Date of Defense

4-22-2026

Date of Graduation

5-2026

Department

Business Information Systems

First Advisor

Smriti Srivastava

Second Advisor

Utkarsh Shrivastava

Abstract

This thesis evaluates whether the core principles of the Moneyball ideology continue to predict team success in Major League Baseball in the modern analytics era. In the last two decades, the widespread adoption of advanced statistical methods across all franchises has raised the central question of whether the original inefficiencies Moneyball exploited may no longer exist. To address this, this study analyzes twenty years of team‑level data from 2005-2025 (excluding 2020), incorporating seven performance metrics historically or conceptually tied to Moneyball which include OBP, WHIP, FIP, DRS, SB%, clutch, and wins per dollar, alongside demographic controls. Success is measured across three dimensions: regular‑season wins, World Series championships, and longest win streak. This allows for a multidimensional assessment of how Moneyball variables perform in both stable and high‑variance environments.

Three regression models were used to evaluate these relationships: a linear model for regular‑season wins, a logistic model for championship probability, and a linear model for longest win streak. The results show that several foundational Moneyball statistics remain highly predictive in large‑sample contexts. OBP and WHIP consistently emerge as significant predictors across models, with OBP showing a strong positive association with both regular‑season wins and championship probability, and WHIP demonstrating a powerful negative relationship with regular‑season wins and streak length. DRS also contributes meaningfully in certain contexts, particularly postseason outcomes.

However, the findings also reveal clear limits to Moneyball’s predictive power. Variables such as clutch, FIP, and wins per dollar show inconsistent or nonsignificant effects across models, especially in postseason and streak‑based analyses. The logistic regression model explains only a modest portion of championship variance, reflecting the inherently unpredictable nature of short playoff series. Similarly, the longest win streak model indicates that streaks are only partially explained by underlying team quality, with randomness and timing playing substantial roles.

Overall, the results suggest that while core Moneyball principles remain relevant, particularly in the regular season, their competitive advantage has diminished as analytics have become ubiquitous across MLB organizations. Team that are strong in the chosen Moneyball statistics consistently perform well in the regular season, but not in the postseason and high‑variance environments because its influence weakens, underscoring the continued role of randomness, situational volatility, and factors not yet fully captured by modern analytics.

Access Setting

Honors Thesis-Open Access

Additional Files
Thesis Presentation.pdf (1308 kB)
Presentation
Download

Share

COinS