Forecasting the outcomes of sports events: A review

References

• Andersson, P., Memmert, D., & Popowicz, E. (2009). Forecasting outcomes of the World Cup 2006 in football: Performance and confidence of bettors and laypeople. Psychology of Sport and Exercise, 10(1), 116–123. doi: 10.1016/j.psychsport.2008.07.008
   Web of Science ®Google Scholar
• Baker, R. D., & McHale, I. G. (2013). Forecasting exact scores in National Football League games. International Journal of Forecasting, 29(1), 122–130. doi: 10.1016/j.ijforecast.2012.07.002
   Web of Science ®Google Scholar
• Barrow, D., Drayer, I., Elliott, P., Gaut, G., & Osting, B. (2013). Ranking rankings: An empirical comparison of the predictive power of sports ranking methods. Journal of Quantitative Analysis in Sports, 9(2). doi: 10.1515/jqas-2013-0013
   Google Scholar
• Ben-Naim, E., Vazquez, F., & Redner, S. (2006). Parity and predictability of competitions. Journal of Quantitative Analysis in Sports, 2(4). doi: 10.2202/1559-0410.1034
   Google Scholar
• Boulier, B. L., & Stekler, H. O. (1999). Are sports seedings good predictors? An evaluation. International Journal of Forecasting, 15(1), 83–91. doi: 10.1016/S0169-2070(98)00067-3
   Web of Science ®Google Scholar
• Braun, S., & Kvasnicka, M. (2011). National sentiment and economic behavior. Journal of Sports Economics, 14(1), 45–64. doi: 10.1177/1527002511414718
   Web of Science ®Google Scholar
• Brown, A., Rambaccussing, D., Reade, J. J., & Rossi, G. (2017). Forecasting with social media: Evidence from tweets on soccer matches. Economic Inquiry, 20(3), 1363. doi: 10.1111/ecin.12506
   Web of Science ®Google Scholar
• Cattelan, M., Varin, C., & Firth, D. (2013). Dynamic Bradley-Terry modelling of sports tournaments. Journal of the Royal Statistical Society: Series C (Applied Statistics), 62(1), 135–150. doi: 10.1111/j.1467-9876.2012.01046.x
   Web of Science ®Google Scholar
• Clarke, S., & Dyte, D. (2000). Using official ratings to simulate major tennis tournaments. International Transactions in Operational Research, 7(6), 585–594. doi:10.1016/S0969-6016(00)00036-8 doi: 10.1111/j.1475-3995.2000.tb00218.x
   Google Scholar
• Dixon, M. J., & Coles, S. G. (1997). Modelling association football scores and inefficiencies in the football betting market. Journal of the Royal Statistical Society: Series C (Applied Statistics), 46(2), 265–280.
   Web of Science ®Google Scholar
• Dixon, M. J., & Robinson, M. E. (1998). A birth process model for association football matches. The Statistician, 47(3), 523–538.
   Google Scholar
• Dyte, D., & Clarke, S. R. (2000). A ratings based Poisson model for World Cup soccer simulation. Journal of the Operational Research Society, 51(8), 993–998. doi: 10.1057/palgrave.jors.2600997
   Web of Science ®Google Scholar
• European Gaming & Betting Association. (2014). Sports betting: Commercial and integrity issues. Retrieved from http://www.egba.eu/media/Sports-Betting-Report-FINAL.pdf
   Google Scholar
• Forrest, D., Goddard, J., & Simmons, R. (2005). Odds-setters as forecasters: The case of English football. International Journal of Forecasting, 21(3), 551–564. doi: 10.1016/j.ijforecast.2005.03.003
   Web of Science ®Google Scholar
• Forrest, D., Sanz, I., & Tena, J. D. (2010). Forecasting national team medal totals at the Summer Olympic Games. International Journal of Forecasting, 26(3), 576–588. doi: 10.1016/j.ijforecast.2009.12.007
   Web of Science ®Google Scholar
• Franck, E., Verbeek, E., & Nüesch, S. (2010). Prediction accuracy of different market structures — bookmakers versus a betting exchange. International Journal of Forecasting, 26(3), 448–459. doi: 10.1016/j.ijforecast.2010.01.004
   Web of Science ®Google Scholar
• Glickman, M. E., & Jones, A. C. (1999). Rating the chess rating system. Chance, (12), 21–28.
