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A bivariate Weibull count model for forecasting association football scores

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  1. 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.
  2. Rose Baker, 2026. "A model for underdispersed count data," Statistical Papers, Springer, vol. 67(2), pages 1-14, April.
  3. 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.
  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. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Maia, Luiz Fernando G.N. & Pennanen, Teemu & da Silva, Moacyr A.H.B. & Targino, Rodrigo S., 2026. "Stochastic modelling of football matches using dynamic regressors," International Journal of Forecasting, Elsevier, vol. 42(2), pages 691-707.
  11. Lawrence Clegg & John Cartlidge, 2023. "Not feeling the buzz: Correction study of mispricing and inefficiency in online sportsbooks," Papers 2306.01740, arXiv.org, revised Jul 2024.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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).
  17. 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.
  18. Evgeny A. Antipov & Anastasiia A. Gagarskaia & Yulia S. Trofimova & Elena B. Pokryshevskaya, 2025. "Applying Shifted-Beta-Geometric and Beta-Discrete-Weibull Models for Employee Retention Curve Projection," SN Operations Research Forum, Springer, vol. 6(1), pages 1-16, March.
  19. Andrei Shynkevich, 2022. "Informational efficiency of football transfer market," Economics Bulletin, AccessEcon, vol. 42(2), pages 1032-1039.
  20. Marius Ötting & Christian Deutscher & Carl Singleton & Luca De Angelis, 2025. "Betting on momentum in contests," Economic Inquiry, Western Economic Association International, vol. 63(4), pages 1066-1089, October.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. Matthew J Penn & Christl A Donnelly, 2022. "Analysis of a double Poisson model for predicting football results in Euro 2020," PLOS ONE, Public Library of Science, vol. 17(5), pages 1-23, May.
  27. Alejandro Álvarez & Alejandro Cataldo & Guillermo Durán & Manuel Durán & Pablo Galaz & Iván Monardo & Denis Sauré, 2025. "Data science approach to simulating the FIFA World Cup Qatar 2022 at a website in tribute to Maradona," Computational Statistics, Springer, vol. 40(4), pages 2223-2247, April.
  28. 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.
  29. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
  30. 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.
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. Hubáček, Ondřej & Šír, Gustav, 2023. "Beating the market with a bad predictive model," International Journal of Forecasting, Elsevier, vol. 39(2), pages 691-719.
  36. 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.
  37. 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.
  38. Florez Mauro & Guindani Michele & Vannucci Marina, 2025. "Bayesian bivariate Conway–Maxwell–Poisson regression model for correlated count data in sports," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 21(1), pages 51-71.
  39. Bruce Lezana, 2026. "Fear of the Draw, Consumption and Mistaken Heuristics: Profit Opportunities in the Football Betting Market," Journal of Sports Economics, , vol. 27(5), pages 548-596, June.
  40. 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.
  41. 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.
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