Skip to main content
SpringerLink
Log in

Modeling Concepts

  • Chapter
The Analysis of Sports Forecasting

Abstract

The oddsmaker’s line plays a key role in our modeling forecasts and defines one type of expectation: a gambling expectation. (Descriptors such as “gambling” and “gambler” are not used in the sense of (3.2.1: Part I); rather, they are used in the sense of conventional dictionary definitions.) Prior to contrasting the line with other types of expectations, we first elaborate on what the line is and is not.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References: Part II

  1. Merchant L. The National Football Lottery. Holy, Reinhart and Winston: NY, 1973.

    Google Scholar 

  2. Scarne J. Scarne’s Complete Guide to Gambling. Simon and Schuster: NY, 1961.

    Google Scholar 

  3. Tobin, J “The Wage-Price Mechanism: Overview of the Conference.” In The Econometrics of Price Determination Conference,O. Eckstein, ed. Federal reserve Board: Washington, 1972. (Quotation, p. 14)

    Google Scholar 

  4. Muth JF. “Rational Expectations and the Theory of Price Movements.” In Rational Expectations and Econometric Practice,R. Lucas and T. Sargant, eds. University of Minnesota Press: Minneapolis, 1981. (Quotation, p.4)

    Google Scholar 

  5. Ibid. (Quotation, p.5)

    Google Scholar 

  6. Lazear EP, Rosen S. Rank-order tournaments as optimum labor contracts. Journal of Political Economy, 1981; 89: 841–64.

    Article  Google Scholar 

  7. Holmstrom B. Moral hazard in teams. Bell Journal of Economics, 1982; 13 (Autumn): 324–40.

    Article  Google Scholar 

  8. Nalebuff BJ, Stiglitz JE. Prizes and incentives: Towards a general theory of compensation and competition. Bell Journal of Economics, 1983; 14 (Spring): 21–43.

    Article  Google Scholar 

  9. Higgins RS, Tollison RD. “Economics at the Track.” In Sportometrics,B. Goff and R. Tollison, eds. Texas A and M University Press: College Station, 1990. (Quotation, p. 15)

    Google Scholar 

  10. Stern H. On the probability of winning a football game. The American Statistician, 1991; 45 (No.3): 179–83.

    Google Scholar 

  11. Lindsey GR. The progress of the score during a baseball game. Journal of the American Statistical Association, 1961; 66: 703–28. (Quotation, p. 703)

    Google Scholar 

  12. Goff BL, Tollison RD. “Sportometrics; Present and Future.” In Reference 9. (Quotation, p. 312)

    Google Scholar 

  13. Tryfos P, Casey S, Cook S, Leger G, Pylypiak B. The profitability of wagering on NFL games. Management Science, 1984; 24: 123–32. (Quotation, p. 123)

    Google Scholar 

  14. Vergin RC Scriabin M. Winning Strategies for wagering on football games. Management Science, 1978; 24: 809–18; 809–18.

    Google Scholar 

  15. Thompson ML. On any given Sunday: Fair competitor orderings with maximum likelihood. Journal of the American Statistical Association, 1975; 70: 536–41.

    Google Scholar 

  16. Stefani RT. Improved least squares football, basketball, and soccer predictions. IEEE Transactions on Systems, Man, and Cybernetics, 1980; 10: 116–23.

    Article  Google Scholar 

  17. Rosner B. “An Analysis of Professional Football Scores.” In Management Science in Sports, M. Starr, ed. North Holland: NY, 1976.

    Google Scholar 

  18. Harville D. Predictions for National Football League games via linear-model methodology. Journal of the American Statistical Association, 1980; 75: 516–24.

    Article  Google Scholar 

  19. Goodie B. Relevant variables in professional football. Proceedings of the American Statistical Association, Social Statistics Section, 1978: 83–6.

    Google Scholar 

  20. Fama EF. Efficient capital markets: A review of theory and empirical work. Journal of Finance, 1970; 25: 383–423.

    Article  Google Scholar 

  21. Dobra JL, Cargill TF, Meyers RA. Efficient markets for wagers: The case of professional basketball wagering, In Reference 9. (Quotation, p. 247)

    Google Scholar 

  22. Higgins RS, Tollison RD. “Economics at the Track.” In Sportometrics,B. Goff and R. Tollison, eds. Texas A and M University Press: College Station, 1990. (Quotation, p. 15)

    Google Scholar 

  23. Snyder WW. Horse racing: Testing the efficient market model. Journal of Finance, 1978; 33: 1109–18.

    Article  Google Scholar 

  24. Ali M. Some evidence of the efficiency of a speculative market. Econometrica, 1979; 47: 38792.

    Article  Google Scholar 

  25. Figlewski S. Subjective information and market efficiency in a betting market. Journal of Political Economy, 1979; 87: 75–88.

    Article  Google Scholar 

  26. Hausch DB, Ziemba WT, Rubinstein M. Efficiency of the market for racetrack betting. Management Science, 1981; 27: 1435–52.