   Google Scholar
• Glickman, M. E., & Stern, H. S. (2017). Estimating team strength in the NFL. Handbook of Statistical Methods and Analyses in Sports, 113–136.
   Google Scholar
• Goddard, J. (2005). Regression models for forecasting goals and match results in association football. International Journal of Forecasting, 21(2), 331–340. doi: 10.1016/j.ijforecast.2004.08.002
   Web of Science ®Google Scholar
• Goddard, J., & Asimakopoulos, I. (2004). Forecasting football results and the efficiency of fixed-odds betting. Journal of Forecasting, 23(1), 51–66. doi: 10.1002/for.877
   Web of Science ®Google Scholar
• Gray, P. K., & Gray, S. F. (1997). Testing market efficiency: Evidence from The NFL sports betting market. The Journal of Finance, 52(4), 1725–1737. doi: 10.1111/j.1540-6261.1997.tb01129.x
   Web of Science ®Google Scholar
• Heuer, A., Müller, C., & Rubner, O. (2010). Soccer: Is scoring goals a predictable Poissonian process? EPL (Europhysics Letters), 89(3), 38007. doi: 10.1209/0295-5075/89/38007
   Google Scholar
• Heuer, A., & Rubner, O. (2009). Fitness, chance, and myths: An objective view on soccer results. The European Physical Journal B, 67(3), 445–458. doi: 10.1140/epjb/e2009-00024-8
   Web of Science ®Google Scholar
• Hill, I. D. (1974). Association football and statistical inference. Applied Statistics, 23(2), 203. doi: 10.2307/2347001
   Web of Science ®Google Scholar
• Hvattum, L. M., & Arntzen, H. (2010). Using ELO ratings for match result prediction in association football. International Journal of Forecasting, 26(3), 460–470. doi: 10.1016/j.ijforecast.2009.10.002
   Web of Science ®Google Scholar
• Karlis, D., & Ntzoufras, I. (2003). Analysis of sports data by using bivariate Poisson models. Journal of the Royal Statistical Society: Series D (the Statistician), 52(3), 381–393. doi: 10.1111/1467-9884.00366
   Google Scholar
• Kelly, J. (1956). A new interpretation of information rate. IEEE Transactions on Information Theory, 2(3), 185–189. doi: 10.1109/TIT.1956.1056803
   Web of Science ®Google Scholar
• Koopman, S. J., & Lit, R. (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 (Statistics in Society), 178(1), 167–186. doi: 10.1111/rssa.12042
   Web of Science ®Google Scholar
• Kovalchik, S. A. (2016). Searching for the GOAT of tennis win prediction. Journal of Quantitative Analysis in Sports, 12(3), 311. doi: 10.1515/jqas-2015-0059
   Web of Science ®Google Scholar
• Lasek, J., Szlávik, Z., & Bhulai, S. (2013). The predictive power of ranking systems in association football. International Journal of Applied Pattern Recognition, 1(1), 27. doi: 10.1504/IJAPR.2013.052339
   Google Scholar
• Leitner, C., Zeileis, A., & Hornik, K. (2010). Forecasting sports tournaments by ratings of (prob)abilities: A comparison for the EURO 2008. International Journal of Forecasting, 26(3), 471–481. doi: 10.1016/j.ijforecast.2009.10.001
   Web of Science ®Google Scholar
• Leitner, C., Zeileis, A., & Hornik, K. (2011). Bookmaker consensus and agreement for the UEFA Champions League 2008/2009. IMA Journal of Management Mathematics, 22(2), 183–194. doi: 10.1093/imaman/dpq016
   Web of Science ®Google Scholar
• Lessmann, S., Sung, M.-C., & Johnson, J. E. V. (2010). Alternative methods of predicting competitive events: An application in horserace betting markets. International Journal of Forecasting, 26(3), 518–536. doi: 10.1016/j.ijforecast.2009.12.013
   Web of Science ®Google Scholar
• Low, B., Coutinho, D., Gonçalves, B., Rein, R., & Memmert, D. (2019). A systematic review of collective tactical behaviours in football using positional data. Sports Medicine, 1–43.
