We present a complete betting strategy for football (soccer), and develop a free graphical user interface to apply it. We use betting odds to elicit the hyperparameters of our prior distributions that are mixed with historical records to estimate the probability of each outcome. Forecast probabilities are used to define the optimal bet size based on the Kelly Criterion and we establish a stopping rule for betting, based on atypical positive returns (referred to as "black swans"). We apply our method to the English Premier League, obtaining profits equal to 33.54%, 22.12%, and 49.01% for seasons 2013-14, 2014-15, and 2015-16, respectively.
CainM., LawD., & PeelD. (2000). The favourite-longshot bias and market efficiency in UK football betting. Scottish Journal of Political Economy, 47, 25–36.
2.
ConstantinouA. C, & FentonN. E. (2012). Solving the problem of inadequate scoring rules for assessing probabilistic football forecast models. Journal of Quantitative Analysis in Sports. doi: https://doi.org/10.1515/1559-0410.1418
3.
ConstantinouA. C, & FentonN. E. (2013a). Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries. Journal of Quantitative Analysis in Sports, 9, 37–50.
4.
ConstantinouA. C, & FentonN. E. (2013b). Profiting from arbitrage and odds biases of the European football gambling market. The Journal of Gambling Business and Economics, 7, 41–70.
5.
ConstantinouA. C, FentonN. E., & MartinN. (2013). Profiting from an inefficient association football gambling market: Prediction, risk and uncertainty using Bayesian networks. Knowledge-Based Systems, 50, 60–86.
6.
ConstantinouA. C, FentonN. E., & NeilM. (2012). Pi-football: Bayesian A. network model for forecasting association football match outcomes. Knowledge-Based Systems, 36, 322–339.
7.
CrowderM., DixonM., LedfordA., & RobinsonM. (2002). Dynamic modelling and prediction of English Football League matches for betting. Journal of the Royal Statistical Society: Series D (the Statistician), 51, 157–168.
8.
DixonM., & PopeP. (2004). The value of statistical forecasts in the UK association football betting market. International Journal of Forecasting, 20, 697–711.
9.
DunningE. G., JosephR. E., & MaguireA. (1993). The sports process: A comparative and developmental approach.Champaign, IL: Human Kinetics Publishers.
10.
FinniganM., & NordstedP. (2010). The premier football betting handbook 2010/11.Hampshire, Great Britain: Harriman House.
11.
ForrestD., GoddardJ., & SimmonsR. (2005). Odds-setters as forecasters: The case of English football. International Journal of Forecasting, 21, 551–564.
12.
GarthwaiteP., KadaneJ., & O'HaganA. (2005). Statistical methods for eliciting probability distributions. Journal of the American Statistical Association, 100, 680–701.
13.
GoddardJ., & AsimakopoulosI. (2004). Forecasting football results and the efficiency of fixed-odds betting. Journal of Forecasting, 23, 51–66.
14.
GrahamI., & StottH. (2008). Predicting bookmaker odds and efficiency for UK football. Applied Economics, 40, 99–109.
15.
HizajiR. H., & JerniganR. W. (2009). Modelling compositional data using Dirichlet regression models. Journal of Applied Statistics & Probability, 4, 77–91.
16.
HvattumL. M., & ArntzenH. (2010). Using ELO ratings for match result prediction in association football. International Journal of Forecasting, 26, 460–470.
17.
JullienB., & SalaniéB. (1994). Measuring the incidence of insider trading: A comment on Shin. The Economic Journal, 104, 1418–1419.
18.
KadaneJ. B. (1980). Predictive and structural methods for eliciting prior distributions. In ZellnerA. (Ed.), Bayesian analysis in econometrics and statistics: Essays in honor of Harold Jeffreys (pp. 89–93). Amsterdam: North-Holland Publishing Company.
19.
KadaneJ., & WolfsonL. (1998). Experiences in elitation. The Statiscian, 47, 3–19.
20.
KuypersT. (2000a). Balance in competition in Dutch soccer. Journal of the Royal Statistical Society: Series D (the Statistician), 49, 419–431.
21.
KuypersT. (2000b). Information and efficiency: An empirical study of a fixed odds betting market. Applied Economics, 32, 1353–1363.
22.
PopeP. E, & PeelD. A. (1989). Information, prices and efficiency in a fixed-odds betting market. Economica, 56, 323–341.
23.
RueH., & SalvesenO. (2000). Prediction and retrospective analysis of soccer matches in a league. Journal of the Royal Statistical Society: Series D (the Statistician), 49, 399–418.
24.
ShinH. (1991). Optimal betting odds against insider trading. The Economic Journal, 101, 1179–1185.
25.
ShinH. (1993). Measuring the incidence of insider trading in a market for state-contingent claims. The Economic Journal, 103, 1141–1153.
26.
SpannM., & SkieraB. (2009). Sports forecasting: A comparison of the forecast accuracy of prediction markets, betting odds and tipsters. Journal of Forecasting, 28, 55–72.
27.
ŠtrumbeljE. (2014). On determining probability forecasts from betting odds. International Journal of Forecasting, 30, 934–943.
28.
ŠtrumbeljE. (2016). A comment on the bias of probabilities derived from bookmaker odds and their use in measuring outcome uncertainty. Journal of Sport Economics, 17, 12–26.
29.
ŠtrumbeljE., & ŠikonjaM. R. (2010). Online bookmakers’ odds as forecasts: The case of European soccer leagues. International Journal of Forecasting, 26, 482–488.
30.
ThomasS., & JacobJ. (2006). A generalized Dirichlet model. Statistics and Probability Letters, 76, 1761–1767.
