IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v26yi3p606-621.html
   My bibliography  Save this article

Issues in sports forecasting

Author

Listed:
  • Stekler, H.O.
  • Sendor, David
  • Verlander, Richard

Abstract

A large amount of effort is spent on forecasting the outcomes of sporting events, but few papers have focused exclusively on the characteristics of sports forecasts. Instead, many papers have been written about the efficiency of sports betting markets. As it turns out, it is possible to derive a considerable amount of information about the forecasts and the forecasting process from studies that have tested the markets for economic efficiency. Moreover, the huge number of observations provided by betting markets makes it possible to obtain robust tests of various forecasting hypotheses. This paper is concerned with a number of forecasting topics in horse racing and several team sports. The first topic involves the type of forecast that is made: picking a winner or predicting whether a particular team will beat the point spread. Different evaluation procedures will be examined and alternative forecasting methods (models, experts, and the market) compared. The paper also examines the evidence with regard to the existence of biases in the forecasts, and concludes by discussing the applicability of these results to forecasting in general.

Suggested Citation

  • Stekler, H.O. & Sendor, David & Verlander, Richard, 2010. "Issues in sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 606-621, July.
  • Handle: RePEc:eee:intfor:v:26:y::i:3:p:606-621
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169-2070(10)00009-9
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    • Herman O. Stekler & David Sendor & Richard Verlander, 2009. "Issues in Sports Forecasting," Working Papers 2009-002, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.

