IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v3y2021i1p7-112d499928.html
   My bibliography  Save this article

Construction of a Predictive Model for MLB Matches

Author

Listed:
  • Chia-Hao Chang

    (Department of Nursing, Chang Gung University of Science and Technology, Chiayi Campus, Chiayi 61363, Taiwan)

Abstract

The main purpose of this article was to define a model that could defeat the online bookmakers’ odds, where the betting item considered was the first five innings of major league baseball (MLB) matches. The betting odds of online bookmakers have two purposes: first, they are used to quantify the amount of profit made by the bettors; second, they are regarded as a market equilibrium point between multiple bookmakers and bettors. If the bettors have a more accurate prediction model than the system used to produce betting odds, it will create a positive expected return for the bettors. In this article, we used the Markov process method and the runner advancement model to estimate the expected runs in an MLB match for the teams based on the batting lineup and the pitcher.

Suggested Citation

  • Chia-Hao Chang, 2021. "Construction of a Predictive Model for MLB Matches," Forecasting, MDPI, vol. 3(1), pages 1-11, February.
  • Handle: RePEc:gam:jforec:v:3:y:2021:i:1:p:7-112:d:499928
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/3/1/7/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2571-9394/3/1/7/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nikolaos Vlastakis & George Dotsis & Raphael Markellos, 2008. "Nonlinear modelling of European football scores using support vector machines," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 111-118.
    2. Albert Jim, 2008. "Streaky Hitting in Baseball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(1), pages 1-34, January.
    3. Wunderlich, Fabian & Memmert, Daniel, 2020. "Are betting returns a useful measure of accuracy in (sports) forecasting?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 713-722.
    4. Anthony Costa Constantinou & Norman Elliott Fenton, 2013. "Profiting From Arbitrage And Odds Biases Of The European Football Gambling Market," Journal of Gambling Business and Economics, University of Buckingham Press, vol. 7(2), pages 41-70.
    5. Kevin Fritz & Bruce Bukiet, 2010. "Objective Method for Determining the Most Valuable Player in Major League Baseball," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 10(2), pages 152-169, August.
    6. Bruce Bukiet & Elliotte Rusty Harold & José Luis Palacios, 1997. "A Markov Chain Approach to Baseball," Operations Research, INFORMS, vol. 45(1), pages 14-23, February.
    7. Shin, Hyun Song, 1991. "Optimal Betting Odds against Insider Traders," Economic Journal, Royal Economic Society, vol. 101(408), pages 1179-1185, September.
    8. Shin, Hyun Song, 1993. "Measuring the Incidence of Insider Trading in a Market for State-Contingent Claims," Economic Journal, Royal Economic Society, vol. 103(420), pages 1141-1153, September.
    9. Hirotsu Nobuyoshi, 2011. "Reconsideration of the Best Batting Order in Baseball: Is the Order to Maximize the Expected Number of Runs Really the Best?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(2), pages 1-12, May.
    10. Hyun Song Shin, 2008. "Prices Of State Contingent Claims With Insider Traders, And The Favourite-Longshot Bias," World Scientific Book Chapters, in: Donald B Hausch & Victor SY Lo & William T Ziemba (ed.), Efficiency Of Racetrack Betting Markets, chapter 34, pages 343-352, World Scientific Publishing Co. Pte. Ltd..
    11. Rump Christopher M, 2008. "Data Clustering for Fitting Parameters of a Markov Chain Model of Multi-Game Playoff Series," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(1), pages 1-19, January.
    12. Constantinou Anthony Costa & Fenton Norman Elliott, 2012. "Solving the Problem of Inadequate Scoring Rules for Assessing Probabilistic Football Forecast Models," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-14, March.
    13. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ioannis Asimakopoulos & John Goddard, 2004. "Forecasting football results and the efficiency of fixed-odds betting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(1), pages 51-66.