   Web of Science ®Google Scholar
• Lucey, P., Bialkowski, A., Carr, P., Yue, Y., & Matthews, I. (2014). How to get an open shot: Analyzing team movement in basketball using tracking data. Proceedings of the 8th annual MIT SLOAN sports analytics conference.
   Google Scholar
• Luckner, S., Schröder, J., & Slamka, C. (2008). On the forecast accuracy of sports prediction markets. In Lecture notes in business information processing: Vol. 2. Negotiation, auctions, and market engineering (Vol. 2, pp. 227–234). Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg. doi: 10.1007/978-3-540-77554-6_17
   Google Scholar
• Maher, M. J. (1982). Modelling association football scores. Statistica Neerlandica, 36(3), 109–118. doi: 10.1111/j.1467-9574.1982.tb00782.x
   Google Scholar
• Manner, H. (2016). Modeling and forecasting the outcomes of NBA basketball games. Journal of Quantitative Analysis in Sports, 12(1), 1128. doi: 10.1515/jqas-2015-0088
   Web of Science ®Google Scholar
• Mauro, A. d., Greco, M., & Grimaldi, M. (2015). What is big data? A consensual definition and a review of key research topics. In AIP conference proceedings (pp. 97–104). AIP Publishing LLC. doi: 10.1063/1.4907823
   Google Scholar
• McHale, I., & Davies, S. (2007). Statistical analysis of the effectiveness of the FIFA world rankings. Statistical Thinking in Sports, 77–90. doi: 10.1201/9781584888697.ch5
   Google Scholar
• McHale, I., & Morton, A. (2011). A Bradley-Terry type model for forecasting tennis match results. International Journal of Forecasting, 27(2), 619–630. doi: 10.1016/j.ijforecast.2010.04.004
   Web of Science ®Google Scholar
• Memmert, D., Lemmink, K. A. P. M., & Sampaio, J. (2017). Current approaches to tactical performance analyses in soccer using position data. Sports Medicine, 47(1), 1–10. doi: 10.1007/s40279-016-0562-5
   PubMed Web of Science ®Google Scholar
• Memmert, D., & Raabe, D. (2018). Data Analytics in Football. Positional Data Collection, Modelling and Analysis. Abingdon: Routledge.
