering applications in pass-protection evaluation, scouting, and scheme design.…Read More

AI Special Feature: AI-Assisted Substitution Decisions: A Fuzzy Logic Approach to Real-Time Game Management

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With millions on the line every match, top soccer clubs still make critical substitution decisions based largely on intuition. This paper introduces an AI-powered Decision Support System (DSS) that brings data-driven rigor to one of the game’s most crucial tactical moments. Using fuzzy logic to model expert coaching knowledge, our system provides real-time substitution priorities by integrating validated performance metrics (playerankScore), fatigue (minutesPlayed), age, and disciplinary risk (TemCartaoAmarelo). A key innovation is its contextual logic, which modulates disciplinary risk based on a player’s tactical position (roleCluster), reflecting deeper tactical awareness. Validation through case studies confirms the system’s ability to balance conflicting factors and escalate priority in high-risk scenarios, providing a tangible competitive advantage for real-time game management when every decision counts.…Read More

A Run Expectancy Approach to Lead Distance Optimization in Major League Baseball

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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

Forecasting NFL Wide Receiver Touchdowns with a Temporal Linear Regression Model

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Forecasting touchdowns for NFL wide receivers is a challenging but valuable problem in football analytics and player evaluation. Touchdowns are notoriously volatile, influenced by red zone usage, quarterback play, and situational variance, making year-to-year outcomes difficult to predict. This study develops a temporal linear regression model to project wide receiver touchdown totals using a feature-rich dataset spanning 1990–2024. The dataset incorporates lagged statistics, two-year rolling averages, player age and experience, team offensive strength, and efficiency metrics such as catch rate and touchdowns per target. The model was trained on 1990–2010 player-seasons and tested on 2011–2024 data, with strict time-bounded feature engineering to prevent data leakage.…Read More

Optimizing Lead Distance

Web-Header-WAIAI-1-650x442 Wharton Sports Analytics Journal Open Access · ISSN 3070-4065 2025 MONEYBALL ACADEMY, ROOKIE REVIEW Optimizing Lead Distance  AUTHOR Jack Whitney-Epstein, University of Pennsylvania (Moneyball Academy) Jackson Hubbard, University of Pennsylvania (Moneyball Academy) Lila Dodson, University of Pennsylvania (Moneyball Academy) William Deflorio, University of Pennsylvania (Moneyball Academy) Zach Sissman, University of…Read More