    2. Rebeggiani, Luca & Gross, Johannes, 2018. "Chance or Ability? The Efficiency of the Football Betting Market Revisited," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181563, Verein für Socialpolitik / German Economic Association.
    3. Martin Kukuk & Stefan Winter, 2008. "An Alternative Explanation of the Favorite-Longshot Bias," Journal of Gambling Business and Economics, University of Buckingham Press, vol. 2(2), pages 79-96, September.
    4. John Peirson & Michael A. Smith, 2010. "Expert Analysis and Insider Information in Horse Race Betting: Regulating Informed Market Behavior," Southern Economic Journal, John Wiley & Sons, vol. 76(4), pages 976-992, April.
    5. Egon Franck & Erwin Verbeek & Stephan Nüesch, 2011. "Sentimental Preferences and the Organizational Regime of Betting Markets," Southern Economic Journal, John Wiley & Sons, vol. 78(2), pages 502-518, October.
    6. Stephen Morris, 1997. "Risk, uncertainty and hidden information," Theory and Decision, Springer, vol. 42(3), pages 235-269, May.
    7. Kai Fischer & Justus Haucap, 2022. "Home advantage in professional soccer and betting market efficiency: The role of spectator crowds," Kyklos, Wiley Blackwell, vol. 75(2), pages 294-316, May.
    8. Babatunde Buraimo & David Peel & Rob Simmons, 2013. "Systematic Positive Expected Returns in the UK Fixed Odds Betting Market: An Analysis of the Fink Tank Predictions," IJFS, MDPI, vol. 1(4), pages 1-15, December.
    9. McAlvanah, Patrick & Moul, Charles C., 2013. "The house doesn’t always win: Evidence of anchoring among Australian bookies," Journal of Economic Behavior & Organization, Elsevier, vol. 90(C), pages 87-99.
    10. Bag, Parimal Kanti & Saha, Bibhas, 2011. "Match-fixing under competitive odds," Games and Economic Behavior, Elsevier, vol. 73(2), pages 318-344.
    11. Michael Cain & David Law & David Peel, 2002. "Is one price enough to value a state-contingent asset correctly? Evidence from a gambling market," Applied Financial Economics, Taylor & Francis Journals, vol. 12(1), pages 33-38.
    12. M. Cain & D. Law & D. A. Peel, 2003. "Some analysis of the properties of the Harville place formulae when allowance is made for the favourite-long shot bias employing Shin Win probabilities," Applied Economics Letters, Taylor & Francis Journals, vol. 10(1), pages 53-57.
    13. Alistair Bruce & David Marginson, 2014. "Power, Not Fear: A Collusion-Based Account of Betting Market Inefficiency," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 21(1), pages 77-97, February.
    14. Vincenzo Candila & Lucio Palazzo, 2020. "Neural Networks and Betting Strategies for Tennis," Risks, MDPI, vol. 8(3), pages 1-19, June.
    15. A. Schnytzer & V. Makropoulou & M. Lamers, 2012. "Pricing Decisions and Insider Trading in Horse Betting Markets," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/772, Ghent University, Faculty of Economics and Business Administration.
    16. Oliver Merz & Raphael Flepp & Egon Franck, 2019. "Does sentiment harm market efficiency? An empirical analysis using a betting exchange setting," Working Papers 381, University of Zurich, Department of Business Administration (IBW).
    17. Smith, Michael A. & Vaughan Williams, Leighton, 2010. "Forecasting horse race outcomes: New evidence on odds bias in UK betting markets," International Journal of Forecasting, Elsevier, vol. 26(3), pages 543-550, July.
    18. Egon Franck & Erwin Verbeek & Stephan Nüesch, 2013. "Inter-market Arbitrage in Betting," Economica, London School of Economics and Political Science, vol. 80(318), pages 300-325, April.
    19. Kai Fischer & Justus Haucap, 2020. "Betting Market Efficiency in the Presence of Unfamiliar Shocks: The Case of Ghost Games during the Covid-19 Pandemic," CESifo Working Paper Series 8526, CESifo.
    20. Michael A. Smith & David Paton & Leighton Vaughan-Williams, 2004. "Costs, biases and betting markets: new evidence," Working Papers 2004/5, Nottingham Trent University, Nottingham Business School, Economics Division.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jforec:v:3:y:2021:i:1:p:7-112:d:499928. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.