   Google Scholar
• Nevill, A. M., & Holder, R. L. (1999). Home advantage in sport. Sports Medicine, 28(4), 221–236. doi: 10.2165/00007256-199928040-00001
   PubMed Web of Science ®Google Scholar
• Newton, P. K., & Aslam, K. (2009). Monte Carlo tennis: A stochastic Markov chain model. Journal of Quantitative Analysis in Sports, 5(3). doi: 10.2202/1559-0410.1169
   Google Scholar
• Ottaviani, M., & Sørensen, P. N. (2008). The favorite-longshot bias: An overview of the main explanations. Handbook of Sports and Lottery Markets, 83–101. doi: 10.1016/B978-044450744-0.50009-3
   Google Scholar
• Pachur, T., & Biele, G. (2007). Forecasting from ignorance: The use and usefulness of recognition in lay predictions of sports events. Acta Psychologica, 125(1), 99–116. doi: 10.1016/j.actpsy.2006.07.002
   PubMed Web of Science ®Google Scholar
• 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. doi: 10.1016/j.ijforecast.2017.08.002
   Web of Science ®Google Scholar
• Rue, H., & Salvesen, O. (2000). Prediction and retrospective analysis of soccer matches in a league. Journal of the Royal Statistical Society: Series D (the Statistician), 49(3), 399–418. doi: 10.1111/1467-9884.00243
   Google Scholar
• Scheibehenne, B., & Bröder, A. (2007). Predicting Wimbledon 2005 tennis results by mere player name recognition. International Journal of Forecasting, 23(3), 415–426. doi: 10.1016/j.ijforecast.2007.05.006
   Web of Science ®Google Scholar
• Smith, M. A., Paton, D., & Williams, L. V. (2006). Market efficiency in person-to-person betting. Economica, 73(292), 673–689. doi: 10.1111/j.1468-0335.2006.00518.x
   Web of Science ®Google Scholar
• Snowberg, E., & Wolfers, J. (2010). Explaining the favorite–long shot bias: Is it risk-love or misperceptions? Journal of Political Economy, 118(4), 723–746. doi: 10.1086/655844
   Web of Science ®Google Scholar
• Song, C., Boulier, B. L., & Stekler, H. O. (2007). The comparative accuracy of judgmental and model forecasts of American football games. International Journal of Forecasting, 23(3), 405–413. doi: 10.1016/j.ijforecast.2007.05.003
   Web of Science ®Google Scholar
• Spann, M., & Skiera, B. (2009). Sports forecasting: A comparison of the forecast accuracy of prediction markets, betting odds and tipsters. Journal of Forecasting, 28(1), 55–72. doi: 10.1002/for.1091
   Web of Science ®Google Scholar
• Stekler, H. O., Sendor, D., & Verlander, R. (2010). Issues in sports forecasting. International Journal of Forecasting, 26(3), 606–621. doi: 10.1016/j.ijforecast.2010.01.003
   Web of Science ®Google Scholar
• Štrumbelj, E., & Šikonja, M. R. (2010). Online bookmakers’ odds as forecasts: The case of European soccer leagues. International Journal of Forecasting, 26(3), 482–488. doi: 10.1016/j.ijforecast.2009.10.005
   Web of Science ®Google Scholar
• Štrumbelj, E., & Vračar, P. (2012). Simulating a basketball match with a homogeneous Markov model and forecasting the outcome. International Journal of Forecasting, 28(2), 532–542. doi: 10.1016/j.ijforecast.2011.01.004
   Web of Science ®Google Scholar
• Wei, X., Lucey, P., Morgan, S., & Sridharan, S. (2013). Sweet-spot: Using spatiotemporal data to discover and predict shots in tennis. 7th Annual MIT Sloan sports Analytics Conference, Boston, MA.
   Google Scholar
• Wigness, M. B., Williams, C. C., & Rowell, M. J. (2010). A new iterative method for ranking college football teams. Journal of Quantitative Analysis in Sports, 6(2). doi: 10.2202/1559-0410.1242
   Google Scholar
• Woodland, L. M., & Woodland, B. M. (1994). Market efficiency and the favorite-longshot bias: The baseball betting market. The Journal of Finance, 49(1), 269–279. doi: 10.1111/j.1540-6261.1994.tb04429.x
   Web of Science ®Google Scholar
• Wunderlich, F., & Memmert, D. (2016). Analysis of the predictive qualities of betting odds and FIFA World Ranking: Evidence from the 2006, 2010 and 2014 Football World Cups. Journal of Sports Sciences, 34(24), 2176–2184. doi: 10.1080/02640414.2016.1218040
   PubMed Web of Science ®Google Scholar
• Wunderlich, F., & Memmert, D. (2018). The betting odds rating system: Using soccer forecasts to forecast soccer. PloS One, 13(6), e0198668. doi: 10.1371/journal.pone.0198668
   PubMed Web of Science ®Google Scholar