Abstract
This paper proposes novel approaches to measuring team productivity and evaluating trading efficiency in Major League Baseball (MLB) from 1995 to 2021 through an application of portfolio theory. The performance of individual players is measured using a structural approach relating player outcomes to team runs developed by Lindsey (1963. An investigation of strategies in baseball. Oper. Res. 11: 477–501). Using a portfolio theory framework, we treat MLB teams as a portfolio of players (assets), each of which can be defined by an expected contribution of runs per game and the variance of this measure. It is found that both the expected value and variance have a positive impact on team runs scored. Given our definition of teams characterized by their expected values and variances, we evaluate trading efficiency between teams given their pre-trade expected values and variances and the acquired player’s pre-trade expected value and variance. We find that trade efficiency has improved over our timeframe, consistent with the growth in data-driven decision making used in MLB front offices.
Acknowledgments
The authors would like to thank Charlie Brown, Rodney Fort, Brad Humphreys, and two anonymous reviewers for their helpful feedback.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.
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© 2024 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Editorial
- Thoughts from the Editor
- Research Articles
- European football player valuation: integrating financial models and network theory
- On the efficiency of trading intangible fixed assets in Major League Baseball
- Expected goals under a Bayesian viewpoint: uncertainty quantification and online learning
- Bayesian bivariate Conway–Maxwell–Poisson regression model for correlated count data in sports
- Success factors in national team football: an analysis of the UEFA EURO 2020
Articles in the same Issue
- Frontmatter
- Editorial
- Thoughts from the Editor
- Research Articles
- European football player valuation: integrating financial models and network theory
- On the efficiency of trading intangible fixed assets in Major League Baseball
- Expected goals under a Bayesian viewpoint: uncertainty quantification and online learning
- Bayesian bivariate Conway–Maxwell–Poisson regression model for correlated count data in sports
- Success factors in national team football: an analysis of the UEFA EURO 2020