Evaluating strange forecasts: the curious case of football match scorelines

Downloads

• Akhtar, S., and P. Scarf. 2012. “Forecasting test cricket match outcomes in play.” International Journal of Forecasting, 28(3): 632–643. • Angelini, G., and L. De Angelis. 2019. “Efficiency of online football betting markets.” International Journal of Forecasting, 35(2): 712–721. • Asif, M., and I. McHale. 2019. “A generalized non-linear forecasting model for limited overs international cricket.” International Journal of Forecasting, 35(2): 634–640. • Asif, M., and I. McHale. 2016. “In-play forecasting of win probability in One-Day International cricket: A dynamic logistic regression model.” International Journal of Forecasting, 32(1): 34–43. • Ayton, P., D. O¨ nkal, and L. McReynolds. 2011. “Effects of ignorance and information on judgments and decisions.” Judgment and Decision Making, 6(5): 381–391. • Boshnakov, G., T. Kharrat, and I. McHale. 2017. “A bivariate Weibull count model for forecasting association football scores.” International Journal of Forecasting, 33(2): 458–466. • Brier, G. 1950. “Verification of forecasts expressed in terms of probability.” Monthly Weather Review, 78(1): 1–3. • Brown, A., and J. J. Reade. 2019. “The wisdom of amateur crowds: Evidence from an online community of sports tipsters.” European Journal of Operational Research, 272(3): 1073–1081. • Buraimo, B., D. Peel, and R. Simmons. 2013. “Systematic Positive Expected Returns in the UK Fixed Odds Betting Market: An Analysis of the Fink Tank Predictions.” International Journal of Financial Studies, 1(4): 1–15. • Butler, D., R. Butler, and J. Eakins. 2020. “Expert performance and crowd wisdom: Evidence from English Premier League predictions.” European Journal of Operational Research. • Cain, M., D. Law, and D. Peel. 2000. “The Favourite-Longshot Bias and Market Efficiency in UK Football Betting.” Scottish Journal of Political Economy, 47(1): 25–36. • Campos, J., D. Hendry, and H.-M. Krolzig. 2003. “Consistent Model Selection by an Automatic Gets Approach.” Oxford Bulletin of Economics and Statistics, 65(s1): 803–819. • Chong, Y., and D. Hendry. 1986. “Econometric evaluation of linear macro-economic models.” The Review of Economic Studies, 53(4): 671–690. • del Corral, J., and J. Prieto-Rodr´ıguez. 2010. “Are differences in ranks good predictors for grand slam tennis matches?” International Journal of Forecasting, 26(3): 551–563, Sports Forecasting. • Dixon, M., and S. Coles. 1997. “Modelling association football scores and inefficiencies in the football betting market.” Applied Statistics, 47(3): 265—280. • Dixon, M., and P. Pope. 2004. “The value of statistical forecasts in the UK association football betting market.” International Journal of Forecasting, 20(4): 697–711. • Elaad, G., J. J. Reade, and C. Singleton. 2020. “Information, prices and efficiency in an online betting market.” Finance Research Letters, 35, p. 101291. • Elo, A. E. 1978. The rating of chessplayers, past and present. London Batsford. • Fair, R., and R. Shiller. 1989. “The Informational Context of Ex Ante Forecasts.” The Review of Economics and Statistics, 71(2): 325–331. • Fawcett, N., L. K¨orber, R. Masolo, and M. Waldron. 2015. “Evaluating UK point and density forecasts from an estimated DSGE model: the role of off-model information over the financial crisis.” Staff Working Paper 538, Bank of England. • Forrest, D. 2011. “The Past and Future of the British Football Pools.” Journal of Gambling Studies, 15, p. 161–176. • Forrest, D., and L. P´erez. 2011. “Football pools and lotteries: substitute roads to riches?” Applied Economics Letters, 18(13): 1253–1257. • Forrest, D., J. Goddard, and R. Simmons. 2005. “Odds-Setters As Forecasters: The Case of English Football.” International Journal of Forecasting, 21(3): 551–564. • Forrest, D., and R. Simmons. 2000. “Forecasting Sport: The Behaviour and Performance of Football Tipsters.” International Journal of Forecasting, 16(3): 317–331. • Foulley, J.-L., and G. Celeux. 2018. “A penalty criterion for score forecasting in soccer.” arXiv preprint arXiv:1806.01595. • Genre, V., G. Kenny, A. Meyler, and A. Timmermann. 