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.
References
Aiken, K.D., Campbell, R.M., and Sukhdial, A. (2023). The sport marketing portfolio matrix: a theory-integrative exploration of Fans’“Buy,”“Hold,” and “sell” behaviors. J. Appl. Mark. Theory 10: 4. https://doi.org/10.20429/jamt.2023.100104.Suche in Google Scholar
Albert, J. and Bennett, J. (2007). Curve ball: baseball, statistics, and the role of chance in the game. Springer Science & Business Media, New York.Suche in Google Scholar
Anderson, A. (2013). Trading and under-diversification. Rev. Finance 17: 1699–1741. https://doi.org/10.1093/rof/rfs044.Suche in Google Scholar
Andersson, T. and Getz, D. (2020). Specialization versus diversification in the event portfolios of amateur athletes. Scand. J. Hosp. Tour. 20: 376–397. https://doi.org/10.1080/15022250.2020.1733653.Suche in Google Scholar
Baumer, B. and Zimbalist, A. (2014). The sabermetric revolution: assessing the growth of analytics in baseball. University of Pennsylvania Press, Pennsylvania.10.9783/9780812209129Suche in Google Scholar
Baumer, B.S., Jensen, S.T., and Matthews, G.J. (2015). openWAR: an open source system for evaluating overall player performance in major league baseball. J. Quant. Anal. Sports 11: 69–84. https://doi.org/10.1515/jqas-2014-0098.Suche in Google Scholar
Best, M.J. and Grauer, R.R. (1991). On the sensitivity of mean-variance-efficient portfolios to changes in asset means: some analytical and computational results. Rev. Finance Stud. 4: 315–342. https://doi.org/10.1093/rfs/4.2.315.Suche in Google Scholar
Cardozo, R.N. and Smith, D.K. (1983). Applying financial portfolio theory to product portfolio decisions: an empirical study. J. Mark. 47: 110–119. https://doi.org/10.2307/1251498.Suche in Google Scholar
Chiu, M.C. and Wong, H.Y. (2011). Mean–variance portfolio selection of cointegrated assets. J. Econ. Dyn. Control 35: 1369–1385. https://doi.org/10.1016/j.jedc.2011.04.003.Suche in Google Scholar
Choi, T.M., Li, D., and Yan, H. (2008). Mean–variance analysis for the newsvendor problem. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 38: 1169–1180. https://doi.org/10.1109/tsmca.2008.2001057.Suche in Google Scholar
Fask, A., Bishop, S., and Englander, F. (2023). Investing in march madness: an examination of the relationship between sports betting and portfolio construction. J. Predict. Mark. 17: 63–82. https://doi.org/10.5750/jpm.v17i2.2072.Suche in Google Scholar
Fitt, A.D. (2009). Markowitz portfolio theory for soccer spread betting. IMA J. Manag. Math. 20: 167–184. https://doi.org/10.1093/imaman/dpn028.Suche in Google Scholar
Fletcher, J. (2009). Risk reduction and mean-variance analysis: an empirical investigation. J. Bus. Finance Account. 36: 951–971. https://doi.org/10.1111/j.1468-5957.2009.02143.x.Suche in Google Scholar
Gârleanu, N. and Pedersen, L.H. (2013). Dynamic trading with predictable returns and transaction costs. J. Finance 68: 2309–2340. https://doi.org/10.1111/jofi.12080.Suche in Google Scholar
Harville, D.A. (2023). Modern and post-modern portfolio theory as applied to moneyline betting. J. Quant. Anal. Sports 19: 73–89. https://doi.org/10.1515/jqas-2021-0107.Suche in Google Scholar
Hickman, K.A., Teets, W.R., and Kohls, J.J. (1999). Social investing and modern portfolio theory. Am. Bus. Rev. 17: 72.Suche in Google Scholar
Jiménez, V., Alberto, R., Ontiveros, L., Manuel, J., and Possani, E. (2023). Sports betting: an application of neural networks and modern portfolio theory to the english premier league. arXiv preprint arXiv:2307.13807.Suche in Google Scholar
Keri, J. (2007). Baseball between the numbers: why everything you know about the game is wrong. Basic Books, New York.Suche in Google Scholar
Lai, T.L., Xing, H., and Chen, Z. (2011). Mean–variance portfolio optimization when means and covariances are unknown. Ann. Appl. Stat. 5: 798–823. https://doi.org/10.1214/10-aoas422.Suche in Google Scholar
