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A bivariate Weibull count model for forecasting association football scores

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Cited by:

  1. J Reade & C Singleton & L Vaughan Williams, 2020. "Betting Markets for English Premier League Results and Scorelines: Evaluating a Simple Forecasting Model," Economic Issues Journal Articles, Economic Issues, vol. 25(1), pages 87-106, March.
  2. Corona, Francisco & Forrest, David & Tena, J.D. & Wiper, Michael, 2019. "Bayesian forecasting of UEFA Champions League under alternative seeding regimes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 722-732.
  3. Holmes, Benjamin & McHale, Ian G. & Żychaluk, Kamila, 2023. "A Markov chain model for forecasting results of mixed martial arts contests," International Journal of Forecasting, Elsevier, vol. 39(2), pages 623-640.
  4. Kharrat, Tarak & McHale, Ian G. & Peña, Javier López, 2020. "Plus–minus player ratings for soccer," European Journal of Operational Research, Elsevier, vol. 283(2), pages 726-736.
  5. J. James Reade & Carl Singleton & Alasdair Brown, 2021. "Evaluating strange forecasts: The curious case of football match scorelines," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(2), pages 261-285, May.
  6. Gavin A. Whitaker & Ricardo Silva & Daniel Edwards & Ioannis Kosmidis, 2021. "A Bayesian approach for determining player abilities in football," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 174-201, January.
  7. McHale, Ian G. & Relton, Samuel D., 2018. "Identifying key players in soccer teams using network analysis and pass difficulty," European Journal of Operational Research, Elsevier, vol. 268(1), pages 339-347.
  8. Song, Kai & Gao, Yiran & Shi, Jian, 2020. "Making real-time predictions for NBA basketball games by combining the historical data and bookmaker’s betting line," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
  9. Wheatcroft, Edward, 2020. "A profitable model for predicting the over/under market in football," LSE Research Online Documents on Economics 103712, London School of Economics and Political Science, LSE Library.
  10. Andrei Shynkevich, 2022. "Informational efficiency of football transfer market," Economics Bulletin, AccessEcon, vol. 42(2), pages 1032-1039.
  11. 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).
  12. Szczecinski Leszek, 2022. "G-Elo: generalization of the Elo algorithm by modeling the discretized margin of victory," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 18(1), pages 1-14, March.
  13. Sharifah Farah Syed Yusoff Alhabshi & Zamira Hasanah Zamzuri & Siti Norafidah Mohd Ramli, 2021. "Monte Carlo Simulation of the Moments of a Copula-Dependent Risk Process with Weibull Interwaiting Time," Risks, MDPI, vol. 9(6), pages 1-21, June.
  14. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
  15. Groll Andreas & Kneib Thomas & Mayr Andreas & Schauberger Gunther, 2018. "On the dependency of soccer scores – a sparse bivariate Poisson model for the UEFA European football championship 2016," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 65-79, June.
  16. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.
  17. Federico Fioravanti & Fernando Delbianco & Fernando Tohmé, 2023. "The relative importance of ability, luck and motivation in team sports: a Bayesian model of performance in the English Rugby Premiership," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 715-731, September.
  18. Wheatcroft, Edward, 2020. "A profitable model for predicting the over/under market in football," International Journal of Forecasting, Elsevier, vol. 36(3), pages 916-932.
  19. Marius Ötting & Christian Deutscher & Carl Singleton & Luca De Angelis, 2023. "Gambling on Momentum in Contests," Economics Discussion Papers em-dp2023-08, Department of Economics, University of Reading.
  20. Scarf, Phil & Parma, Rishikesh & McHale, Ian, 2019. "On outcome uncertainty and scoring rates in sport: The case of international rugby union," European Journal of Operational Research, Elsevier, vol. 273(2), pages 721-730.
  21. da Costa, Igor Barbosa & Marinho, Leandro Balby & Pires, Carlos Eduardo Santos, 2022. "Forecasting football results and exploiting betting markets: The case of “both teams to score”," International Journal of Forecasting, Elsevier, vol. 38(3), pages 895-909.
  22. Butler, David & Butler, Robert & Eakins, John, 2021. "Expert performance and crowd wisdom: Evidence from English Premier League predictions," European Journal of Operational Research, Elsevier, vol. 288(1), pages 170-182.
  23. 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.
  24. Lasek, Jan & Gagolewski, Marek, 2021. "Interpretable sports team rating models based on the gradient descent algorithm," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1061-1071.
  25. Lawrence Clegg & John Cartlidge, 2023. "Not feeling the buzz: Correction study of mispricing and inefficiency in online sportsbooks," Papers 2306.01740, arXiv.org, revised Jan 2024.
  26. Kaori Narita & Benjamin Holmes & Ian McHale, 2022. "Managerial Contribution to Firm Success: Evidence from Professional Football Leagues," Working Papers 202224, University of Liverpool, Department of Economics.
  27. 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.
  28. Pearson Mitchell & Jr Glen Livingston & King Robert, 2020. "An exploration of predictive football modelling," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 27-39, March.
  29. Koopman, Siem Jan & Lit, Rutger, 2019. "Forecasting football match results in national league competitions using score-driven time series models," International Journal of Forecasting, Elsevier, vol. 35(2), pages 797-809.
  30. Fry, John & Serbera, Jean-Philippe & Wilson, Rob, 2021. "Managing performance expectations in association football," Journal of Business Research, Elsevier, vol. 135(C), pages 445-453.
  31. Asif, M. & McHale, I.G., 2019. "A generalized non-linear forecasting model for limited overs international cricket," International Journal of Forecasting, Elsevier, vol. 35(2), pages 634-640.
  32. Hubáček, Ondřej & Šír, Gustav, 2023. "Beating the market with a bad predictive model," International Journal of Forecasting, Elsevier, vol. 39(2), pages 691-719.
  33. 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.
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