2013. “Combining expert forecasts: Can anything beat the simple average?” International Journal of Forecasting, 29(1): 108–121. • Goddard, J. 2005. “Regression Models for Forecasting Goals and Match Results in Association Football.” International Journal of Forecasting, 21(2): 331–340. • Granger, C. W. J., and M. H. Pesaran. 2000. “Economic and statistical measures of forecast accuracy.” Journal of Forecasting, 19(7): 537–560. • Heuer, A., and O. Rubner. 2012. “How Does the Past of a Soccer Match Influence Its Future? Concepts and Statistical Analysis.” PLOS One, 7(11): 1–7. • Hvattum, L. M., and H. Arntzen. 2010. “Using elo ratings for match result prediction in association football.” International Journal of Forecasting, 26(3): 460–470. • Karlis, D., and I. Ntzoufras. 2003. “Analysis of Sports Data Using Bivariate Poisson Models.” Journal of the Royal Statistical Society (Statistician), 52(3): 381–393. • Karlis, D., and I. Ntzoufras. 2005. “Bivariate Poisson and Diagonal Inflated Bivariate Poisson Regression Models in R.” Journal of Statistical Software, 14(10): . • Klaassen, F., and J. Magnus. 2003. “Forecasting the winner of a tennis match.” • European Journal of Operational Research, 148(2): 257–267, Sport and Computers. • Lawrence, M., P. Goodwin, M. O’Connor, and D. O¨ nkal. 2006. “Judgmental forecasting: A review of progress over the last 25 years.” International Journal of Forecasting, 22(3): 493–518. • Levitt, S. 2004. “Why are gambling markets organised so differently from financial markets?” The Economic Journal, 114(495): 223–246. • Maher, M. 1982. “Modelling association football scores.” Statistica Neerlandica, 36(3): 109–118. • McHale, I., and A. Morton. 2011. “A Bradley-Terry type model for forecasting tennis match results.” International Journal of Forecasting, 27(2): 619–630. • Mincer, J., and V. Zarnowitz. 1969. “The evaluation of economic forecasts.” In Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance. NBER, 1–46. • O’Leary, D. 2017. “Crowd performance in prediction of the World Cup 2014.” European Journal of Operational Research, 260(2): 715–724. • Ottaviani, M., and P. N. Sørensen. 2008. “The Favorite-Longshot Bias: An Overview of the Main Explanations.” In Handbook of Sports and Lottery Markets. Eds. by D. B. Hausch, and W. T. Ziemba, San Diego Elsevier, 83–101. • Peeters, T. 2018. “Testing the Wisdom of Crowds in the field: Transfermarkt valuations and international soccer results.” International Journal of Forecasting, 34(1): 17–29. • Pope, P., and D. Peel. 1989. “Information, Prices and Efficiency in a Fixed-Odds Betting Market.” Economica, 56(223): 323–341. • Reade, J. J. 2014. “Information and predictability: Bookmakers, prediction markets and tipsters as forecasters.” The Journal of Prediction Markets, 8(1): 43–76. • Shannon, C. E. 1948. “A mathematical theory of communication.” Bell System Technical Journal, 27(3): 379–423. • Simmons, J., L. Nelson, J. Galak, and S. Frederick. 2010. “Intuitive biases in choice versus estimation: Implications for the wisdom of crowds.” Journal of Consumer Research, 38(1): 1–15. • Singleton, C., J. J. Reade, and A. Brown. 2019. “Going with your gut: The (In)accuracy of forecast revisions in a football score prediction game.” Journal of Behavioral and Experimental Economics, p. 101502. • Snowberg, E., and J. Wolfers. 2010. “Explaining the Favorite-Longshot Bias: Is It Risk-Love or Misperceptions?” Journal of Political Economy, 118(4): 723–746. • Spann, M., and B. Skiera. 2009. “Sports forecasting: A comparison of the forecast accuracy of prediction markets, betting odds and tipsters.” Journal of Forecasting, 28(1): 55–72. • Sˇtrumbelj, E. 2014. “On determining probability forecasts from betting odds.” • International Journal of Forecasting, 30(4): 934–943. • Sˇtrumbelj, E., and M. Sˇikonja. 2010. “Online bookmakers’ odds as forecasts: The case of European soccer leagues.” International Journal of Forecasting, 26(3): 482–488. • Surowiecki, J. 2004. The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations. Brown Little. • Vaughan Williams, L., M.-C. Sung, P. Fraser-Mackenzie, J. Peirson, and J. Johnson. 2018. “Towards an Understanding of the Origins of the Favourite–Longshot Bias: Evidence from Online Poker Markets, a Real-money Natural Laboratory.” Economica, 85(338): 360–382.