Lewis, M. (2003). Moneyball: the art of winning an unfair game. Norton, New York.Suche in Google Scholar
Lindsey, G.R. (1963). An investigation of strategies in baseball. Oper. Res. 11: 477–501. https://doi.org/10.1287/opre.11.4.477.Suche in Google Scholar
Lubatkin, M. and Chatterjee, S. (1994). Extending modern portfolio theory into the domain of corporate diversification: does it apply? Acad. Manag. J. 37: 109–136. https://doi.org/10.2307/256772.Suche in Google Scholar
Madura, J. (1982). The theory of the firm and labor portfolio choice in professional team sports. Bus. Econ.: 11–18.Suche in Google Scholar
Marchi, M. and Albert, J. (2013). Analyzing baseball data with R. CRC Press, Boca Raton.Suche in Google Scholar
Markowitz, H.M. (1952). Portfolio selection. J. Finance 7: 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x.Suche in Google Scholar
Markowitz, H.M. (1959). Portfolio selection: efficient diversification of investments. John Wiley & Sons, New York.Suche in Google Scholar
O’Reilly, N. (2013). Portfolio theory and the management of professional sports clubs: the case of maple leaf sports and entertainment. In: Handbook of research on sport and business. Edward Elgar Publishing, pp. 333–349.10.4337/9781781005866.00028Suche in Google Scholar
Palczewski, A. and Palczewski, J. (2014). Theoretical and empirical estimates of mean–variance portfolio sensitivity. Eur. J. Oper. Res. 234: 402–410. https://doi.org/10.1016/j.ejor.2013.04.018.Suche in Google Scholar
Pinheiro, R. and Szymanski, S. (2022). All runs are created equal: labor market efficiency in major league baseball. J. Sports Econ. 23: 1046–1075. https://doi.org/10.1177/15270025221085712.Suche in Google Scholar
Schmidt, M.B. (2021). Risk and uncertainty in team building: evidence from a professional basketball market. J. Econ. Behav. Organ. 186: 735–753. https://doi.org/10.1016/j.jebo.2020.11.001.Suche in Google Scholar
Sharpe, W.F. (1964). Capital asset prices: a theory of market equilibrium under conditions of risk. J. Finance 19: 425–442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x.Suche in Google Scholar
Sharpe, W.F. (1966). Mutual fund performance. J. Bus. 39: 119–138. https://doi.org/10.1086/294846.Suche in Google Scholar
Tango, T.M., Lichtman, M.G., and Dolphin, A.E. (2007). The book: playing the percentages in baseball. Potomac Books, Inc., Williamsport, Maryland.Suche in Google Scholar
Teller, J. and Kock, A. (2013). An empirical investigation on how portfolio risk management influences project portfolio success. Int. J. Proj. Manag. 31: 817–829. https://doi.org/10.1016/j.ijproman.2012.11.012.Suche in Google Scholar
Thorn, J., Palmer, P., and Reuther, D. (2015). The hidden game of baseball: a revolutionary approach to baseball and its statistics. University of Chicago Press, Chicago.10.7208/chicago/9780226276830.001.0001Suche in Google Scholar
Tobin, J. (1958). Liquidity preference as behavior towards risk. Rev. Econ. Stud. 25: 65–86. https://doi.org/10.2307/2296205.Suche in Google Scholar
Wu, J., Li, J., Wang, S., and Cheng, T.C. (2009). Mean–variance analysis of the newsvendor model with stockout cost. Omega 37: 724–730. https://doi.org/10.1016/j.omega.2008.02.005.Suche in Google Scholar
Yorke, D.A. and Droussiotis, G. (1994). The use of customer portfolio theory: an empirical survey. J. Bus. Ind. Mark. 9: 6–18, https://doi.org/10.1108/08858629410066818.Suche in Google Scholar
Zhao, L. and Palomar, D.P. (2018). A markowitz portfolio approach to options trading. IEEE Trans. Signal Process. 66: 4223–4238. https://doi.org/10.1109/tsp.2018.2849733.Suche in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- 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
Artikel in diesem Heft
- 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