    References listed on IDEAS

    as
    1. Stephen Shmanske, 2005. "Odds-setting efficiency in gambling markets: Evidence from the PGA TOUR," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 29(3), pages 391-402, September.
    2. repec:reg:rpubli:259 is not listed on IDEAS
    3. John M. Gandar & William H. Dare & Craig R. Brown & Richard A. Zuber, 1998. "Informed Traders and Price Variations in the Betting Market for Professional Basketball Games," Journal of Finance, American Finance Association, vol. 53(1), pages 385-401, February.
    4. 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.
    5. James D. Dana & Michael M. Knetter, 1994. "Learning and Efficiency in a Gambling Market," Management Science, INFORMS, vol. 40(10), pages 1317-1328, October.
    6. Sauer, Raymond D & Brajer, Vic & Ferris, Stephen P & Marr, M Wayne, 1988. "Hold Your Bets: Another Look at the Efficiency of the Gambling Market for National Football League Games: Comment," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 206-213, February.
    7. David Forrest & Ian Mchale, 2007. "Anyone for Tennis (Betting)?," The European Journal of Finance, Taylor & Francis Journals, vol. 13(8), pages 751-768.
    8. Turocy, Theodore L., 2005. "Offensive performance, omitted variables, and the value of speed in baseball," Economics Letters, Elsevier, vol. 89(3), pages 283-286, December.
    9. Roger C. Vergin & Michael Scriabin, 1978. "Winning Strategies for Wagering on National Football League Games," Management Science, INFORMS, vol. 24(8), pages 809-818, April.
    10. Raymond Sauer, 2005. "The state of research on markets for sports betting and suggested future directions," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 29(3), pages 416-426, September.
    11. John M. Gandar & Richard A. Zuber & William H. Dare, 2000. "The Search for Informed Traders in the Totals Betting Market for National Basketball Association Games," Journal of Sports Economics, , vol. 1(2), pages 177-186, May.
    12. Leighton Vaughan Williams & David Paton, 1998. "Why are some favourite-longshot biases positive and others negative?," Applied Economics, Taylor & Francis Journals, vol. 30(11), pages 1505-1510.
    13. Raymond D. Sauer, 1998. "The Economics of Wagering Markets," Journal of Economic Literature, American Economic Association, vol. 36(4), pages 2021-2064, December.
    14. Rodney J. Paul & Andrew P. Weinbach, 2005. "Bettor Misperceptions in the NBA," Journal of Sports Economics, , vol. 6(4), pages 390-400, November.
    15. Brown, William O & Sauer, Raymond D, 1993. "Does the Basketball Market Believe in the Hot Hand? Comment," American Economic Review, American Economic Association, vol. 83(5), pages 1377-1386, December.
    16. I. Graham & H. Stott, 2008. "Predicting bookmaker odds and efficiency for UK football," Applied Economics, Taylor & Francis Journals, vol. 40(1), pages 99-109.
    17. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    18. Woodland, Linda M & Woodland, Bill M, 1994. "Market Efficiency and the Favorite-Longshot Bias: The Baseball Betting Market," Journal of Finance, American Finance Association, vol. 49(1), pages 269-279, March.
    19. Boulier, Bryan L. & Stekler, H. O., 2003. "Predicting the outcomes of National Football League games," International Journal of Forecasting, Elsevier, vol. 19(2), pages 257-270.
    20. Andersson, Patric & Edman, Jan & Ekman, Mattias, 2005. "Predicting the World Cup 2002 in soccer: Performance and confidence of experts and non-experts," International Journal of Forecasting, Elsevier, vol. 21(3), pages 565-576.
    21. J. M. Gandar & Richard Zuber & R. S. Johnson & W. Dare, 2002. "Re-examining the betting market on Major League Baseball games: is there a reverse favourite-longshot bias?," Applied Economics, Taylor & Francis Journals, vol. 34(10), pages 1309-1317.
    22. Crafts, Nicholas F R, 1985. "Some Evidence of Insider Knowledge in Horse Race Betting in Britain," Economica, London School of Economics and Political Science, vol. 52(27), pages 295-304, August.
    23. Dixon, Mark J. & Pope, Peter F., 2004. "The value of statistical forecasts in the UK association football betting market," International Journal of Forecasting, Elsevier, vol. 20(4), pages 697-711.
    24. Boulier, Bryan L. & Stekler, H. O., 1999. "Are sports seedings good predictors?: an evaluation," International Journal of Forecasting, Elsevier, vol. 15(1), pages 83-91, February.
    25. Roger Vergin, 2001. "Overreaction in the NFL point spread market," Applied Financial Economics, Taylor & Francis Journals, vol. 11(5), pages 497-509.
    26. Stefan Szymanski, 2010. "The Economic Design of Sporting Contests," Palgrave Macmillan Books, in: The Comparative Economics of Sport, chapter 1, pages 1-78, Palgrave Macmillan.
    27. Vaughan Williams,Leighton (ed.), 2005. "Information Efficiency in Financial and Betting Markets," Cambridge Books, Cambridge University Press, number 9780521816038.
    28. Busche, Kelly & Hall, Christopher D, 1988. "An Exception to the Risk Preference Anomaly," The Journal of Business, University of Chicago Press, vol. 61(3), pages 337-346, July.
    29. David J. Berri, 1999. "Who is 'most valuable'? Measuring the player's production of wins in the National Basketball Association," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 411-427.
    30. Steven Caudill & Norman Godwin, 2002. "Heterogeneous skewness in binary choice models: Predicting outcomes in the men's NCAA basketball tournament," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 991-1001.
    31. Edward H. Kaplan & Stanley J. Garstka, 2001. "March Madness and the Office Pool," Management Science, INFORMS, vol. 47(3), pages 369-382, March.
    32. Zak, Thomas A & Huang, Cliff J & Siegfried, John J, 1979. "Production Efficiency: The Case of Professional Basketball," The Journal of Business, University of Chicago Press, vol. 52(3), pages 379-392, July.
    33. Michael Cain & David Law & David Peel, 2000. "The Favourite‐Longshot Bias and Market Efficiency in UK Football betting," Scottish Journal of Political Economy, Scottish Economic Society, vol. 47(1), pages 25-36, February.
    34. Dare, William H. & MacDonald, S. Scott, 1996. "A generalized model for testing the home and favorite team advantage in point spread markets," Journal of Financial Economics, Elsevier, vol. 40(2), pages 295-318, February.
    35. Forrest, David & Simmons, Robert, 2000. "Forecasting sport: the behaviour and performance of football tipsters," International Journal of Forecasting, Elsevier, vol. 16(3), pages 317-331.
    36. 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.
    37. Ruth N. Bolton & Randall G. Chapman, 2008. "Searching For Positive Returns At The Track: A Multinomial Logit Model For Handicapping Horse Races," World Scientific Book Chapters, in: Donald B Hausch & Victor SY Lo & William T Ziemba (ed.), Efficiency Of Racetrack Betting Markets, chapter 17, pages 151-171, World Scientific Publishing Co. Pte. Ltd..
    38. Brown, William O & Sauer, Raymond D, 1993. "Fundamentals or Noise? Evidence from the Professional Basketball Betting Market," Journal of Finance, American Finance Association, vol. 48(4), pages 1193-1209, September.
    39. Fiona Carmichael & Dennis Thomas & Robert Ward, 2000. "Team performance: the case of English Premiership football," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 21(1), pages 31-45.
    40. William Dare & A. Steven Holland, 2004. "Efficiency in the NFL betting market: modifying and consolidating research methods," Applied Economics, Taylor & Francis Journals, vol. 36(1), pages 9-15.
    41. David Forrest & Robert Simmons & Babatunde Buraimo, 2005. "Outcome Uncertainty And The Couch Potato Audience," Scottish Journal of Political Economy, Scottish Economic Society, vol. 52(4), pages 641-661, September.
    42. Lawrence Hadley & Marc Poitras & John Ruggiero & Scott Knowles, 2000. "Performance evaluation of National Football League teams," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 21(2), pages 63-70.
    43. Forrest, David & Goddard, John & Simmons, Robert, 2005. "Odds-setters as forecasters: The case of English football," International Journal of Forecasting, Elsevier, vol. 21(3), pages 551-564.
    44. Camerer, Colin F, 1989. "Does the Basketball Market Believe in the 'Hot Hand'?," American Economic Review, American Economic Association, vol. 79(5), pages 1257-1261, December.
    45. Vaughan Williams, Leighton, 1999. "Information Efficiency in Betting Markets: A Survey," Bulletin of Economic Research, Wiley Blackwell, vol. 51(1), pages 1-30, January.
    46. Gray, Philip K & Gray, Stephen F, 1997. "Testing Market Efficiency: Evidence from the NFL Sports Betting Market," Journal of Finance, American Finance Association, vol. 52(4), pages 1725-1737, September.
    47. Bill Woodland & Linda Woodland, 1999. "Expected utility, skewness, and the baseball betting market," Applied Economics, Taylor & Francis Journals, vol. 31(3), pages 337-345.
    48. John Ruggiero & Lawrence Hadley & Gerry Ruggiero & Scott Knowles, 1997. "A Note on the Pythagorean Theorem of Baseball Production," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 18(4), pages 335-342.
    49. M. J. Maher, 1982. "Modelling association football scores," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 36(3), pages 109-118, September.
    50. Ron Bird & Michael Mccrae, 2008. "Tests Of The Efficiency Of Racetrack Betting Using Bookmaker Odds," World Scientific Book Chapters, in: Donald B Hausch & Victor SY Lo & William T Ziemba (ed.), Efficiency Of Racetrack Betting Markets, chapter 59, pages 593-603, World Scientific Publishing Co. Pte. Ltd..
    51. Pope, Peter F & Peel, David A, 1989. "Information, Prices and Efficiency in a Fixed-Odds Betting Market," Economica, London School of Economics and Political Science, vol. 56(223), pages 323-341, August.
    52. Snyder, Wayne W, 1978. "Horse Racing: Testing the Efficient Markets Model," Journal of Finance, American Finance Association, vol. 33(4), pages 1109-1118, September.
    53. Lyn D. Pankoff, 1968. "Market Efficiency and Football Betting," The Journal of Business, University of Chicago Press, vol. 41, pages 203-203.
    54. Rodney Paul & Andrew Weinbach, 2005. "Market efficiency and NCAA college basketball gambling," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 29(3), pages 403-408, September.
    55. Marshall Gramm & Douglas Owens, 2005. "Determinants of betting market efficiency," Applied Economics Letters, Taylor & Francis Journals, vol. 12(3), pages 181-185.
    56. Song, ChiUng & Boulier, Bryan L. & Stekler, Herman O., 2007. "The comparative accuracy of judgmental and model forecasts of American football games," International Journal of Forecasting, Elsevier, vol. 23(3), pages 405-413.
    57. Caudill, Steven B., 2003. "Predicting discrete outcomes with the maximum score estimator: the case of the NCAA men's basketball tournament," International Journal of Forecasting, Elsevier, vol. 19(2), pages 313-317.
    58. Bruno Deschamps & Olivier Gergaud, 2007. "Efficiency in Betting Markets: Evidence from English Football," Journal of Prediction Markets, University of Buckingham Press, vol. 1(1), pages 61-73, February.
    59. William Dare & John Gandar & Richard Zuber & Robert Pavlik, 2005. "In search of the source of informed trader information in the college football betting market," Applied Financial Economics, Taylor & Francis Journals, vol. 15(3), pages 143-152.
    60. Tim Kuypers, 2000. "Information and efficiency: an empirical study of a fixed odds betting market," Applied Economics, Taylor & Francis Journals, vol. 32(11), pages 1353-1363.
    61. Avery, Christopher & Chevalier, Judith, 1999. "Identifying Investor Sentiment from Price Paths: The Case of Football Betting," The Journal of Business, University of Chicago Press, vol. 72(4), pages 493-521, October.
    62. Zuber, Richard A & Gandar, John M & Bowers, Benny D, 1985. "Beating the Spread: Testing the Efficiency of the Gambling Market for National Football League Games," Journal of Political Economy, University of Chicago Press, vol. 93(4), pages 800-806, August.
    63. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, vol. 21(2), pages 331-340.
    64. Golec, Joseph & Tamarkin, Maurry, 1991. "The degree of inefficiency in the football betting market : Statistical tests," Journal of Financial Economics, Elsevier, vol. 30(2), pages 311-323, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christoph Schlembach & Sascha L. Schmidt & Dominik Schreyer & Linus Wunderlich, 2020. "Forecasting the Olympic medal distribution during a pandemic: a socio-economic machine learning model," Papers 2012.04378, arXiv.org, revised Jun 2021.
    2. Li, Yongjun & Wang, Lizheng & Li, Feng, 2021. "A data-driven prediction approach for sports team performance and its application to National Basketball Association," Omega, Elsevier, vol. 98(C).
    3. Manner Hans, 2016. "Modeling and forecasting the outcomes of NBA basketball games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(1), pages 31-41, March.
    4. Singleton, Carl & Reade, J. James & Brown, Alasdair, 2020. "Going with your gut: The (In)accuracy of forecast revisions in a football score prediction game," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 89(C).
    5. Alexis Direr, 2013. "Are betting markets efficient? Evidence from European Football Championships," Applied Economics, Taylor & Francis Journals, vol. 45(3), pages 343-356, January.
    6. Vincenzo Candila & Antonio Scognamillo, 2019. "On the Longshot Bias in Tennis Betting Markets: The Casco Normalization," Working Papers 3_236, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno.
    7. Erik Å trumbelj, 2016. "A Comment on the Bias of Probabilities Derived From Betting Odds and Their Use in Measuring Outcome Uncertainty," Journal of Sports Economics, , vol. 17(1), pages 12-26, January.
    8. Štrumbelj, Erik & Vračar, Petar, 2012. "Simulating a basketball match with a homogeneous Markov model and forecasting the outcome," International Journal of Forecasting, Elsevier, vol. 28(2), pages 532-542.
    9. Peeters, Thomas, 2018. "Testing the Wisdom of Crowds in the field: Transfermarkt valuations and international soccer results," International Journal of Forecasting, Elsevier, vol. 34(1), pages 17-29.
    10. Schlembach, Christoph & Schmidt, Sascha L. & Schreyer, Dominik & Wunderlich, Linus, 2022. "Forecasting the Olympic medal distribution – A socioeconomic machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    11. Baker, Rose D. & McHale, Ian G., 2013. "Forecasting exact scores in National Football League games," International Journal of Forecasting, Elsevier, vol. 29(1), pages 122-130.
    12. Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.
    13. Jeon, Gyuhyeon & Park, Juyong, 2021. "Characterizing patterns of scoring and ties in competitive sports," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    14. Delen, Dursun & Cogdell, Douglas & Kasap, Nihat, 2012. "A comparative analysis of data mining methods in predicting NCAA bowl outcomes," International Journal of Forecasting, Elsevier, vol. 28(2), pages 543-552.
    15. Coussement, Kristof & De Bock, Koen W., 2013. "Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning," Journal of Business Research, Elsevier, vol. 66(9), pages 1629-1636.
    16. Sung, Ming-Chien & McDonald, David C.J. & Johnson, Johnnie E.V. & Tai, Chung-Ching & Cheah, Eng-Tuck, 2019. "Improving prediction market forecasts by detecting and correcting possible over-reaction to price movements," European Journal of Operational Research, Elsevier, vol. 272(1), pages 389-405.
    17. Vittorio Maniezzo & Fabian Andres Aspee Encina, 2022. "Predictive Analytics for Real-time Auction Bidding Support: a Case on Fantasy Football," SN Operations Research Forum, Springer, vol. 3(3), pages 1-23, September.
    18. Ruud H. Koning & Renske Zijm, 2023. "Betting market efficiency and prediction in binary choice models," Annals of Operations Research, Springer, vol. 325(1), pages 135-148, June.
    19. June Buchanan & Yun Shen, 2021. "Gambling and marketing: a systematic literature review using HistCite," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(2), pages 2837-2851, June.
    20. Vaughan Williams Leighton & Liu Chunping & Dixon Lerato & Gerrard Hannah, 2021. "How well do Elo-based ratings predict professional tennis matches?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 91-105, June.
    21. Kovalchik, Stephanie & Reid, Machar, 2019. "A calibration method with dynamic updates for within-match forecasting of wins in tennis," International Journal of Forecasting, Elsevier, vol. 35(2), pages 756-766.
    22. Hubáček, Ondřej & Šourek, Gustav & Železný, Filip, 2019. "Exploiting sports-betting market using machine learning," International Journal of Forecasting, Elsevier, vol. 35(2), pages 783-796.
    23. Hubáček, Ondřej & Šír, Gustav, 2023. "Beating the market with a bad predictive model," International Journal of Forecasting, Elsevier, vol. 39(2), pages 691-719.
    24. Song, Kai & Shi, Jian, 2020. "A gamma process based in-play prediction model for National Basketball Association games," European Journal of Operational Research, Elsevier, vol. 283(2), pages 706-713.
    25. B. Jay Coleman, 2014. "Minimum violations and predictive meta‐rankings for college football," Naval Research Logistics (NRL), John Wiley & Sons, vol. 61(1), pages 17-33, February.