    Article  Google Scholar 

  27. Asch P, Malkiel BG, Quandt RE. Market efficiency in racetrack betting. Journal of Business, 1984; 57: 165–75.

    Article  Google Scholar 

  28. Bird R, McRae M. “The Efficiency of Gambling Markets Revisited.” In Proceedings of the Seventh International Conference on Gambling and Risk Taking, W. Eadington, ed. Bureau of Business and Economic Research, University of Nevada-Reno: Reno, 1988.

    Google Scholar 

  29. Amoako-Adu B. Manner H, Yagil J. The efficiency of certain speculative markets and gambling behavior. Journal of Economics and Business, 1985; 37: 365–78.

    Google Scholar 

  30. Pankoff LD. Market efficiency and football betting. Journal of Business, 1968; 41: 203–14.

    Article  Google Scholar 

  31. Zuber RA, Gander JM, Bowers BD. Beating the spread: Testing the efficiency of the market for National Football League games. Journal of Political Economy, 1985; 93: 800–6.

    Article  Google Scholar 

  32. Lacey NJ. Testing weak-form market efficiency in the NFL betting market. In Reference 28.

    Google Scholar 

  33. Cover TM. Do longer games favor stronger teams? The American Statistician, 1989; 43 (No. 4): 277–78.

    Google Scholar 

  34. Cooper H, DeNeve KM, Mosteller F. Predicting professional sport game outcomes from intermediate game scores. Chance, 1992; 5 (No. 3–4): 18–22.

    Google Scholar 

  35. Efron B, Morris C. Data analysis using Stein’s estimator and generalizations. Journal of the American Statistical Association, 1975; 70: 311–19.

    Article  Google Scholar 

  36. Berry DA, Berry TD. The probability of a field goal: rating kickers. The American Statistician, 1985; 39 (No. 2): 152–55.

    Article  Google Scholar 

  37. Mosteller F. The world series competition. Journal of the American Statistical Association, 1952; 47: 355–80.

    Article  Google Scholar 

  38. Searls DT. On the probability of winning with different tournament procedures. Journal of the American Statistical Association, 1963; 58: 1064–81.

    Article  Google Scholar 

  39. Management Science in Sports, M. Starr, ed. North Holland: NY, 1976.

    Google Scholar 

  40. In Reference 9.

    Google Scholar 

  41. Wright S. The interpretation of multivariate systems. In Statistics and Mathematics in Biology, O. Kempthorne, J. Gowen, J. Lush, eds. Iowa State College Press: Ames, 1954.

    Google Scholar 

  42. Tukey J. Causation, regression, and path analysis. In Reference 41.

    Google Scholar 

  43. Mallios WS. Statistical Modeling: Applications in Contemporary Issues. Iowa State University Press: Ames, 1989.

    Google Scholar 

  44. Granger CWJ. Testing for causality. Journal of Economic Dynamics and Control, 1980; 2: 32952. (Quotation, p. 343)

    Google Scholar 

  45. Overseth 0E. Experiments in time reversal. Scientific American, 1969; 221 (Oct.): 88–101.

    Article  Google Scholar 

  46. Granger CWJ. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 1969; 37 (No. 3): 424–38. (Quotation, p. 430)

    Google Scholar 

  47. Frank P. Philosophy of Science. Prentice Hall:Engelwood Cliffs, NJ, 1957. (Quotation, p. 280)

    Google Scholar 

  48. Suppes P. A Probabilistic Theory of Causation. North Holland:Amsterdam, 1970. (Quotation, pp. 9–10)

    Google Scholar 

  49. Holland PW. Statistics and causal inference. Journal of the American Statistical Assoociation, 1986; 81 (No. 396): 945–70.

    Article  Google Scholar 

  50. Zellner A. Causality and Econometrics. Proceedings of conference held at University of Rochester, NY, 1978.

    Google Scholar 

  51. Miller AJ. Subset Selection in Regression. Chapman and Hill: NY, 1990.

    Google Scholar 

  52. Leger C, Altman N. Assessing influence in variable selection problems. Journal of the American Statistical Association, 1993; 88 (No. 442): 457–56.

    Google Scholar 

  53. Weiss RE. The influence of variable selection: A Bayesian diagnostic perspective. Handout from UCLA Statistics Symposium, April 1993.

    Google Scholar 

  54. Zellner A, Palm F. Time series analysis and simultaneous equation econometric models. Journal of Econometrics, 1974; 2: 17–54.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Craig School of Business, California State University, Fresno, USA

    William S. Mallios

Authors
  1. William S. Mallios
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer Science+Business Media New York

About this chapter

Cite this chapter

Mallios, W.S. (2000). Modeling Concepts. In: The Analysis of Sports Forecasting. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-6713-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4757-6713-1_3

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4419-4958-5

  • Online ISBN: 978-1-4757-6713-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation