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Cited by 77

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Efficiency of online football betting markets

Angelini, Giovanni, De Angelis, Luca·2019·International Journal of Forecasting
DOI 10.1016/j.ijforecast.2018.07.008
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Citations

Cites

Research works printed but not linked: 32

  1. Angelini, 2017, PARX model for football matches predictions, Journal of Forecasting, 36, 795, 10.1002/for.2471 · DOI
  2. Boshnakov, 2017, A bivariate Weibull count model for forecasting association football scores, International Journal of Forecasting, 33, 458, 10.1016/j.ijforecast.2016.11.006 · DOI
  3. Brown, 2017, Using social media to identify market inefficiencies: evidence from twitter and betfair, Economic Inquiry, 56, 1748, 10.1111/ecin.12506 · DOI
  4. Brown, A., & Reade, J. J. (2017). Combining prediction markets and forecasting contests. Available at SSRN: https://ssrn.com/abstract=2944675.
  5. Croxson, 2014, Information and efficiency: goal arrival in soccer betting, Economic Journal, 124, 62, 10.1111/ecoj.12033 · DOI
  6. Dixon, 2004, The value of statistical forecasts in the UK association football betting market, International Journal of Forecasting, 20, 686, 10.1016/j.ijforecast.2003.12.007 · DOI
  7. Fama, 1970, Efficient capital markets: a review of theory and empirical work, Journal of Finance, 25, 383, 10.2307/2325486 · DOI · Paper page
  8. Forrest, 2005, Odds-setters as forecasters: the case of English football, International Journal of Forecasting, 21, 551, 10.1016/j.ijforecast.2005.03.003 · DOI
  9. Forrest, 2000, Forecasting sport: the behaviour and performance of football tipsters, International Journal of Forecasting, 16, 317, 10.1016/S0169-2070(00)00050-9 · DOI
  10. Franck, 2010, Prediction accuracy of different market structures — bookmakers versus a betting exchange, International Journal of Forecasting, 26, 448, 10.1016/j.ijforecast.2010.01.004 · DOI
  11. Franck, 2013, Inter-market arbitrage in betting, Economica, 80, 300, 10.1111/ecca.12009 · DOI
  12. Glickman, 1998, A state-space model for National Football League scores, Journal of the American Statistical Association, 93, 25, 10.1080/01621459.1998.10474084 · DOI · Paper page
  13. Goddard, 2004, Forecasting football results and the efficiency of fixed-odds betting, Journal of Forecasting, 23, 51, 10.1002/for.877 · DOI
  14. Grant, 2010, Finding profitable forecast combinations using probability scoring rules, International Journal of Forecasting, 26, 498, 10.1016/j.ijforecast.2010.01.002 · DOI
  15. Griffith, 1949, Odds adjustment by American horse-race bettors, American Journal of Psychology, 62, 290, 10.2307/1418469 · DOI · Paper page
  16. Hurley, 1995, Note on the Hayek hypothesis and the favorite–longshot bias in parimutuel betting, American Economic Review, 85, 949
  17. Ioannidis, 2005, Testing for market efficiency in gambling markets when the errors are non-normal and heteroskedastic — an application of the wild bootstrap, Economics Letters, 87, 221, 10.1016/j.econlet.2004.06.025 · DOI · Paper page
  18. Koopman, 2015, A dynamic bivariate Poisson model for analysing and forecasting match results in the English Premier League, Journal of the Royal Statistical Society, Series A, 178, 167, 10.1111/rssa.12042 · DOI
  19. Lessmann, 2010, Alternative methods of predicting competitive events: an application in horserace betting markets, International Journal of Forecasting, 26, 518, 10.1016/j.ijforecast.2009.12.013 · DOI
  20. Liang, 1986, Longitudinal data analysis using generalized linear models, Biometrika, 73, 13, 10.1093/biomet/73.1.13 · DOI · Paper page
  21. McHale, 2011, A Bradley–Terry type model for forecasting tennis match results, International Journal of Forecasting, 27, 619, 10.1016/j.ijforecast.2010.04.004 · DOI
  22. Pope, 1989, Information, prices and efficiency in a fixed-odds betting market, Economica, 56, 323, 10.2307/2554281 · DOI
  23. Reade, 2014, Information and predictability: bookmakers, prediction markets and tipsters as forecasters, Journal of Prediction Markets, 8, 43, 10.5750/jpm.v8i1.865 · DOI
  24. Rydall, 2010, An optimized ratings-based model for forecasting Australian Rules football, International Journal of Forecasting, 26, 511, 10.1016/j.ijforecast.2010.01.001 · DOI
  25. Spann, 2009, Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters, Journal of Forecasting, 28, 55, 10.1002/for.1091 · DOI
  26. Thaler, 1988, Parimutual betting markets: racetracks and lotteries, Journal of Economic Perspectives, 2, 161, 10.1257/jep.2.2.161 · DOI · Paper page
  27. S̆trumbelj, 2010, Online bookmakers’ odds as forecasts: the case of European soccer leagues, International Journal of Forecasting, 26, 482, 10.1016/j.ijforecast.2009.10.005 · DOI
  28. S̆trumbelj, 2012, Simulating a basketball match with a homogeneous Markov model and forecasting the outcome, International Journal of Forecasting, 28, 532, 10.1016/j.ijforecast.2011.01.004 · DOI
  29. Vlastakis, 2009, How efficient is the European football betting market? Evidence from arbitrage and trading strategies, Journal of Forecasting, 28, 426, 10.1002/for.1085 · DOI
  30. Štrumbelj, 2014, On determining probability forecasts from betting odds, International Journal of Forecasting, 30, 934, 10.1016/j.ijforecast.2014.02.008 · DOI
  31. Woodland, 1994, Market efficiency and the favorite–longshot bias: the baseball betting market, Journal of Finance, 49, 269, 10.1111/j.1540-6261.1994.tb04429.x · DOI
  32. Woodland, 2001, Market efficiency and profitable wagering in the National Hockey League: can bettors score on longshots?, Southern Economic Journal, 67, 983, 10.2307/1061582 · DOI

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Metadata

Authors
Angelini, G., & De Angelis, L.
Venue
International Journal of Forecasting
Publisher
Elsevier BV
Published
2019-04
Volume
35
Issue
2
Pages
712-721
ISSN
0169-2070
DOI
10.1016/j.ijforecast.2018.07.008
Cited by
77
References
4
Language
en
Update notices
erratum (2021-07-01)
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.