    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. Vaughan Williams, Leighton & Stekler, Herman O., 2010. "Sports forecasting," International Journal of Forecasting, Elsevier, vol. 26(3), pages 445-447, July.
      • Herman O. Stekler, 2007. "Sports Forecasting," Working Papers 2007-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting, revised Jan 2007.
    2. Jinook Jeong & Jee Young Kim & Yoon Jae Ro, 2019. "On the efficiency of racetrack betting market: a new test for the favourite-longshot bias," Applied Economics, Taylor & Francis Journals, vol. 51(54), pages 5817-5828, November.
    3. 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.
    4. Philip W. S. Newall & Dominic Cortis, 2021. "Are Sports Bettors Biased toward Longshots, Favorites, or Both? A Literature Review," Risks, MDPI, vol. 9(1), pages 1-9, January.
    5. Jaiho Chung & Joon Ho Hwang, 2010. "An Empirical Examination of the Parimutuel Sports Lottery Market versus the Bookmaker Market," Southern Economic Journal, John Wiley & Sons, vol. 76(4), pages 884-905, April.
    6. Montone, Maurizio, 2021. "Optimal pricing in the online betting market," Journal of Economic Behavior & Organization, Elsevier, vol. 186(C), pages 344-363.
    7. Benjamin Waggoner & Daniel Wines & Brian P. Soebbing & Chad S. Seifried & Jean Michael Martinez, 2014. "“Hot Hand” in the National Basketball Association Point Spread Betting Market: A 34-Year Analysis," IJFS, MDPI, vol. 2(4), pages 1-12, November.
    8. Miller, Thomas W. & Rapach, David E., 2013. "An intra-week efficiency analysis of bookie-quoted NFL betting lines in NYC," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 10-23.
    9. Ray C. Fair & John F. Oster, 2007. "College Football Rankings and Market Efficiency," Journal of Sports Economics, , vol. 8(1), pages 3-18, February.
    10. del Corral, Julio & Prieto-Rodríguez, Juan, 2010. "Are differences in ranks good predictors for Grand Slam tennis matches?," International Journal of Forecasting, Elsevier, vol. 26(3), pages 551-563, July.
    11. Nikolaos Vlastakis & George Dotsis & Raphael N. Markellos, 2009. "How efficient is the European football betting market? Evidence from arbitrage and trading strategies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 426-444.
    12. Michael Sinkey & Trevon Logan, 2014. "Does the Hot Hand Drive the Market? Evidence from College Football Betting Markets," Eastern Economic Journal, Palgrave Macmillan;Eastern Economic Association, vol. 40(4), pages 583-603, September.
    13. Arne Feddersen & Brad R. Humphreys & Brian P. Soebbing, 2018. "Sentiment Bias in National Basketball Association Betting," Journal of Sports Economics, , vol. 19(4), pages 455-472, May.
    14. Hvattum, Lars Magnus & Arntzen, Halvard, 2010. "Using ELO ratings for match result prediction in association football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 460-470, July.
    15. Bernardo, Giovanni & Ruberti, Massimo & Verona, Roberto, 2015. "Testing semi-strong efficiency in a fixed odds betting market: Evidence from principal European football leagues," MPRA Paper 66414, University Library of Munich, Germany.
    16. Bernardo, Giovanni & Ruberti, Massimo & Verona, Roberto, 2019. "Semi-strong inefficiency in the fixed odds betting market: Underestimating the positive impact of head coach replacement in the main European soccer leagues," The Quarterly Review of Economics and Finance, Elsevier, vol. 71(C), pages 239-246.
    17. Yoon Tae Sung & Scott Tainsky, 2014. "The National Football League Wagering Market," Journal of Sports Economics, , vol. 15(4), pages 365-384, August.
    18. Ray Fair & John Oster, 2002. "College Football Rankings and Market Efficiency," Yale School of Management Working Papers amz2377, Yale School of Management, revised 01 Aug 2007.
    19. 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.
    20. Ray Fair & John Oster, 2002. "College Football Rankings and Market Efficiency," Yale School of Management Working Papers amz2377, Yale School of Management, revised 01 Aug 2007.

    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:eee:intfor:v:26:y::i:3:p:606-621. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.